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	<title>bioRxiv Channel: Ernst Strüngmann Institute (ESI) for Neuroscience</title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "Ernst Strüngmann Institute (ESI) for Neuroscience"
	</description>

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	<title>bioRxiv</title>
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	<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.28.469673v1?rss=1">
<title>
<![CDATA[
Does the brain care about averages? A simple test. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.28.469673v1?rss=1"
</link>
<description><![CDATA[
Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: O_LIReliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.
C_LIO_LIBehavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses.
C_LI

We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of data set 1, both assumptions were largely fulfilled. Moreover, better-matched single-trial responses predicted correct behaviour. In contrast, the less restrictive paradigm of data set 2 met neither assumption, with the match between single-trial and average responses being neither reliable nor predictive of behaviour. Simulations confirmed these results. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.
]]></description>
<dc:creator>Tlaie, A.</dc:creator>
<dc:creator>Shapcott, K. A.</dc:creator>
<dc:creator>Tiesinga, P.</dc:creator>
<dc:creator>Schölvinck, M.</dc:creator>
<dc:creator>Havenith, M. N.</dc:creator>
<dc:date>2021-11-28</dc:date>
<dc:identifier>doi:10.1101/2021.11.28.469673</dc:identifier>
<dc:title><![CDATA[Does the brain care about averages? A simple test.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.09.21.307256v1?rss=1">
<title>
<![CDATA[
Influence of sensory modality and control dynamics on human path integration 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.09.21.307256v1?rss=1"
</link>
<description><![CDATA[
Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.
]]></description>
<dc:creator>Stavropoulos, A.</dc:creator>
<dc:creator>Lakshminarasimhan, K. J.</dc:creator>
<dc:creator>Laurens, J.</dc:creator>
<dc:creator>Pitkow, X.</dc:creator>
<dc:creator>Angelaki, D.</dc:creator>
<dc:date>2020-09-23</dc:date>
<dc:identifier>doi:10.1101/2020.09.21.307256</dc:identifier>
<dc:title><![CDATA[Influence of sensory modality and control dynamics on human path integration]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-09-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.30.470367v1?rss=1">
<title>
<![CDATA[
Stimulus dependence of theta rhythmic activity in primate V1 and its potential relevance for visual perception 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.30.470367v1?rss=1"
</link>
<description><![CDATA[
Theta-band (3-8 Hz) neural oscillations are integral to sensory processing and active exploration. Traditionally associated with higher-order areas such as hippocampus and prefrontal cortex, recent studies identified theta rhythmic modulations in the primary visual cortex (V1) of mice during locomotion, suggesting sensory processing functions. Here, we demonstrate that careful optimization of visual stimulus size and contrast can induce robust theta oscillations in macaque V1. During visual detection, monkeys reaction times fluctuated rhythmically at the theta frequency of V1 neural activity, with detection performance correlated to the theta phase. These findings suggest that induced theta oscillations may reflect an intrinsic temporal filtering mechanism of V1 neurons, highlighting the importance of early sensory cortical dynamics in shaping perceptual timing.
]]></description>
<dc:creator>Fanyiwi, P. T.</dc:creator>
<dc:creator>Agayby, B.</dc:creator>
<dc:creator>Kienitz, R.</dc:creator>
<dc:creator>Haag, M.</dc:creator>
<dc:creator>Schmid, M. C.</dc:creator>
<dc:date>2021-12-01</dc:date>
<dc:identifier>doi:10.1101/2021.11.30.470367</dc:identifier>
<dc:title><![CDATA[Stimulus dependence of theta rhythmic activity in primate V1 and its potential relevance for visual perception]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-12-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.11.22.469550v1?rss=1">
<title>
<![CDATA[
Human visual gamma for color stimuli: When LGN drive is equalized, red is not special 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.11.22.469550v1?rss=1"
</link>
<description><![CDATA[
Strong gamma-band oscillations in primate early visual cortex can be induced by spatially homogeneous, high-contrast stimuli, such as color surfaces. Compared to other hues, particularly strong gamma oscillations have been reported for red stimuli. However, precortical color processing and the resultant strength of input to V1 has often not been fully controlled for. This leaves the possibility that stronger responses to some hues were due to differences in V1 input strength. We presented stimuli that had equal luminance and color contrast levels in a color coordinate system based on color responses of the lateral geniculate nucleus, the main input source for area V1. With these stimuli, we recorded magnetoencephalography in 30 human subjects. We found narrowband color-induced gamma oscillations in early visual cortex, which, contrary to previous reports, did not differ between red and green stimuli of equal L-M cone contrast. Notably, blue stimuli with contrast exclusively on the S-cone axis induced very weak gamma responses, as well as smaller event-related fields and poorer change detection performance. The strength of human color gamma responses could be well explained by the strength of thalamic input induced by each hue and does not show a clear red bias when this input strength is properly equalized.
]]></description>
<dc:creator>Stauch, B. J.</dc:creator>
<dc:creator>Peter, A.</dc:creator>
<dc:creator>Ehrlich, I.</dc:creator>
<dc:creator>Nolte, Z.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2021-11-22</dc:date>
<dc:identifier>doi:10.1101/2021.11.22.469550</dc:identifier>
<dc:title><![CDATA[Human visual gamma for color stimuli: When LGN drive is equalized, red is not special]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.10.30.466578v1?rss=1">
<title>
<![CDATA[
Multi-area recordings and optogenetics in the awake, behaving marmoset 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.10.30.466578v1?rss=1"
</link>
<description><![CDATA[
The common marmoset has emerged as a key primate model in neuroscience. Marmosets are small in size, show great potential as transgenic models and exhibit complex behaviors. These advantages place the marmoset model in the critical gap between rodents and larger primates. Thus, it is necessary to develop technology that enables monitoring and manipulation of the neural circuits underlying the behavior of the marmoset. Here, we present a novel approach to record and optogenetically manipulate neural activity in the awake, behaving marmoset. Our design utilizes a light-weight, 3D printed titanium chamber that can house several high-density silicon probes for semi-chronic recordings, while enabling simultaneous optogenetic stimulation. Surgical procedures are streamlined via custom 3D printed guides and implantation holders. We demonstrate the application of our method by recording multi- and single-unit data from areas V1 and V6 with 192 channels simultaneously, and show for the first time that optogenetic activation of excitatory neurons in area V6 can influence behavior in a detection task. Together, the work presented here will support future studies investigating the neural basis of perception and behavior in the marmoset.
]]></description>
<dc:creator>Jendritza, P.</dc:creator>
<dc:creator>Klein, F. J.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2021-11-02</dc:date>
<dc:identifier>doi:10.1101/2021.10.30.466578</dc:identifier>
<dc:title><![CDATA[Multi-area recordings and optogenetics in the awake, behaving marmoset]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.10.20.465101v1?rss=1">
<title>
<![CDATA[
Arnold tongue entrainment reveals dynamicalprinciples of the embryonic segmentation clock 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.10.20.465101v1?rss=1"
</link>
<description><![CDATA[
Living systems exhibit an unmatched complexity, due to countless, entangled interactions across scales. Here we aim to understand a complex system, i.e. segmentation timing in mouse embryos, without a reference to these detailed interactions. To this end, we develop a coarse-grained approach, in which theory guides the experimental identification of the segmentation clock entrainment responses.

We demonstrate period- and phase-locking of the segmentation clock across a wide range of entrainment parameters, including higher-order coupling. These quantifications allow to derive the phase response curve (PRC) and Arnold tongues of the segmentation clock, revealing its essential dynamical properties. Our results indicate that the somite segmentation clock has characteristics reminiscent of a highly non-linear oscillator close to an infinite period bifurcation and suggests the presence of long-term feedbacks.

Combined, this coarse-grained theoretical-experimental approach reveals how we can derive simple, essential features of a highly complex dynamical system, providing precise experimental control over the pace and rhythm of the somite segmentation clock.
]]></description>
<dc:creator>Sanchez, P. G. L.</dc:creator>
<dc:creator>Mochulska, V.</dc:creator>
<dc:creator>Mauffette Denis, C.</dc:creator>
<dc:creator>Moenke, G.</dc:creator>
<dc:creator>Tomita, T.</dc:creator>
<dc:creator>Tsuchida-Straeten, N.</dc:creator>
<dc:creator>Petersen, Y.</dc:creator>
<dc:creator>Sonnen, I.</dc:creator>
<dc:creator>Francois, P.</dc:creator>
<dc:creator>Aulehla, A.</dc:creator>
<dc:date>2021-10-21</dc:date>
<dc:identifier>doi:10.1101/2021.10.20.465101</dc:identifier>
<dc:title><![CDATA[Arnold tongue entrainment reveals dynamicalprinciples of the embryonic segmentation clock]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-10-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.11.459914v1?rss=1">
<title>
<![CDATA[
Is sensor space analysis good enough? Spatial patterns as a tool for assessing spatial mixing of EEG/MEG rhythms 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.11.459914v1?rss=1"
</link>
<description><![CDATA[
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
]]></description>
<dc:creator>Schaworonkow, N.</dc:creator>
<dc:creator>Nikulin, V. V.</dc:creator>
<dc:date>2021-09-15</dc:date>
<dc:identifier>doi:10.1101/2021.09.11.459914</dc:identifier>
<dc:title><![CDATA[Is sensor space analysis good enough? Spatial patterns as a tool for assessing spatial mixing of EEG/MEG rhythms]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.17.423293v1?rss=1">
<title>
<![CDATA[
Neural stem cells restore motor and cognitive function in a macaque model of Parkinson's disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.17.423293v1?rss=1"
</link>
<description><![CDATA[
Parkinsons disease (PD) evolves over an extended and variable period in humans; several years prior to the onset of classical motor symptoms, cognitive deficits as well as sleep and biological rhythm disorders develop and worsen with disease progression, significantly impacting the quality of life of patients. The gold standard MPTP macaque model of PD recapitulates the progression of motor and non-motor symptoms over contracted periods of time.

Here, this multidisciplinary and multiparametric study follows, in five animals, the steady progression of motor and non-motor symptoms and describes their reversal following bilateral grafts of neural precursors in diverse functional domains of the basal ganglia.

Results show unprecedented recovery from cognitive symptoms in addition to a strong clinical motor recuperation. Both motor and cognitive recovery and partial circadian rhythm recovery correlate with the degree of graft integration into the host environment as well as with in-vivo levels of striatal dopaminergic innervation and function.

Given inter-individuality of disease progression and recovery the present study underlines the importance of longitudinal multidisciplinary assessments in view of clinical translation and provides empirical evidence that integration of neural precursors following transplantation efficiently restores function at multiple levels in parkinsonian non-human primates.

One Sentence SummaryEmpirical evidence that cell therapy efficiently reverts cognitive and clinical motor symptoms in the non-human primate model of Parkinsons disease.
]]></description>
<dc:creator>Wianny, F.</dc:creator>
<dc:creator>Dzahini, K.</dc:creator>
<dc:creator>Fifel, K.</dc:creator>
<dc:creator>Wilson, C. R. E.</dc:creator>
<dc:creator>Bernat, A.</dc:creator>
<dc:creator>Dolmazon, V.</dc:creator>
<dc:creator>Misery, P.</dc:creator>
<dc:creator>Lamy, C.</dc:creator>
<dc:creator>Cooper, H. M.</dc:creator>
<dc:creator>Procyk, E.</dc:creator>
<dc:creator>Kennedy, H.</dc:creator>
<dc:creator>Savatier, P.</dc:creator>
<dc:creator>Dehay, C.</dc:creator>
<dc:creator>Vezoli, J.</dc:creator>
<dc:date>2020-12-19</dc:date>
<dc:identifier>doi:10.1101/2020.12.17.423293</dc:identifier>
<dc:title><![CDATA[Neural stem cells restore motor and cognitive function in a macaque model of Parkinson's disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.15.422902v1?rss=1">
<title>
<![CDATA[
The role of anatomical connection strength for interareal communication in macaque cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.15.422902v1?rss=1"
</link>
<description><![CDATA[
What is the relationship between anatomical connection strength and rhythmic synchronization? Simultaneous recordings of 15 cortical areas in two macaque monkeys show that interareal networks are functionally organized in spatially distinct modules with specific synchronization frequencies, i.e. frequency-specific functional connectomes. We relate the functional interactions between 91 area pairs to their anatomical connection strength defined in a separate cohort of twenty six subjects. This reveals that anatomical connection strength predicts rhythmic synchronization and vice-versa, in a manner that is specific for frequency bands and for the feedforward versus feedback direction, even if interareal distances are taken into account. These results further our understanding of structure-function relationships in large-scale networks covering different modality-specific brain regions and provide strong constraints on mechanistic models of brain function. Because this approach can be adapted to non-invasive techniques, it promises to open new perspectives on the functional organization of the human brain.
]]></description>
<dc:creator>Vezoli, J.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:creator>Bosman, C. A.</dc:creator>
<dc:creator>Bastos, A. M.</dc:creator>
<dc:creator>Lewis, C. M.</dc:creator>
<dc:creator>Kennedy, H.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2020-12-15</dc:date>
<dc:identifier>doi:10.1101/2020.12.15.422902</dc:identifier>
<dc:title><![CDATA[The role of anatomical connection strength for interareal communication in macaque cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.09.443096v1?rss=1">
<title>
<![CDATA[
Two functionally distinct Purkinje cell populations implement an internal model within a single olivo-cerebellar loop 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.09.443096v1?rss=1"
</link>
<description><![CDATA[
Olivo-cerebellar loops, where anatomical patches of the cerebellar cortex and inferior olive project one onto the other, form an anatomical unit of cerebellar computation. Here, we investigated how successive computational steps map onto olivo-cerebellar loops. Lobules IX-X of the cerebellar vermis, i.e. the nodulus and uvula, implement an internal model of the inner ears graviceptor, the otolith organs. We have previously identified two populations of Purkinje cells that participate in this computation: Tilt-selective cells transform egocentric rotation signals into allocentric tilt velocity signals, to track head motion relative to gravity, and translation-selective cells encode otolith prediction error. Here we show that, despite very distinct simple spike response properties, both types of Purkinje cells emit complex spikes that are proportional to sensory prediction error. This indicates that both cell populations comprise a single olivo-cerebellar loop, in which only translation-selective cells project to the inferior olive. We propose a neural network model where sensory prediction errors computed by translation-selective cells are used as a teaching signal for both populations, and demonstrate that this network can learn to implement an internal model of the otoliths.
]]></description>
<dc:creator>Angelaki, D.</dc:creator>
<dc:creator>Laurens, J.</dc:creator>
<dc:date>2021-05-10</dc:date>
<dc:identifier>doi:10.1101/2021.05.09.443096</dc:identifier>
<dc:title><![CDATA[Two functionally distinct Purkinje cell populations implement an internal model within a single olivo-cerebellar loop]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/502328v1?rss=1">
<title>
<![CDATA[
Visual exposure optimizes stimulus encoding in primary visual cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/502328v1?rss=1"
</link>
<description><![CDATA[
The brain adapts to the sensory environment. For example, simple sensory exposure can modify the response properties of early sensory neurons. How these changes affect the overall encoding and maintenance of stimulus information across neuronal populations remains unclear. We perform parallel recordings in the primary visual cortex of anesthetized cats and find that brief, repetitive exposure to structured visual stimuli enhances stimulus encoding by decreasing the selectivity and increasing the range of the neuronal responses that persist after stimulus presentation. Low-dimensional projection methods and simple classifiers demonstrate that visual exposure increases the segregation of persistent neuronal population responses into stimulus-specific clusters. These observed refinements preserve the representational details required for stimulus reconstruction and are detectable in post-exposure spontaneous activity. Assuming response facilitation and recurrent network interactions as the core mechanisms underlying stimulus persistence, we show that the exposure-driven segregation of stimulus responses can arise through strictly local plasticity mechanisms, also in the absence of firing rate changes. Our findings provide evidence for the existence of an automatic, unguided optimization process that enhances the encoding power of neuronal populations in early visual cortex, thus potentially benefiting simple readouts at higher stages of visual processing.
]]></description>
<dc:creator>Lazar, A.</dc:creator>
<dc:creator>Lewis, C.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:creator>Nikolic, D.</dc:creator>
<dc:date>2018-12-20</dc:date>
<dc:identifier>doi:10.1101/502328</dc:identifier>
<dc:title><![CDATA[Visual exposure optimizes stimulus encoding in primary visual cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-12-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.07.08.451663v1?rss=1">
<title>
<![CDATA[
Flexible utilization of spatial- and motor-based codes for the storage of visuo-spatial information 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.07.08.451663v1?rss=1"
</link>
<description><![CDATA[
Working memory provides flexible storage of information in service of upcoming behavioral goals. Some models propose specific fixed loci and mechanisms for the storage of visual information in working memory, such as sustained spiking in parietal and prefrontal cortex during working memory maintenance. An alternative view is that information can be remembered in a flexible format that best suits current behavioral goals. For example, remembered visual information might be stored in sensory areas for easier comparison to future sensory inputs, or might be re-coded into a more abstract action-oriented format and stored in motor areas. Here, we tested this hypothesis using a visuo-spatial working memory task where the required behavioral response was either known or unknown during the memory delay period. Using fMRI and multivariate decoding, we found that there was less information about remembered spatial position in early visual and parietal regions when the required response was known versus unknown. Further, a representation of the planned motor action emerged in primary somatosensory, primary motor, and premotor cortex during the same task condition where spatial information was reduced in early visual cortex. These results suggest that the neural networks supporting working memory can be strategically reconfigured depending on specific behavioral requirements during a canonical visual working memory paradigm.
]]></description>
<dc:creator>Henderson, M. M.</dc:creator>
<dc:creator>Rademaker, R. L.</dc:creator>
<dc:creator>Serences, J. T.</dc:creator>
<dc:date>2021-07-09</dc:date>
<dc:identifier>doi:10.1101/2021.07.08.451663</dc:identifier>
<dc:title><![CDATA[Flexible utilization of spatial- and motor-based codes for the storage of visuo-spatial information]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-07-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.26.433066v1?rss=1">
<title>
<![CDATA[
Improved correspondence of fMRI visual field localizer data after macroanatomical alignment 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.26.433066v1?rss=1"
</link>
<description><![CDATA[
Studying the visual system with fMRI often requires using localizer paradigms to define regions of interest (ROIs). However, the considerable interindividual variability of the cerebral cortex represents a crucial confound for group-level analyses. Cortex-based alignment (CBA) techniques reliably reduce interindividual macroanatomical variability. Yet, their utility has not been assessed for visual field localizer paradigms, which map specific parts of the visual field within retinotopically organized visual areas. We evaluated CBA for an attention-enhanced visual field localizer mapping homologous parts of each visual quadrant in 50 participants. We compared CBA with volume-based alignment and a surface-based analysis, which did not include macroanatomical alignment. CBA led to the strongest increase in the probability of activation overlap (up to 40 percent). On the group level, CBA led to the most consistent increase in ROI size while preserving vertical ROI symmetry. Overall, our results indicate, that in addition to the increased signal-to-noise ratio of a surface-based analysis macroanatomical alignment considerably improves statistical power. These findings confirm and extend the utility of CBA for the study of the visual system in the context of group analyses. CBA should be particularly relevant when studying neuropsychiatric disorders with abnormally increased interindividual macroanatomical variability.
]]></description>
<dc:creator>Qubad, M.</dc:creator>
<dc:creator>Barnes-Scheufler, C. V.</dc:creator>
<dc:creator>Schaum, M.</dc:creator>
<dc:creator>Raspor, E.</dc:creator>
<dc:creator>Roesler, L.</dc:creator>
<dc:creator>Peters, B.</dc:creator>
<dc:creator>Goebel, R.</dc:creator>
<dc:creator>Reif, A.</dc:creator>
<dc:creator>Bittner, R. A.</dc:creator>
<dc:date>2021-02-27</dc:date>
<dc:identifier>doi:10.1101/2021.02.26.433066</dc:identifier>
<dc:title><![CDATA[Improved correspondence of fMRI visual field localizer data after macroanatomical alignment]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.19.442954v1?rss=1">
<title>
<![CDATA[
Impaired and Intact Aspects of Attentional Competition and Prioritization during Visual Working Memory Encoding in Schizophrenia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.19.442954v1?rss=1"
</link>
<description><![CDATA[
BackgroundPeople with schizophrenia (PSZ) are impaired in attentional prioritization of non-salient but relevant stimuli over salient distractors during visual working memory (VWM) encoding. Conversely, guidance of top-down attention by external predictive cues is intact. Yet, it is unknown whether this preserved ability can help PSZ encode more information in the presence of salient distractors.

MethodsWe employed a visuospatial change-detection task using four Gabor patches with differing orientations in 66 PSZ and 74 healthy controls (HCS). Two Gabor patches flickered which were designated either as targets or distractors and either a predictive or a non-predictive cue was displayed to manipulate top-down attention, resulting in four conditions.

ResultsWe observed significant effects of group, salience and cue as well as significant interactions of salience by cue, group by salience and group by cue. Across all conditions, PSZ stored significantly less information in VWM than HCS. PSZ stored significantly less non-flickering than flickering information with a non-predictive cue. However, PSZ stored significantly more flickering and non-flickering information with a predictive cue.

ConclusionsOur findings indicate that control of attentional selection is impaired in schizophrenia. We demonstrate that additional top-down information significantly improves performance in PSZ. The observed deficit in attentional control suggests a disturbance of GABAergic inhibition in early visual areas. Moreover, our findings are indicative of a mechanism for enhancing attentional control in PSZ, which could be utilized by pro-cognitive interventions. Thus, the current paradigm is suitable to reveal both preserved and compromised cognitive component processes in schizophrenia.
]]></description>
<dc:creator>Barnes, C. V.</dc:creator>
<dc:creator>Roesler, L.</dc:creator>
<dc:creator>Schaum, M.</dc:creator>
<dc:creator>Schiweck, C.</dc:creator>
<dc:creator>Peters, B.</dc:creator>
<dc:creator>Reif, A.</dc:creator>
<dc:creator>Wibral, M.</dc:creator>
<dc:creator>Bittner, R. A.</dc:creator>
<dc:date>2021-05-19</dc:date>
<dc:identifier>doi:10.1101/2021.05.19.442954</dc:identifier>
<dc:title><![CDATA[Impaired and Intact Aspects of Attentional Competition and Prioritization during Visual Working Memory Encoding in Schizophrenia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.03.324848v1?rss=1">
<title>
<![CDATA[
Spatial representations in macaque hippocampal formation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.03.324848v1?rss=1"
</link>
<description><![CDATA[
The hippocampal formation is linked to spatial navigation, but there is little corroboration from freely-moving primates with concurrent monitoring of three-dimensional head and gaze stances. We recorded neurons and local field potentials across hippocampal regions in rhesus macaques during free foraging in an open environment while tracking their head and eye. Theta band activity was intermittently present at movement onset and modulated by saccades. Many cells were phase-locked to theta, with few showing theta phase precession. Most hippocampal neurons encoded a mixture of spatial variables beyond place fields and a negligible number showed prominent grid tuning. Spatial representations were dominated by facing location and allocentric direction, mostly in head, rather than gaze, coordinates. Importantly, eye movements strongly modulated neural activity in all regions. These findings reveal that the macaque hippocampal formation represents three-dimensional space using a multiplexed code, with head orientation and eye movement properties dominating over simple place and grid coding during free exploration.
]]></description>
<dc:creator>Mao, D.</dc:creator>
<dc:creator>Avila, E.</dc:creator>
<dc:creator>Caziot, B.</dc:creator>
<dc:creator>Laurens, J.</dc:creator>
<dc:creator>Dickman, D.</dc:creator>
<dc:creator>Angelaki, D.</dc:creator>
<dc:date>2020-10-04</dc:date>
<dc:identifier>doi:10.1101/2020.10.03.324848</dc:identifier>
<dc:title><![CDATA[Spatial representations in macaque hippocampal formation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-10-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.11.13.381467v1?rss=1">
<title>
<![CDATA[
Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.11.13.381467v1?rss=1"
</link>
<description><![CDATA[
Under natural conditions, the visual system often sees a given input repeatedly. This provides an opportunity to optimize processing of the repeated stimuli. Stimulus repetition has been shown to strongly modulate neuronal-gamma band synchronization, yet crucial questions remained open. Here we used magnetoencephalography in 30 human subjects and find that gamma decreases across ~10 repetitions and then increases across further repetitions, revealing plastic changes of the activated neuronal circuits. Crucially, changes induced by one stimulus did not affect responses to other stimuli, demonstrating stimulus specificity. Changes partially persisted when the inducing stimulus was repeated after 25 minutes of intervening stimuli. They were strongest in early visual cortex and increased interareal feedforward influences. Our results suggest that early visual cortex gamma synchronization enables adaptive neuronal processing of recurring stimuli. These and previously reported changes might be due to an interaction of oscillatory dynamics with established synaptic plasticity mechanisms.
]]></description>
<dc:creator>Stauch, B. J.</dc:creator>
<dc:creator>Peter, A.</dc:creator>
<dc:creator>Schuler, H.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2020-11-13</dc:date>
<dc:identifier>doi:10.1101/2020.11.13.381467</dc:identifier>
<dc:title><![CDATA[Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-11-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.02.433519v1?rss=1">
<title>
<![CDATA[
Slow rTMS to the left DLPFC enhances verbal memory formation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.02.433519v1?rss=1"
</link>
<description><![CDATA[
Encoding of episodic memories relies on stimulus-specific information processing and involves the left prefrontal cortex. We here present an incidental finding from a simultaneous EEG-TMS experiment as well as a replication of this unexpected effect. Our results reveal that stimulating the left dorsolateral prefrontal cortex (DLPFC) with slow repetitive transcranial magnetic stimulation (rTMS) leads to enhanced word memory performance. 40 healthy human participants engaged in a list learning paradigm. Half of the subjects (N=20) received 1 Hz rTMS to the left DLPFC while the other half (N=20) received 1 Hz rTMS to the vertex and served as a control group. Subjects receiving left DLPFC stimulation demonstrated enhanced memory performance compared to the control group. This effect was replicated in a double-blind within-subjects experiment where 24 participants received 1 Hz rTMS to the left DLPFC and vertex. In this second experiment, DLPFC stimulation also induced better memory performance compared to vertex stimulation. In addition to these behavioural effects, we found that 1 Hz rTMS to DLPFC induced stronger beta power modulation in posterior areas, a state which is known to be beneficial for memory encoding. Further analysis indicated, that beta modulations did not have an oscillatory origin. Instead, the observed beta modulations were a result of a spectral tilt, suggesting inhibition of these parietal regions. These results show that applying 1 Hz rTMS to DLPFC, an area involved in episodic memory formation, improves memory performance via modulating neural activity in parietal regions.
]]></description>
<dc:creator>van der Plas, M.</dc:creator>
<dc:creator>Braun, V.</dc:creator>
<dc:creator>Stauch, B. J.</dc:creator>
<dc:creator>Hanslmayr, S.</dc:creator>
<dc:date>2021-03-02</dc:date>
<dc:identifier>doi:10.1101/2021.03.02.433519</dc:identifier>
<dc:title><![CDATA[Slow rTMS to the left DLPFC enhances verbal memory formation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.02.27.433162v1?rss=1">
<title>
<![CDATA[
Dynamic signatures of the Eureka effect: An EEG study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.02.27.433162v1?rss=1"
</link>
<description><![CDATA[
The Eureka effect refers to the common experience of suddenly solving a problem. Here we study this effect in a pattern recognition paradigm that requires the segmentation of complex scenes and recognition of objects on the basis of Gestalt rules and prior knowledge. In the experiments both sensory evidence and prior knowledge were manipulated in order to obtain trials that do or do not converge towards a perceptual solution. Subjects had to detect objects in blurred scenes and signal recognition with manual responses. Neural dynamics were analyzed with high-density Electroencephalography (EEG) recordings. The results show significant changes of neural dynamics with respect to spectral distribution, coherence, phase locking, and fractal dimensionality. The Eureka effect was associated with increased coherence of oscillations in the alpha and theta band over widely distributed regions of the cortical mantle predominantly in the right hemisphere. This increase in coherence was associated with a decrease of beta band activity over parietal and central regions, and with a decrease of alpha power over frontal and occipital areas. In addition, there was a lateralized reduction of fractal dimensionality for activity recorded from the right hemisphere. These results suggest that the transition towards the solution of a perceptual task is mainly associated with a change of network dynamics in the right hemisphere that is characterized by enhanced coherence and reduced complexity. We propose that the Eureka effect requires cooperation of cortical regions involved in working memory, creative thinking, and the control of attention.
]]></description>
<dc:creator>Lu, Y.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:date>2021-03-01</dc:date>
<dc:identifier>doi:10.1101/2021.02.27.433162</dc:identifier>
<dc:title><![CDATA[Dynamic signatures of the Eureka effect: An EEG study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.30.361238v1?rss=1">
<title>
<![CDATA[
Head-free eye tracking, and efficient receptive field mapping in the marmoset 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.30.361238v1?rss=1"
</link>
<description><![CDATA[
The marmoset has emerged as a promising primate model system, in particular for visual neuroscience. Many common experimental paradigms rely on head fixation and an extended period of eye fixation during the presentation of salient visual stimuli. Both of these behavioral requirements can be challenging for marmosets. Here, we present two methodological developments, each addressing one of these difficulties. First, we show that it is possible to use a standard eye tracking system without head fixation to assess visual behavior in the marmoset. Eye tracking quality from head-free animals is sufficient to obtain precise psychometric functions from a visual acuity task. Secondly, we introduce a novel method for efficient receptive field mapping that does not rely on moving stimuli but uses fast flashing annuli and wedges. We present data recorded during head-fixation in areas V1 and V6 and show that receptive field locations are readily obtained within a short period of recording time. Thus, the methodological advancements presented in this work will contribute to establish the marmoset as a valuable model in neuroscience.
]]></description>
<dc:creator>Jendritza, P.</dc:creator>
<dc:creator>Klein, F. J.</dc:creator>
<dc:creator>Rohenkohl, G.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2020-10-30</dc:date>
<dc:identifier>doi:10.1101/2020.10.30.361238</dc:identifier>
<dc:title><![CDATA[Head-free eye tracking, and efficient receptive field mapping in the marmoset]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-10-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.09.417782v1?rss=1">
<title>
<![CDATA[
Cortical resonance selects coherent input 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.09.417782v1?rss=1"
</link>
<description><![CDATA[
Synchronization has been implicated in neuronal communication, but causal evidence remains indirect. We used optogenetics to generate depolarizing currents in pyramidal neurons of cat visual cortex, emulating excitatory synaptic inputs under precise temporal control, while measuring spike output. Cortex transformed constant excitation into strong gamma-band synchronization, revealing the well-known cortical resonance. Increasing excitation with ramps increased the strength and frequency of synchronization. Slow, symmetric excitation profiles revealed hysteresis of power and frequency. Crucially, white-noise input sequences enabled causal analysis of network transmission, establishing that cortical resonance selectively transmits coherent input components. Models composed of recurrently coupled excitatory and inhibitory units uncovered a crucial role of feedback inhibition and suggest that hysteresis can arise through spike-frequency adaptation. The presented approach provides a powerful means to investigate the resonance properties of local circuits and probe how these properties transform input and shape transmission.
]]></description>
<dc:creator>Lewis, C. M.</dc:creator>
<dc:creator>Ni, J.</dc:creator>
<dc:creator>Wunderle, T.</dc:creator>
<dc:creator>Jendritza, P. M.</dc:creator>
<dc:creator>Lazar, A.</dc:creator>
<dc:creator>Diester, I.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2020-12-11</dc:date>
<dc:identifier>doi:10.1101/2020.12.09.417782</dc:identifier>
<dc:title><![CDATA[Cortical resonance selects coherent input]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.09.418590v1?rss=1">
<title>
<![CDATA[
Developmental loss of ErbB4 in PV interneurons disrupts state-dependent cortical circuit dynamics. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.09.418590v1?rss=1"
</link>
<description><![CDATA[
GABAergic inhibition plays an important role in the establishment and maintenance of cortical circuits during development. Neuregulin 1 (Nrg1) and its interneuron-specific receptor ErbB4 are key elements of a signaling pathway critical for the maturation and proper synaptic connectivity of interneurons. Using conditional deletions of the ERBB4 gene in mice, we tested the role of this signaling pathway at two developmental timepoints in parvalbumin-expressing (PV) interneurons, the largest subpopulation of cortical GABAergic cells. Loss of ErbB4 in PV interneurons during embryonic, but not late postnatal, development leads to alterations in the activity of excitatory and inhibitory cortical neurons, along with severe disruption of cortical temporal organization. These impairments emerge by the end of the second postnatal week, prior to the complete maturation of the PV interneurons themselves. Early loss of ErbB4 in PV interneurons also results in profound dysregulation of excitatory pyramidal neuron dendritic architecture and a redistribution of spine density at the apical dendritic tuft. In association with these deficits, excitatory cortical neurons exhibit normal tuning for sensory inputs, but a loss of state-dependent modulation of the gain of sensory responses. Together these data support a key role for early developmental Nrg1/ErbB4 signaling in PV interneurons as powerful mechanism underlying the maturation of both the inhibitory and excitatory components of cortical circuits.
]]></description>
<dc:creator>Batista-Brito, R.</dc:creator>
<dc:creator>Majumdar, A.</dc:creator>
<dc:creator>Nuno, A.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:creator>Cardin, J. A.</dc:creator>
<dc:date>2020-12-10</dc:date>
<dc:identifier>doi:10.1101/2020.12.09.418590</dc:identifier>
<dc:title><![CDATA[Developmental loss of ErbB4 in PV interneurons disrupts state-dependent cortical circuit dynamics.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.11.13.381418v1?rss=1">
<title>
<![CDATA[
Stimulus-specific plasticity of macaque V1 spike rates and gamma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.11.13.381418v1?rss=1"
</link>
<description><![CDATA[
When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here we show in awake macaque area V1 that both, repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects showed some persistence on the timescale of minutes. Further, gamma increases were specific to the presented stimulus location. Importantly, repetition effects on gamma and on firing rates generalized to natural images. These findings suggest that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters, both for generating efficient stimulus responses and possibly for memory formation.
]]></description>
<dc:creator>Peter, A. S.</dc:creator>
<dc:creator>Stauch, B. J.</dc:creator>
<dc:creator>Shapcott, K.</dc:creator>
<dc:creator>Kouroupaki, K.</dc:creator>
<dc:creator>Schmiedt, J. T.</dc:creator>
<dc:creator>Klein, L.</dc:creator>
<dc:creator>Klon-Lipok, J.</dc:creator>
<dc:creator>Dowdall, J. R.</dc:creator>
<dc:creator>Schoelvinck, M. L.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:creator>Schmid, M. C.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2020-11-15</dc:date>
<dc:identifier>doi:10.1101/2020.11.13.381418</dc:identifier>
<dc:title><![CDATA[Stimulus-specific plasticity of macaque V1 spike rates and gamma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-11-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.07.328765v1?rss=1">
<title>
<![CDATA[
Anterior cingulate cortex hypofunction causes anti-social aggression in mice 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.07.328765v1?rss=1"
</link>
<description><![CDATA[
Controlling aggression is a crucial skill in social species like rodents and humans, and has been associated with anterior cingulate cortex (ACC). Here, we demonstrate a causal link between ACC hypofunction and failed aggression control in BALB/cJ mice. We first show that ACC in BALB/cJ mice is structurally degraded: Neuron density is decreased, with pervasive neuron death and neuro-toxic astroglia. Gene-set enrichment analysis suggested that this process is driven by neuronal degeneration, which then causes toxic astrogliosis. cFos expression across ACC indicated functional consequences: During aggressive encounters, ACC was engaged in control mice, but not BALB/cJ mice. Chemogenetically activating ACC during aggressive encounters drastically suppressed anti-social aggression but left adaptive aggression intact. The network effects of our chemogenetic perturbation suggest that this behavioural rescue is mediated by suppression of amygdala and hypothalamus and activation of mediodorsal thalamus. Together, these findings highlight the causal role of ACC in curbing anti-social aggression.
]]></description>
<dc:creator>van Heukelum, S.</dc:creator>
<dc:creator>Tulva, K.</dc:creator>
<dc:creator>Geers, F.</dc:creator>
<dc:creator>van Dulm, S.</dc:creator>
<dc:creator>Ruisch, H.</dc:creator>
<dc:creator>Mill, J.</dc:creator>
<dc:creator>Viana, J. F.</dc:creator>
<dc:creator>Beckmann, C. F.</dc:creator>
<dc:creator>Buitelaar, J. K.</dc:creator>
<dc:creator>Poelmans, G.</dc:creator>
<dc:creator>Glennon, J. C.</dc:creator>
<dc:creator>Vogt, B. A.</dc:creator>
<dc:creator>Havenith, M. N.</dc:creator>
<dc:creator>Franca, A. S. C.</dc:creator>
<dc:date>2020-10-08</dc:date>
<dc:identifier>doi:10.1101/2020.10.07.328765</dc:identifier>
<dc:title><![CDATA[Anterior cingulate cortex hypofunction causes anti-social aggression in mice]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.01.322750v1?rss=1">
<title>
<![CDATA[
The branching code: a model of actin-driven dendrite arborisation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.01.322750v1?rss=1"
</link>
<description><![CDATA[
Dendrites display a striking variety of neuronal type-specific morphologies, but the mechanisms and principles underlying such diversity remain elusive. A major player in defining the morphology of dendrites is the neuronal cytoskeleton, including evolutionarily conserved actin-modulatory proteins (AMPs). Still, we lack a clear understanding of how AMPs might support developmental phenomena such as neuron-type specific dendrite dynamics. To address precisely this level of in vivo specificity, we concentrated on a defined neuronal type, the class III dendritic arborisation (c3da) neuron of Drosophila larvae, displaying actin-enriched short terminal branchlets (STBs). Computational modelling reveals that the main branches of c3da neurons follow a general growth model based on optimal wiring, but the STBs do not. Instead, model STBs are defined by a short reach and a high affinity to grow towards the main branches. We thus concentrated on c3da STBs and developed new methods to quantitatively describe dendrite morphology and dynamics based on in vivo time-lapse imaging of mutants lacking individual AMPs. In this way, we extrapolated the role of these AMPs in defining STB properties. We propose that dendrite diversity is supported by the combination of a common step, refined by a neuron type-specific second level. For c3da neurons, we present a molecular model of how the combined action of multiple AMPs in vivo define the properties of these second level specialisations, the STBs.

In briefA quantitative morphological dissection of the concerted actin-modulatory protein actions provides a model of dendrite branchlet outgrowth.

HighlightsO_LIActin organisation in small terminal branchlets of Drosophila class III dendritic arborisation neurons
C_LIO_LISix actin-modulatory proteins individually control the characteristic morphology and dynamics of branchlets
C_LIO_LIQuantitative tools for dendrite morphology and branch dynamics enable a comparative analysis
C_LIO_LIA two-step computational growth model reproduces c3da dendrite morphology
C_LI
]]></description>
<dc:creator>Stürner, T.</dc:creator>
<dc:creator>Castro, A. F.</dc:creator>
<dc:creator>Philipps, M.</dc:creator>
<dc:creator>Cuntz, H.</dc:creator>
<dc:creator>Tavosanis, G.</dc:creator>
<dc:date>2020-10-03</dc:date>
<dc:identifier>doi:10.1101/2020.10.01.322750</dc:identifier>
<dc:title><![CDATA[The branching code: a model of actin-driven dendrite arborisation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-10-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.08.032706v1?rss=1">
<title>
<![CDATA[
Cortical Hierarchy and The Dual Counterstream Architecture. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.08.032706v1?rss=1"
</link>
<description><![CDATA[
Hierarchy is a major organizational principle of the cortex and underscores modern computational theories of cortical function. The local microcircuit amplifies long-distance inter-areal input, which show distance-dependent changes in their laminar profiles. Statistical modeling of these changes in laminar profiles demonstrates that inputs from multiple hierarchical levels to their target areas show remarkable consistency, allowing the construction of a cortical hierarchy based on a principle of hierarchical distance. The statistical modeling that is applied to structure can also be applied to laminar differences in the oscillatory coherence between areas thereby determining a functional hierarchy of the cortex. Close examination of the anatomy of inter-areal connectivity reveals a dual counterstream architecture with well-defined distance-dependent feedback and feedforward pathways in both the supra- and infragranular layers, suggesting a multiplicity of feedback pathways with well-defined functional properties. These findings are consistent with feedback connections providing a generative network involved in a wide range of cognitive functions. A dynamical model constrained by connectivity data shed insights into the experimentally observed signatures of frequency-dependent Granger causality for feedforward versus feedback signaling. Concerted experiments capitalizing on recent technical advances and combining tract-tracing, high-resolution fMRI, optogenetics and mathematical modeling hold the promise of a much improved understanding of lamina-constrained mechanisms of neural computation and cognition. However, because inter-areal interactions involve cortical layers that have been the target of important evolutionary changes in the primate lineage, these investigations will need to include human and non-human primates comparisons.

PlanO_LIIntroduction
C_LIO_LIHierarchy - signatures of inputs to the local circuits.
C_LIO_LIModels of hierarchy
C_LIO_LIHierarchy - input consistency
C_LIO_LIDual stream architecture.
C_LIO_LIFunctional characteristics of FF and FB pathways.
C_LIO_LIThe predictive brain and the importance of top-down generative networks.
C_LIO_LIConclusion.
C_LI
]]></description>
<dc:creator>Vezoli, J.</dc:creator>
<dc:creator>Magrou, L.</dc:creator>
<dc:creator>Wang, X.-J.</dc:creator>
<dc:creator>Knoblauch, K.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:creator>Kennedy, H.</dc:creator>
<dc:date>2020-04-09</dc:date>
<dc:identifier>doi:10.1101/2020.04.08.032706</dc:identifier>
<dc:title><![CDATA[Cortical Hierarchy and The Dual Counterstream Architecture.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/443846v1?rss=1">
<title>
<![CDATA[
Perceptual phenotypes: Perceptual gains and losses in synesthesia and schizophrenia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/443846v1?rss=1"
</link>
<description><![CDATA[
Individual differences in perception are widespread. Considering inter-individual variability, synesthetes experience stable additional sensations; schizophrenia patients suffer perceptual deficits in e.g. perceptual organization (alongside hallucinations and delusions). Is there a unifying principle explaining inter-individual variability in perception? There is good reason to believe perceptual experience results from inferential processes whereby sensory evidence is weighted by prior knowledge about the world. Different perceptual phenotypes may result from different precision weighting of sensory evidence and prior knowledge. We tested this hypothesis by comparing visibility thresholds in a perceptual hysteresis task across medicated schizophrenia patients, synesthetes, and controls. Participants rated the subjective visibility of stimuli embedded in noise while we parametrically manipulated the availability of sensory evidence. Additionally, precise long-term priors in synesthetes were leveraged by presenting either synesthesia-inducing or neutral stimuli. Schizophrenia patients showed increased visibility thresholds, consistent with overreliance on sensory evidence. In contrast, synesthetes exhibited lowered thresholds exclusively for synesthesia-inducing stimuli suggesting high-precision long-term priors. Additionally, in both synesthetes and schizophrenia patients explicit, short-term priors - introduced during the hysteresis experiment - lowered thresholds but did not normalize perception. Our results imply that distinct perceptual phenotypes might result from differences in the precision afforded to prior beliefs and sensory evidence, respectively.
]]></description>
<dc:creator>van Leeuwen, T. M.</dc:creator>
<dc:creator>Sauer, A.</dc:creator>
<dc:creator>Jurjut, A.-M.</dc:creator>
<dc:creator>Wibral, M.</dc:creator>
<dc:creator>Uhlhaas, P.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:creator>Melloni, L.</dc:creator>
<dc:date>2018-10-16</dc:date>
<dc:identifier>doi:10.1101/443846</dc:identifier>
<dc:title><![CDATA[Perceptual phenotypes: Perceptual gains and losses in synesthesia and schizophrenia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-10-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.09.01.277319v1?rss=1">
<title>
<![CDATA[
A simple model for detailed visual cortex maps predicts fixed hypercolumn sizes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.09.01.277319v1?rss=1"
</link>
<description><![CDATA[
Orientation hypercolumns in the visual cortex are delimited by the repeating pinwheel patterns of orientation selective neurons. We design a generative model for visual cortex maps that reproduces such orientation hypercolumns as well as ocular dominance maps while preserving retinotopy. The model uses a neural placement method based on t-distributed stochastic neighbour embedding (t-SNE) to create maps that order common features in the connectivity matrix of the circuit. We find that, in our model, hypercolumns generally appear with fixed cell numbers independently of the overall network size. These results would suggest that existing differences in absolute pinwheel densities are a consequence of variations in neuronal density. Indeed, available measurements in the visual cortex indicate that pinwheels consist of a constant number of [~]30, 000 neurons. Our model is able to reproduce a large number of characteristic properties known for visual cortex maps. We provide the corresponding software in our MAPStoolbox for Matlab.

In briefWe present a generative model that predicts visual map structures in the brain and a large number of their characteristic properties; a neural placement method for any given connectivity matrix.

HighlightsO_LIGenerative model with retinotopy, orientation preference and ocular dominance.
C_LIO_LIPrediction of constant neuronal numbers per orientation hypercolumn.
C_LIO_LICurated data shows constant [~]30, 000 neurons per pinwheel across species.
C_LIO_LISimple explanation for constant pinwheel and orientation hypercolumn ratios.
C_LIO_LIPrecise prediction of [~]80% nearest neighbour singularities with opposing polarity.
C_LIO_LIModel asymptotically approaches realistic normalised pinwheel densities.
C_LIO_LISmall brains with < [~]300 potential pinwheels exhibit salt-and-pepper maps.
C_LIO_LIDifferent map phenotypes can exist even for similar connectivity.
C_LI
]]></description>
<dc:creator>Weigand, M.</dc:creator>
<dc:creator>Cuntz, H.</dc:creator>
<dc:date>2020-09-02</dc:date>
<dc:identifier>doi:10.1101/2020.09.01.277319</dc:identifier>
<dc:title><![CDATA[A simple model for detailed visual cortex maps predicts fixed hypercolumn sizes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-09-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.06.17.156190v1?rss=1">
<title>
<![CDATA[
A general theory of coherence between brain areas 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.06.17.156190v1?rss=1"
</link>
<description><![CDATA[
What does neuronal coherence tell us about neuronal communication? Does coherence between field potentials (e.g. LFP, EEG, MEG) reflect spiking entrainment or coupling between oscillators? Is it a mechanism for communication between brain areas, or a byproduct of interareal connectivity and spectral power? We hypothesized that interareal coherence is explained by the fact that outputs from one cortical area give rise to synaptic inputs in the same brain area, and correlated synaptic inputs in another area. Our mathematical analysis demonstrates that coherence between a sending and receiving area is precisely predicted from only two parameters: Interareal connectivity and oscillatory synchronization in the sending area. This model predicts narrow-band coherence even in case of a flat transfer function and in the absence of spiking entrainment in a downstream area, and reproduces frequency-specific Granger-causality patterns between brain areas (gamma feedforward, beta feedback). In general, we find that Granger-causality between field potentials is dominated by oscillatory synchronization in the sending area, whereas spiking entrainment is determined by the resonant properties of the receiver. Our model accurately reproduces LFP-LFP beta-coherence between macaque areas 7B and F5 in the absence of beta phase-locking within area F5. Together, these findings uncover a precise mechanistic model of interareal coherence as a (by)product of connectivity and power.
]]></description>
<dc:creator>Schneider, M.</dc:creator>
<dc:creator>Dann, B.</dc:creator>
<dc:creator>Sheshadri, S.</dc:creator>
<dc:creator>Scherberger, H. G.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2020-06-18</dc:date>
<dc:identifier>doi:10.1101/2020.06.17.156190</dc:identifier>
<dc:title><![CDATA[A general theory of coherence between brain areas]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-06-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.07.09.195446v1?rss=1">
<title>
<![CDATA[
Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.07.09.195446v1?rss=1"
</link>
<description><![CDATA[
Class I ventral posterior dendritic arborisation (c1vpda) proprioceptive sensory neurons respond to contractions in the Drosophila larval body wall during crawling. Their dendritic branches run along the direction of contraction, possibly a functional requirement to maximise membrane curvature during crawling contractions. Although the molecular machinery of dendritic patterning in c1vpda has been extensively studied, the process leading to the precise elaboration of their comb-like shapes remains elusive. Here, to link dendrite shape with its proprioceptive role, we performed long-term, non-invasive, in vivo time-lapse imaging of c1vpda embryonic and larval morphogenesis to reveal a sequence of differentiation stages. We combined computer models and dendritic branch dynamics tracking to propose that distinct sequential phases of targeted growth and stochastic retraction achieve efficient dendritic trees both in terms of wire and function. Our study shows how dendrite growth balances structure–function requirements, shedding new light on general principles of self-organisation in functionally specialised dendrites.In brief An optimal wire and function trade-off emerges from noisy growth and stochastic retraction during Drosophila class I ventral posterior dendritic arborisation (c1vpda) dendrite development.HighlightsC1vpda dendrite outgrowth follows wire constraints.Stochastic retraction of functionally suboptimal branches in a subsequent growth phase.C1vpda growth rules favour branches running parallel to larval body wall contraction.Comprehensive growth model reproduces c1vpda development in silico.Highlights
Competing Interest StatementThe authors have declared no competing interest.View Full Text
]]></description>
<dc:creator>André Ferreira Castro</dc:creator>
<dc:creator>Lothar Baltruschat</dc:creator>
<dc:creator>Tomke Stürner</dc:creator>
<dc:creator>Amirhoushang Bahrami</dc:creator>
<dc:creator>Peter Jedlicka</dc:creator>
<dc:creator>Gaia Tavosanis</dc:creator>
<dc:creator>Hermann Cuntz</dc:creator>
<dc:date>2020-07-10</dc:date>
<dc:identifier>doi:10.1101/2020.07.09.195446</dc:identifier>
<dc:title><![CDATA[Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-07-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.07.07.191064v1?rss=1">
<title>
<![CDATA[
A developmental stretch-and-fill process that optimises dendritic wiring 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.07.07.191064v1?rss=1"
</link>
<description><![CDATA[
Circuit connectivity and computation depend on how dendrites branch and occupy space within neural tissue. While optimal wiring principles have long been known to constrain dendritic morphology and their scaling behaviour, the growth dynamics that produce such optimised structures remain unclear. Leveraging structural imaging across development, we identify two complementary growth strategies - inside-out versus outside-in - that together generate mature dendritic arbours. We formalise these dynamics in a mathematical model that captures the two growth modes and show that their interplay yields wiring-efficient, space-filling morphologies and class-specific developmental trajectories across species. This framework provides an algorithmic account of how local branching dynamics give rise to globally optimised architectures. By linking dendritic growth rules to functional design constraints, our theory offers a unifying description of dendritic differentiation and a basis for understanding how coverage and connectivity emerge during neural circuit formation.

In briefWe derive a detailed mathematical model that describes long-term time-lapse data of growing dendrites; it optimises total wiring and space-filling.

HighlightsO_LIFly neurons stretch and fill a given target area with precise scaling relations.
C_LIO_LIWe observe a sequence of two growth strategies.
C_LIO_LIEach growth type implements optimal wiring which leads to optimal space filling.
C_LIO_LIA model combining these programs captures the development of dendritic structures.
C_LI



O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/191064v2_ufig1.gif" ALT="Figure 1">
View larger version (50K):
org.highwire.dtl.DTLVardef@1f2aed5org.highwire.dtl.DTLVardef@1b449edorg.highwire.dtl.DTLVardef@161a400org.highwire.dtl.DTLVardef@1562be5_HPS_FORMAT_FIGEXP  M_FIG C_FIG
]]></description>
<dc:creator>Baltruschat, L.</dc:creator>
<dc:creator>Tavosanis, G.</dc:creator>
<dc:creator>Cuntz, H.</dc:creator>
<dc:date>2020-07-07</dc:date>
<dc:identifier>doi:10.1101/2020.07.07.191064</dc:identifier>
<dc:title><![CDATA[A developmental stretch-and-fill process that optimises dendritic wiring]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.01.14.906537v1?rss=1">
<title>
<![CDATA[
Dendritic normalisation improves learning in sparsely connected artificial neural networks 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.01.14.906537v1?rss=1"
</link>
<description><![CDATA[
Inspired by the physiology of neuronal systems in the brain, artificial neural networks have become an invaluable tool for machine learning applications. However, their biological realism and theoretical tractability are limited, resulting in poorly understood parameters. We have recently shown that biological neuronal firing rates in response to distributed inputs are largely independent of size, meaning that neurons are typically responsive to the proportion, not the absolute number, of their inputs that are active. Here we introduce such a normalisation, where the strength of a neurons afferents is divided by their number, to various sparsely-connected artificial networks. The learning performance is dramatically increased, providing an improvement over other widely-used normalisations in sparse networks. The resulting machine learning tools are universally applicable and biologically inspired, rendering them better understood and more stable in our tests.
]]></description>
<dc:creator>Bird, A. D.</dc:creator>
<dc:creator>Cuntz, H.</dc:creator>
<dc:date>2020-01-15</dc:date>
<dc:identifier>doi:10.1101/2020.01.14.906537</dc:identifier>
<dc:title><![CDATA[Dendritic normalisation improves learning in sparsely connected artificial neural networks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-01-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/793729v1?rss=1">
<title>
<![CDATA[
Gamma-cycle duration predicts instantaneous amplitude, spike rate and synchrony in macaque V1. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/793729v1?rss=1"
</link>
<description><![CDATA[
Circuits of excitatory and inhibitory neurons can generate rhythmic activity in the gamma frequency-range (30-80Hz). Individual gamma-cycles show spontaneous variability in amplitude and duration. The mechanisms underlying this variability are not fully understood. We recorded local-field-potentials (LFPs) and spikes from awake macaque V1, and developed a noise-robust method to detect gamma-cycle amplitudes and durations. Amplitudes and durations showed a weak but positive correlation. This correlation, and the joint amplitude-duration distribution, is well reproduced by a dampened harmonic oscillator driven by stochastic noise. We show that this model accurately fits LFP power spectra and is equivalent to a linear PING (Pyramidal Interneuron Network Gamma) circuit. The model recapitulates two additional features of V1 gamma: (1) Amplitude-duration correlations decrease with oscillation strength; (2) Amplitudes and durations exhibit strong and weak autocorrelations, respectively, depending on oscillation strength. Finally, longer gamma-cycles are associated with stronger spike-synchrony, but lower spike-rates in both (putative) excitatory and inhibitory neurons. In sum, V1 gamma-dynamics are well described by the simplest possible model of gamma: A linear harmonic oscillator driven by noise.
]]></description>
<dc:creator>Spyropoulos, G.</dc:creator>
<dc:creator>Dowdall, J. R.</dc:creator>
<dc:creator>Scholvinck, M. L.</dc:creator>
<dc:creator>Bosman, C. A.</dc:creator>
<dc:creator>Lima, B.</dc:creator>
<dc:creator>Peter, A.</dc:creator>
<dc:creator>Onorato, I.</dc:creator>
<dc:creator>Klon-Lipok, J.</dc:creator>
<dc:creator>Roese, R.</dc:creator>
<dc:creator>Neuenschwander, S.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2019-10-05</dc:date>
<dc:identifier>doi:10.1101/793729</dc:identifier>
<dc:title><![CDATA[Gamma-cycle duration predicts instantaneous amplitude, spike rate and synchrony in macaque V1.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-10-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.03.26.010678v1?rss=1">
<title>
<![CDATA[
Gamma-rhythmic input causes spike output 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.03.26.010678v1?rss=1"
</link>
<description><![CDATA[
The gamma rhythm has been implicated in neuronal communication, but causal evidence remains indirect. We measured spike output of local neuronal networks and emulated their synaptic input through optogenetics. Opsins provide currents through somato-dendritic membranes, similar to synapses, yet under experimental control with high temporal precision. We expressed Channelrhodopsin-2 in excitatory neurons of cat visual cortex and recorded neuronal responses to light with different temporal characteristics. Sine waves of different frequencies entrained neuronal responses with a reliability that peaked for input frequencies in the gamma band. Crucially, we also presented white-noise sequences, because their temporal unpredictability enables analysis of causality. Neuronal spike output was caused specifically by the inputs gamma component. This gamma-specific transfer function is likely an emergent property of in-vivo networks with feedback inhibition. The method described here could reveal the transfer function between the input to any one and the output of any other neuronal group.
]]></description>
<dc:creator>Lewis, C. M.</dc:creator>
<dc:creator>Ni, J.</dc:creator>
<dc:creator>Wunderle, T.</dc:creator>
<dc:creator>Jendritza, P.</dc:creator>
<dc:creator>Diester, I.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2020-03-26</dc:date>
<dc:identifier>doi:10.1101/2020.03.26.010678</dc:identifier>
<dc:title><![CDATA[Gamma-rhythmic input causes spike output]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-03-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/275818v1?rss=1">
<title>
<![CDATA[
Why not record from every channel with a CMOS scanning probe? 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/275818v1?rss=1"
</link>
<description><![CDATA[
It is an uninformative truism to state that the brain operates at multiple spatial and temporal scales, each with each own set of emergent phenomena. More worthy of attention is the point that our current understanding of it cannot clearly indicate which of these phenomenological scales are the significant contributors to the brains function and primary output (i.e. behaviour). Apart from the sheer complexity of the problem, a major contributing factor to this state of affairs is the lack of instrumentation that can simultaneously address these multiple scales without causing function altering damages to the underlying tissue. One important facet of this problem is that standard neural recording devices normally require one output connection per electrode. This limits the number of electrodes that can fit along the thin shafts of implantable probes generating a limiting balance between density and spread. Sharing a single output connection between multiple electrodes relaxes this constraint and permits designs of ultra-high density probes.

Here we report the design and in-vivo validation of such a device, a complementary metal-oxide-semiconductor (CMOS) scanning probe with 1344 electrodes; the outcome of the European research project NeuroSeeker. We show that this design targets both local and global spatial scales by allowing the simultaneous recording of more than 1000 neurons spanning 7 functional regions with a single shaft. The neurons show similar recording longevity and signal to noise ratio to passive probes of comparable size and no adverse effects in awake or anesthetized animals. Addressing the data management of this device we also present novel visualization and monitoring methods. Using the probe with freely moving animals we show how accessing a number of cortical and subcortical brain regions offers a novel perspective on how the brain operates around salient behavioural events. Finally, we compare this probe with lower density, non CMOS designs (which have to adhere to the one electrode per output line rule). We show that an increase in density results in capturing neural firing patterns, undetectable by lower density devices, which correlate to self-similar structures inherent in complex naturalistic behaviour.

To help design electrode configurations for future, even higher density, CMOS probes, recordings from many different brain regions were obtained with an ultra-dense passive probe.
]]></description>
<dc:creator>Dimitriadis, G.</dc:creator>
<dc:creator>Neto, J. P.</dc:creator>
<dc:creator>Aarts, A.</dc:creator>
<dc:creator>Alexandru, A.</dc:creator>
<dc:creator>Ballini, M.</dc:creator>
<dc:creator>Battaglia, F.</dc:creator>
<dc:creator>Calcaterra, L.</dc:creator>
<dc:creator>David, F.</dc:creator>
<dc:creator>Fiath, R.</dc:creator>
<dc:creator>Frazao, J.</dc:creator>
<dc:creator>Geerts, J.</dc:creator>
<dc:creator>Gentet, L. J.</dc:creator>
<dc:creator>Helleputte, N. V.</dc:creator>
<dc:creator>Holzhammer, T.</dc:creator>
<dc:creator>Hoof, C. v.</dc:creator>
<dc:creator>Horvath, D.</dc:creator>
<dc:creator>Lopes, G.</dc:creator>
<dc:creator>Maris, E.</dc:creator>
<dc:creator>Marques-Smith, A.</dc:creator>
<dc:creator>Marton, G.</dc:creator>
<dc:creator>Meszena, D.</dc:creator>
<dc:creator>Mitra, S.</dc:creator>
<dc:creator>Musa, S.</dc:creator>
<dc:creator>Neves, H.</dc:creator>
<dc:creator>Nogueira, J.</dc:creator>
<dc:creator>Orban, G. A.</dc:creator>
<dc:creator>Pothof, F.</dc:creator>
<dc:creator>Putzeys, J.</dc:creator>
<dc:creator>Raducanu, B.</dc:creator>
<dc:creator>Ruther, P.</dc:creator>
<dc:creator>Schroeder, T.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:creator>Tiesinga, P.</dc:creator>
<dc:creator>Ulbert, I.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Welkenhuysen, M.</dc:creator>
<dc:creator>Kampff, A. R.</dc:creator>
<dc:date>2018-03-03</dc:date>
<dc:identifier>doi:10.1101/275818</dc:identifier>
<dc:title><![CDATA[Why not record from every channel with a CMOS scanning probe?]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-03-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.02.09.940841v1?rss=1">
<title>
<![CDATA[
Cortical network mechanisms of response inhibition 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.02.09.940841v1?rss=1"
</link>
<description><![CDATA[
Both the right inferior frontal gyrus (rIFG) and the pre-supplementary motor area (pre-SMA) are crucial for successful response inhibition. However, the particular functional roles of those two regions have been controversially debated for more than a decade now. It is unclear whether the rIFG directly initiates stopping or serves an attentional function, whereas the stopping is triggered by the pre-SMA. The current multimodal MEG/fMRI study sought to clarify the role and temporal activation order of both regions in response inhibition using a selective stopping task. This task dissociates inhibitory from attentional processes. Our results reliably reveal a temporal precedence of rIFG over pre-SMA. Moreover, connectivity during response inhibition is directed from rIFG to pre-SMA and predicts stopping performance. Response inhibition is implemented via beta-band oscillations. Our findings support the hypothesis that response inhibition is initiated by the rIFG as a form of attention-independent top-down control.
]]></description>
<dc:creator>Schaum, M.</dc:creator>
<dc:creator>Pinzuti, E.</dc:creator>
<dc:creator>Sebastian, A.</dc:creator>
<dc:creator>Lieb, K.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Mobascher, A.</dc:creator>
<dc:creator>Jung, P.</dc:creator>
<dc:creator>Wibral, M.</dc:creator>
<dc:creator>Tuescher, O.</dc:creator>
<dc:date>2020-02-10</dc:date>
<dc:identifier>doi:10.1101/2020.02.09.940841</dc:identifier>
<dc:title><![CDATA[Cortical network mechanisms of response inhibition]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-02-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.02.03.931956v1?rss=1">
<title>
<![CDATA[
Signal transfer of visual stimuli to V4 occurs in gamma-rhythmic, pulsed information packages 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.02.03.931956v1?rss=1"
</link>
<description><![CDATA[
SummarySelective visual attention allows the brain to focus on behaviorally relevant information while ignoring irrelevant signals. As a possible mechanism, routing by synchronization was proposed: neural populations sending attended signals align their gamma-rhythmic activities with receiving populations, such that spikes from the senders arrive at excitability peaks of the receivers, enhancing signal transfer. Conversely, the non-attended signals arrive unaligned to the receivers oscillation, reducing signal transfer. Therefore, visual signals should be transferred through periodically pulsed information packages, resulting in a modulation of the stimulus content within the receivers activity by its gamma phase and amplitude. To test this prediction, we quantified gamma phase-specific stimulus content within neural activity from area V4 of macaques performing a visual attention task. For the attended stimulus we find enhanced stimulus content reaching its maximum near excitability peaks, with effect magnitude increasing with oscillation amplitude, establishing a functional link between selective processing and gamma activity.
]]></description>
<dc:creator>Lisitsyn, D.</dc:creator>
<dc:creator>Grothe, I.</dc:creator>
<dc:creator>Kreiter, A. K.</dc:creator>
<dc:creator>Ernst, U. A.</dc:creator>
<dc:date>2020-02-03</dc:date>
<dc:identifier>doi:10.1101/2020.02.03.931956</dc:identifier>
<dc:title><![CDATA[Signal transfer of visual stimuli to V4 occurs in gamma-rhythmic, pulsed information packages]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-02-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/795567v1?rss=1">
<title>
<![CDATA[
Brain rhythms shift and deploy attention 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/795567v1?rss=1"
</link>
<description><![CDATA[
One of the most central cognitive functions is attention. Its neuronal underpinnings have primarily been studied during conditions of sustained attention. Much less is known about the neuronal dynamics underlying the processes of shifting attention in space, as compared to maintaining it on one stimulus, and of deploying it to a particular stimulus. Here, we use ECoG to investigate four rhythms across large parts of the left hemisphere of two macaque monkeys during a task that allows investigation of deployment and shifting. Shifting involved a strong transient enhancement of power in a 2-7 Hz theta band in frontal, pre-motor and visual areas, and reductions of power in an 11-20 Hz beta band in a fronto-centro-parietal network and in a 29-36 Hz high-beta band in premotor cortex. Deployment of attention to the contralateral hemifield involved an enhancement of beta power in parietal areas, a concomitant reduction of high-beta power in pre-motor areas and an enhancement of power in a 60-76 Hz gamma band in extra-striate cortex. Effects due to shifting occurred earlier than effects due to deployment. These results demonstrate that the four investigated rhythms are involved in attentional allocation, with striking differences between shifting and deployment between different brain areas.nnSignificanceWe are often confronted by many visual stimuli, and attentional mechanisms select one stimulus for in-depth processing. This involves that attention is shifted between stimuli and deployed to one stimulus at a time. Prior studies have revealed that these processes are subserved by several brain rhythms. Therefore, we recorded brain activity in macaque monkeys with many electrodes distributed over large parts of their left hemisphere, while they performed a task that involved shifting and deploying attention. We found four dominant rhythms: theta (2-7 Hz), beta (11-20 Hz), high-beta (29-36 Hz) and gamma (60-76 Hz). Attentional shifting and deployment involved dynamic modulations in the strength of those rhythms with high specificity in space and time.
]]></description>
<dc:creator>Richter, C. G.</dc:creator>
<dc:creator>Bosman, C. A.</dc:creator>
<dc:creator>Vezoli, J.</dc:creator>
<dc:creator>Schoffelen, J.-M.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2019-10-07</dc:date>
<dc:identifier>doi:10.1101/795567</dc:identifier>
<dc:title><![CDATA[Brain rhythms shift and deploy attention]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-10-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/787911v1?rss=1">
<title>
<![CDATA[
A general principle of dendritic constancy – a neuron’s size and shape invariant excitability 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/787911v1?rss=1"
</link>
<description><![CDATA[
Reducing neuronal size results in less cell membrane and therefore lower input conductance. Smaller neurons are thus more excitable as seen in their voltage responses to current injections in the soma. However, the impact of a neurons size and shape on its voltage responses to synaptic activation in dendrites is much less understood. Here we use analytical cable theory to predict voltage responses to distributed synaptic inputs and show that these are entirely independent of dendritic length. For a given synaptic density, a neurons response depends only on the average dendritic diameter and its intrinsic conductivity. These results remain true for the entire range of possible dendritic morphologies irrespective of any particular arborisation complexity. Also, spiking models result in morphology invariant numbers of action potentials that encode the percentage of active synapses. Interestingly, in contrast to spike rate, spike times do depend on dendrite morphology. In summary, a neurons excitability in response to synaptic inputs is not affected by total dendrite length. It rather provides a homeostatic input-output relation that specialised synapse distributions, local non-linearities in the dendrites and synaptic plasticity can modulate. Our work reveals a new fundamental principle of dendritic constancy that has consequences for the overall computation in neural circuits.nnIn briefWe show that realistic neuron models essentially collapse to point neurons when stimulated by randomly distributed inputs instead of by single synapses or current injection in the soma.nnHighlightsO_LIA simple equation that predicts voltage in response to distributed synaptic inputs.nC_LIO_LIResponses to distributed and clustered inputs are largely independent of dendritic length.nC_LIO_LISpike rates in various Hodgkin Huxley (HH) like or Leaky Integrate-and-Fire (LIF) models are largely independent of morphology.nC_LIO_LIPrecise spike timing (firing pattern) depends on dendritic morphology.nC_LIO_LINeuroMorpho.Org database-wide analysis of the relation between dendritic morphology and electrophysiology.nC_LIO_LIOur equations set precise input-output relations in realistic dendrite models.nC_LI
]]></description>
<dc:creator>Cuntz, H.</dc:creator>
<dc:creator>Bird, A. D.</dc:creator>
<dc:creator>Beining, M.</dc:creator>
<dc:creator>Schneider, M.</dc:creator>
<dc:creator>Mediavilla, L.</dc:creator>
<dc:creator>Hoffmann, F. Z.</dc:creator>
<dc:creator>Deller, T.</dc:creator>
<dc:creator>Jedlicka, P.</dc:creator>
<dc:date>2019-10-01</dc:date>
<dc:identifier>doi:10.1101/787911</dc:identifier>
<dc:title><![CDATA[A general principle of dendritic constancy – a neuron’s size and shape invariant excitability]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-10-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/583955v1?rss=1">
<title>
<![CDATA[
A distinct class of bursting neurons with strong gamma synchronization and stimulus selectivity in monkey V1 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/583955v1?rss=1"
</link>
<description><![CDATA[
Cortical computation depends on interactions between excitatory and inhibitory neurons. The contributions of distinct neuron-types to sensory processing and network synchronization in primate visual-cortex remain largely undetermined. We show that in awake monkey V1, there exists a distinct cell-type ({approx}30% of neurons) that has narrow-waveform action-potentials, high spontaneous discharge-rates, and fires in high-frequency bursts. These neurons are more stimulus-selective and phase-locked to gamma (30-80Hz) oscillations as compared to other neuron types. Unlike the other neuron-types, their gamma phase-locking is highly predictive of their orientation tuning. We find evidence for strong rhythmic inhibition in these neurons, suggesting that they interact with interneurons to act as excitatory pacemakers for the V1 gamma rhythm. These neurons have not been observed in other primate cortical areas and we find that they are not present in rodent V1. However, they resemble the excitatory "chattering" neurons previously identified by intracellular recordings in cat V1. Given its properties, this neuron type should be pivotal for the encoding and transmission of V1 stimulus information.
]]></description>
<dc:creator>Onorato, I.</dc:creator>
<dc:creator>Neuenschwander, S.</dc:creator>
<dc:creator>Hoy, J.</dc:creator>
<dc:creator>Lima, B.</dc:creator>
<dc:creator>Rocha, K.-S.</dc:creator>
<dc:creator>Broggini, A. C.</dc:creator>
<dc:creator>Uran, C.</dc:creator>
<dc:creator>Spyropoulos, G.</dc:creator>
<dc:creator>Womelsdorf, T.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Niell, C.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2019-03-21</dc:date>
<dc:identifier>doi:10.1101/583955</dc:identifier>
<dc:title><![CDATA[A distinct class of bursting neurons with strong gamma synchronization and stimulus selectivity in monkey V1]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-03-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/552877v1?rss=1">
<title>
<![CDATA[
Hippocampal and neocortical oscillations are tuned to behavioral state in freely-behaving macaques 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/552877v1?rss=1"
</link>
<description><![CDATA[
Wireless recordings in macaque neocortex and hippocampus showed stronger theta oscillations during early-stage sleep than during alert volitional movement including walking. In contrast, hippocampal beta and gamma oscillations were prominent during walking and other active behaviors. These relations between hippocampal rhythms and behavioral states in the primate differ markedly from those observed in rodents. Primate neocortex showed similar changes in spectral content across behavioral state as the hippocampus.
]]></description>
<dc:creator>Talakoub, O.</dc:creator>
<dc:creator>Sayegh, P. F.</dc:creator>
<dc:creator>Womelsdorf, T.</dc:creator>
<dc:creator>Zinke, W.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Lewis, C. M.</dc:creator>
<dc:creator>Hoffman, K. L.</dc:creator>
<dc:date>2019-02-18</dc:date>
<dc:identifier>doi:10.1101/552877</dc:identifier>
<dc:title><![CDATA[Hippocampal and neocortical oscillations are tuned to behavioral state in freely-behaving macaques]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-02-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/529875v1?rss=1">
<title>
<![CDATA[
Excess neuronal branching allows for innervation of specific dendritic compartments in cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/529875v1?rss=1"
</link>
<description><![CDATA[
The connectivity of cortical microcircuits is a major determinant of brain function; defining how activity propagates between different cell types is key to scaling our understanding of individual neuronal behaviour to encompass functional networks. Furthermore, the integration of synaptic currents within a dendrite depends on the spatial organisation of inputs, both excitatory and inhibitory. We identify a simple equation to estimate the number of potential anatomical contacts between neurons; finding a linear increase in potential connectivity with cable length and maximum spine length, and a decrease with overlapping volume. This enables us to predict the mean number of candidate synapses for reconstructed cells, including those realistically arranged. We identify an excess of putative connections in cortical data, with densities of neurite higher than is necessary to reliably ensure the possible implementation of any given connection. We show that potential contacts allow the particular implementation of connectivity at a subcellular level.
]]></description>
<dc:creator>Bird, A. D.</dc:creator>
<dc:creator>Deters, L. H.</dc:creator>
<dc:creator>Cuntz, H.</dc:creator>
<dc:date>2019-01-24</dc:date>
<dc:identifier>doi:10.1101/529875</dc:identifier>
<dc:title><![CDATA[Excess neuronal branching allows for innervation of specific dendritic compartments in cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-01-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/421040v1?rss=1">
<title>
<![CDATA[
Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/421040v1?rss=1"
</link>
<description><![CDATA[
The integration of direct bottom-up inputs with contextual information is a canonical motif in neocortical circuits. In area V1, neurons may reduce their firing rates when the (classical) receptive field input can be predicted by the spatial context. We previously hypothesized that gamma-synchronization (30-80Hz) provides a complementary signal to rates, encoding whether stimuli are predicted from spatial context by preferentially synchronizing neuronal populations receiving predictable inputs. Here we investigated how rates and synchrony are modulated by predictive context. Large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma-synchronization, but only when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma-synchronization while increasing firing rates. Differences between colors, including strong gamma-responses to red, arose because of stimulus adaptation to a full-screen background, with a prominent difference in adaptation between M- and L-cone signaling pathways. Thus, synchrony signals whether RF inputs are predicted from spatial context and may encode relationships across space, while firing rates increase when stimuli are unpredicted from the context.
]]></description>
<dc:creator>Peter, A.</dc:creator>
<dc:creator>Uran, C.</dc:creator>
<dc:creator>Klon-Lipok, J.</dc:creator>
<dc:creator>Roese, R.</dc:creator>
<dc:creator>van Stijn, S.</dc:creator>
<dc:creator>Barnes, W.</dc:creator>
<dc:creator>Dowdall, J. R.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2018-09-18</dc:date>
<dc:identifier>doi:10.1101/421040</dc:identifier>
<dc:title><![CDATA[Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-09-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/413286v1?rss=1">
<title>
<![CDATA[
Cortico-basal-ganglia communication: Temporally structured activity for selective motor control 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/413286v1?rss=1"
</link>
<description><![CDATA[
Despite the hard-wired structural connectivity of neural pathways, neural circuits allow context-dependent reactions to sensory cues by triggering the desired movement. Cortico-basal-ganglia circuits seem particularly important for flexible motor control as this is impaired in Parkinsons disease (PD). We analysed subthalamic nucleus (STN) spike and cortical ECoG activity from PD patients performing a visually-cued hand grip task. Fast reaction times were preceded by enhanced STN spike-to-cortical gamma phase coupling irrespective of firing rate changes, suggesting a role of gamma coupling in motor preparation. STN spike timing was offset by half a cycle when comparing ipsilateral with contralateral movements. Additionally, cortical high-frequency activity increased more steeply within each gamma cycle at the sites that showed the strongest coupling with STN spikes. Cortico-basal-ganglia gamma coupling may thus help shape neural activity to facilitate selective motor control. The observation that this effect occurs independent of changes in mean firing rate has far-reaching implications.nnHighlightsO_LIFast RTs were preceded by enhanced STN spike-to-cortical gamma phase couplingnC_LIO_LISTN spike probability was significantly modulated relative to the gamma cyclenC_LIO_LIDuring ipsilateral movement, spikes were more likely at the opposite part of the cyclenC_LIO_LISTN output may thus help shape cortical gamma for selective motor controlnC_LI
]]></description>
<dc:creator>Fischer, P.</dc:creator>
<dc:creator>Lipski, W.</dc:creator>
<dc:creator>Neumann, W.-J.</dc:creator>
<dc:creator>Turner, R. S.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Brown, P.</dc:creator>
<dc:creator>Richardson, R. M.</dc:creator>
<dc:date>2018-09-11</dc:date>
<dc:identifier>doi:10.1101/413286</dc:identifier>
<dc:title><![CDATA[Cortico-basal-ganglia communication: Temporally structured activity for selective motor control]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-09-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/117804v1?rss=1">
<title>
<![CDATA[
A Theta Rhythm In Awake Macaque V1 And V4 And Its Attentional Modulation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/117804v1?rss=1"
</link>
<description><![CDATA[
Theta rhythms govern rodent sniffing and whisking, and human language processing. Human psychophysics suggests a role for theta also in visual attention. Yet, little is known about theta in visual areas and its attentional modulation. We used electrocorticography (ECoG) to record local field potentials (LFPs) simultaneously from areas V1, V2, V4 and TEO of two macaque monkeys performing a selective visual attention task. We found a {approx}4 Hz theta rhythm within both the V1-V2 and the V4-TEO region, and theta synchronization between them, with a predominantly feedforward directed influence. ECoG coverage of large parts of these regions revealed a surprising spatial correspondence between theta and visually induced gamma. Furthermore, gamma power was modulated with theta phase. Selective attention to the respective visual stimulus strongly reduced these theta-rhythmic processes, leading to an unusually strong attention effect for V1. Microsaccades (MSs) were partly locked to theta. Yet, neuronal theta rhythms tended to be even more pronounced for epochs devoid of MSs. Thus, we find an MS-independent theta rhythm specific to visually driven parts of V1-V2, which rhythmically modulates local gamma and entrains V4-TEO, and which is strongly reduced by attention. We propose that the less theta-rhythmic and thereby more continuous processing of the attended stimulus serves the exploitation of this behaviorally most relevant information. The theta-rhythmic and thereby intermittent processing of the unattended stimulus likely reflects the ecologically important exploration of less relevant sources of information.
]]></description>
<dc:creator>Spyropoulos, G.</dc:creator>
<dc:creator>Bosman, C. A.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2017-03-17</dc:date>
<dc:identifier>doi:10.1101/117804</dc:identifier>
<dc:title><![CDATA[A Theta Rhythm In Awake Macaque V1 And V4 And Its Attentional Modulation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-03-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/252791v1?rss=1">
<title>
<![CDATA[
A novel distance measure for the unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/252791v1?rss=1"
</link>
<description><![CDATA[
Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. SPOTDisClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDis). Then, it performs density-based clustering based on the resulting inter-pattern dissimilarity matrix. SPOTDisClust does not require binning and can detect complex patterns (beyond sequential activation) even when high levels of out-of-pattern "noise" spiking are present. Our method handles efficiently the additional information from increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns.nnAuthor summaryThe brain encodes information by ensembles of neurons, and recent technological developments allow researchers to simultaneously record from over thousands of neurons. Neurons exhibit spontaneous activity patterns, which are constrained by experience and development, limiting the portion of state space that is effectively visited. Patterns of spontaneous activity may contribute to shaping the synaptic connectivity matrix and contribute to memory consolidation, and synaptic plasticity formation depends crucially on the temporal spiking order among neurons. Hence, the unsupervised detection of spike sequences is a sine qua non for understanding how spontaneous activity contributes to memory formation. Yet, sequence detection presents major methodological challenges like the sparsity and stochasticity of neuronal output, and its high dimensionality. We propose a dissimilarity measure between neuronal patterns based on optimal transport theory, determining their similarity from the pairwise cross-correlation matrix, which can be taken as a proxy of the "trace" that is left on the synaptic matrix. We then perform unsupervised clustering and visualization of patterns using density clustering on the dissimilarity matrix and low-dimensional embedding techniques. This method does not require binning of spike times, is robust to noise, jitter and rate fluctuations, and can detect more patterns than the number of neurons.
]]></description>
<dc:creator>Grossberger, L.</dc:creator>
<dc:creator>Battaglia, F. P.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2018-01-24</dc:date>
<dc:identifier>doi:10.1101/252791</dc:identifier>
<dc:title><![CDATA[A novel distance measure for the unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-01-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/290817v1?rss=1">
<title>
<![CDATA[
Gamma synchronization between V1 and V4 improves behavioral performance 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/290817v1?rss=1"
</link>
<description><![CDATA[
Motor behavior is often driven by visual stimuli, relying on efficient feedforward communication from lower to higher visual areas. The Communication-through-Coherence hypothesis proposes that interareal communication depends on coherence at an optimal phase relation. While previous studies have linked effective communication to enhanced interareal coherence, it remains unclear, whether this interareal coherence occurs at an optimal phase relation that actually improves the stimulus transmission to behavioral report. We recorded local field potentials simultaneously from areas V1 and V4 of macaque monkeys performing a selective visual attention task, during which they reported changes of the attended stimulus. Gamma synchronization between V1 and V4, immediately preceding the stimulus change, predicted subsequent reaction times (RTs). Crucially, RTs were systematically slowed as trial-by-trial interareal gamma phase relations deviated from the phase relation at which V1 and V4 synchronized on average. These effects were specific to the attended stimulus and not due to local power or phase inside V1 or V4. We conclude that interareal gamma synchronization occurs at the optimal phase relation and thereby improves interareal communication and the effective transformation of sensory inputs into motor responses.
]]></description>
<dc:creator>Rohenkohl, G.</dc:creator>
<dc:creator>Bosman, C. A.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2018-04-02</dc:date>
<dc:identifier>doi:10.1101/290817</dc:identifier>
<dc:title><![CDATA[Gamma synchronization between V1 and V4 improves behavioral performance]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-04-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/252130v1?rss=1">
<title>
<![CDATA[
Rhythmic neural spiking and attentional samplingarising from cortical receptive field interactions 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/252130v1?rss=1"
</link>
<description><![CDATA[
Growing evidence suggests that distributed spatial attention may invoke theta (3-9 Hz) rhythmic sampling processes. The neuronal basis of such attentional sampling is however not fully understood. Here we show using array recordings in visual cortical area V4 of two awake macaques that presenting separate visual stimuli to the excitatory center and suppressive surround of neuronal receptive fields elicits rhythmic multi-unit activity (MUA) at 3-6 Hz. This neuronal rhythm did not depend on small fixational eye movements. In the context of a distributed spatial attention task, during which the monkeys detected a spatially and temporally uncertain target, reaction times (RT) exhibited similar rhythmic fluctuations. RTs were fast or slow depending on the target occurrence during high or low MUA, resulting in rhythmic MUA-RT cross-correlations at at theta frequencies. These findings suggest that theta-rhythmic neuronal activity arises from competitive receptive field interactions and that this rhythm may subserve attentional sampling.nnHighlightsO_LICenter-surround interactions induce theta-rhythmic MUA of visual cortex neuronsnC_LIO_LIThe MUA rhythm does not depend on small fixational eye movementsnC_LIO_LIReaction time fluctuations lock to the neuronal rhythm under distributed attentionnC_LI
]]></description>
<dc:creator>Kienitz, R.</dc:creator>
<dc:creator>Schmiedt, J. T.</dc:creator>
<dc:creator>Shapcott, K. A.</dc:creator>
<dc:creator>Kouroupaki, K.</dc:creator>
<dc:creator>Saunders, R.</dc:creator>
<dc:creator>Schmid, M. C.</dc:creator>
<dc:date>2018-01-23</dc:date>
<dc:identifier>doi:10.1101/252130</dc:identifier>
<dc:title><![CDATA[Rhythmic neural spiking and attentional samplingarising from cortical receptive field interactions]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-01-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/238774v1?rss=1">
<title>
<![CDATA[
Stimulus content shapes cortical response statistics 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/238774v1?rss=1"
</link>
<description><![CDATA[
Spike count correlations (SCCs) are ubiquitous in sensory cortices, are characterized by rich structure and arise from structured internal interactions. Yet, most theories of visual perception focus exclusively on the mean responses of individual neurons. Here, we argue that feedback interactions in primary visual cortex (V1) establish the context in which individual neurons process complex stimuli and that changes in visual context give rise to stimulus-dependent SCCs. Measuring V1 population responses to natural scenes in behaving macaques, we show that the fine structure of SCCs is stimulus-specific and variations in response correlations across-stimuli are independent of variations in response means. Moreover, we demonstrate that stimulus-specificity of SCCs in V1 can be directly manipulated by controlling the high-order structure of synthetic stimuli. We propose that stimulus-specificity of SCCs is a natural consequence of hierarchical inference where inferences on the presence of high-level image features modulate inferences on the presence of low-level features.
]]></description>
<dc:creator>Banyai, M.</dc:creator>
<dc:creator>Lazar, A.</dc:creator>
<dc:creator>Klein, L.</dc:creator>
<dc:creator>Klon-Lipok, J.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:creator>Orban, G.</dc:creator>
<dc:date>2017-12-22</dc:date>
<dc:identifier>doi:10.1101/238774</dc:identifier>
<dc:title><![CDATA[Stimulus content shapes cortical response statistics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-12-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/221051v1?rss=1">
<title>
<![CDATA[
Reconstruction of resting-state networks from macaqueelectrocorticographic data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/221051v1?rss=1"
</link>
<description><![CDATA[
The discovery of haemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electrophysiological cortical rhythms are organized into RSNs. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or electroencephalography (EEG), which limits the spatial scale on which RSNs can be investigated. Due to their close proximity to the cortical surface, electroencephalographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms. In this study we propose using source-space independent component analysis for identifying generators of resting-state cortical rhythms as recorded with ECoG and reconstructing their network structure. Their network structure is characterized by two kinds of connectivity: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. Using simulated data, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated as a consequence of first-order and higher-order volume-conduction effects, which troubles the interpretation of interaction measures based on imaginary phase-locking or coherence. The methodology is applied to resting-state beta (15-30 Hz) rhythms within the motor system of a macaque monkey and leads to the identification of a functional network of seven cortical generators that are distributed across the sensorimotor system. The spatial extent of the identified generators, together with consistent phase-lags, suggests that these rhythms can be viewed as being spatially continuous with complex dynamics including traveling waves. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human.
]]></description>
<dc:creator>Hindriks, R.</dc:creator>
<dc:creator>Micheli, C.</dc:creator>
<dc:creator>Bosman, C.</dc:creator>
<dc:creator>Oostenveld, R.</dc:creator>
<dc:creator>Lewis, C. M.</dc:creator>
<dc:creator>Mantini, D.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Deco, G.</dc:creator>
<dc:date>2017-11-17</dc:date>
<dc:identifier>doi:10.1101/221051</dc:identifier>
<dc:title><![CDATA[Reconstruction of resting-state networks from macaqueelectrocorticographic data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/212241v1?rss=1">
<title>
<![CDATA[
A simulation and comparison of dynamic functional connectivity methods 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/212241v1?rss=1"
</link>
<description><![CDATA[
There is a current interest in quantifying brain dynamic functional connectivity (DFC) based on neuroimaging data such as fMRI. Many methods have been proposed, and are being applied, revealing new insight into the brains dynamics. However, given that the ground truth for DFC in the brain is unknown, many concerns remain regarding the accuracy of proposed estimates. Since there exists many DFC methods it is difficult to assess differences in dynamic brain connectivity between studies. Here, we evaluate five different methods that together represent a wide spectrum of current approaches to estimating DFC (sliding window, tapered sliding window, temporal derivative, spatial distance and jackknife correlation). In particular, we were interested in each methods ability to track changes in covariance over time, which is a key property in DFC analysis. We found that all tested methods correlated positively with each other, but there were large differences in the strength of the correlations between methods. To facilitate comparisons with future DFC methods, we propose that the described simulations can act as benchmark tests for evaluation of methods. In this paper, we present dfcbenchmarker, which is a Python package where researchers can easily submit and compare their own DFC methods to evaluate its performance.
]]></description>
<dc:creator>Thompson, W. H.</dc:creator>
<dc:creator>Richter, C. G.</dc:creator>
<dc:creator>Plaven-Sigray, P.</dc:creator>
<dc:creator>Fransson, P.</dc:creator>
<dc:date>2017-11-01</dc:date>
<dc:identifier>doi:10.1101/212241</dc:identifier>
<dc:title><![CDATA[A simulation and comparison of dynamic functional connectivity methods]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-11-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/092569v1?rss=1">
<title>
<![CDATA[
In vivo magnetic recording of neuronal activity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/092569v1?rss=1"
</link>
<description><![CDATA[
Neuronal activity generates ionic flows and thereby both magnetic fields and electric potential differences, i.e. voltages. Voltage measurements are widely used, but suffer from isolating and smearing properties of tissue between source and sensor, are blind to ionic flow direction, and reflect the difference between two electrodes, complicating interpretation. Magnetic field measurements could overcome these limitations, but have been essentially limited to magnetoencephalography (MEG), using centimeter-sized, helium-cooled extracranial sensors. Here, we report on in vivo magnetic recordings of neuronal activity from visual cortex of cats with magnetrodes, specially developed needle-shaped probes carrying micron-sized, non-cooled magnetic sensors based on spin electronics. Event-related magnetic fields inside the neuropil were on the order of several nanoteslas, informing MEG source models and efforts for magnetic field measurements through MRI. Though the signal-to-noise ratio is still inferior to electrophysiology, this proof of concept demonstrates the potential to exploit the fundamental advantages of magnetophysiology.nnHIGHLIGHTSO_LISpin-electronics based probes achieve local magnetic recordings inside the neuropilnC_LIO_LIMagnetic field recordings were performed in vivo, in anesthetized cat visual cortexnC_LIO_LIEvent-related fields (ERFs) to visual stimuli were up to several nanoteslas in sizenC_LIO_LIERFs could be detected after averaging less than 20 trialsnC_LInnIN BRIEFCaruso et al. report in vivo, intra-cortical recordings of magnetic fields that reflect neuronal activity, using magnetrodes, i.e. micron size magnetic sensors based on spin electronics.
]]></description>
<dc:creator>Caruso, L.</dc:creator>
<dc:creator>Wunderle, T.</dc:creator>
<dc:creator>Lewis, C. M.</dc:creator>
<dc:creator>Valadeiro, J.</dc:creator>
<dc:creator>Trauchessec, V.</dc:creator>
<dc:creator>Trejo-Rosillo, J.</dc:creator>
<dc:creator>Amaral, J. P.</dc:creator>
<dc:creator>Ni, J.</dc:creator>
<dc:creator>Fermon, C.</dc:creator>
<dc:creator>Cardoso, S.</dc:creator>
<dc:creator>Freitas, P. P.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Pannetier-Lecoeur, M.</dc:creator>
<dc:date>2016-12-09</dc:date>
<dc:identifier>doi:10.1101/092569</dc:identifier>
<dc:title><![CDATA[In vivo magnetic recording of neuronal activity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-12-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/054288v1?rss=1">
<title>
<![CDATA[
Top-down modulation of stimulus drive via beta-gamma cross-frequency interaction. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/054288v1?rss=1"
</link>
<description><![CDATA[
AbstractSeveral recent studies have demonstrated that the bottom-up signaling of a visual stimulus is subserved by interareal gamma-band synchronization, whereas top-down influences are mediated by alpha-beta band synchronization. These processes may implement top-down control of stimulus processing if top-down and bottom-up mediating rhythms are coupled via cross-frequency interaction. To test this possibility, we investigated Granger-causal influences among awake male macaque primary visual area V1, higher visual area V4 and parietal control area 7a during attentional task performance. Top-down 7a-to-V1 beta-band influences enhanced visually driven V1-to-V4 gamma-band influences. This enhancement was spatially specific and largest when beta-band activity preceded gamma-band activity by [~]0.1 s, suggesting a causal effect of top-down processes on bottom-up processes. We propose that this cross-frequency interaction mechanistically subserves the attentional control of stimulus selection.nnSignificance StatementContemporary research indicates that the alpha-beta frequency band underlies top-down control, while the gamma-band mediates bottom-up stimulus processing. This arrangement inspires an attractive hypothesis, which posits that top-down beta-band influences directly modulate bottom-up gamma band influences via cross-frequency interaction. We evaluate this hypothesis determining that beta-band top-down influences from parietal area 7a to visual area V1 are correlated with bottom-up gamma frequency oscillations from V1 to area V4, in a spatially specific manner, and that this correlation is maximal when top-down activity precedes bottom-up activity. These results show that for top-down processes such as spatial attention, elevated top-down beta-band influences directly enhance feedforward stimulus induced gamma-band processing, leading to enhancement of the selected stimulus.
]]></description>
<dc:creator>Craig Geoffrey Richter</dc:creator>
<dc:creator>William Hedley Thompson</dc:creator>
<dc:creator>Conrado A Bosman</dc:creator>
<dc:creator>Pascal Fries</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-05-19</dc:date>
<dc:identifier>doi:10.1101/054288</dc:identifier>
<dc:title><![CDATA[Top-down modulation of stimulus drive via beta-gamma cross-frequency interaction.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-05-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/135467v1?rss=1">
<title>
<![CDATA[
Gamma-Band Resonance Of Visual Cortex To Optogenetic Stimulation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/135467v1?rss=1"
</link>
<description><![CDATA[
Activated visual cortex typically engages in neuronal synchronization in the gamma-frequency band (30-90 Hz). Gamma-band synchronization is related to cognitive functioning, and its mechanisms have been extensively investigated, predominantly through in-vitro studies. To further elucidate its mechanisms in-vivo, we performed simultaneous optogenetic stimulation and electrophysiological recordings of visual cortical areas 17 and 21a in the anesthetized cat. Viral transfection with AAV1 or AAV9 under a CamKII promoter led to robust Channelrhodopsin-2 (ChR2) expression. Immunohistochemical analysis showed that all ChR2-expressing neurons were negative for Parvalbumin, consistent with predominant or exclusive expression in excitatory neurons. Optogenetic stimulation used primarily surface illumination directly above the transfected and recorded cells. Stimulation with constant light led to strong and sustained gamma-band synchronization with strength and bandwidth similar to visually induced gamma. Rhythmic stimulation with light-pulse trains or sinusoidal light modulation revealed strongest resonance for gamma-band frequencies. Gamma resonance was confirmed by optogenetic white-noise stimulation. White-noise stimulation allowed the quantification of the transfer function between the optogenetic stimulation and the local field potential response. This transfer function showed a dominant peak in the gamma band. Thus, we find that visual cortical circuits resonate most strongly to gamma-band components in their input. This resonance renders both the sensitivity to input, and the output of these circuits, selectively tuned to gamma.nnSignificance StatementActivated groups of cortical neurons often display rhythmic synchronization in the gamma-frequency band (30-90 Hz). Gamma-band synchronization is particularly well studied in visual cortex. We used optogenetics to control visual cortex neurons with light. Different optogenetic stimulation protocols, using constant light, rhythmically modulated light or white-noise modulated light, all demonstrated that the investigated circuits predominantly resonate to stimulation in the gamma band. The observed gamma-band resonance renders visual cortical circuits most sensitive to gamma-rhythmic synaptic inputs. This in turn renders their spike output and the ensuing interareal synchronization gamma rhythmic.nnThis work was supported by DFG (SPP 1665, FOR 1847, FR2557/5-1-CORNET to P.F.; EXC 1086, DI 1908/5-1, DI 1908/6-1 to I.D.), BMBF (01GQ1301 to I.D.), EU (HEALTH-F2-2008-200728-BrainSynch, FP7-604102-HBP, FP7-600730-Magnetrodes to P.F.; ERC Starting Grant OptoMotorPath to I.D.), a European Young Investigator Award to P.F., the FENS-Kavli Network of Excellence to I.D., National Institutes of Health (1U54MH091657-WU-Minn-Consortium-HCP to P.F.), the LOEWE program (NeFF to P.F. and I.D.). Present address of I.D.: Optophysiology, Bernstein Center and BrainLinks-BrainTools, University of Freiburg, Albertstrase 23, 79104 Freiburg, Germany.nnAuthor contributionsJ.N, C.M.L., T.W., P.F. designed research; J.N, C.M.L., T.W., P.J., I.D., P.F. performed experiments; J.N., C.M.L., T.W. analyzed data; J.N., P.F. wrote the paper.
]]></description>
<dc:creator>Ni, J.</dc:creator>
<dc:creator>Lewis, C.</dc:creator>
<dc:creator>Wunderle, T.</dc:creator>
<dc:creator>Jendritza, P.</dc:creator>
<dc:creator>Diester, I.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2017-05-08</dc:date>
<dc:identifier>doi:10.1101/135467</dc:identifier>
<dc:title><![CDATA[Gamma-Band Resonance Of Visual Cortex To Optogenetic Stimulation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-05-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/070672v1?rss=1">
<title>
<![CDATA[
Neuronal gamma-band synchronization regulated by instantaneous modulations of the oscillation frequency 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/070672v1?rss=1"
</link>
<description><![CDATA[
Neuronal gamma-band synchronization shapes information flow during sensory and cognitive processing. A common view is that a stable and shared frequency over time is required for robust and functional synchronization. To the contrary, we found that non-stationary instantaneous frequency modulations were essential for synchronization. First, we recorded gamma rhythms in monkey visual area V1, and found that they synchronized by continuously modulating their frequency difference in a phase-dependent manner. The frequency modulation properties regulated both the phase-locking and the preferred phase-relation between gamma rhythms. Second, our experimental observations were in agreement with a biophysical model of gamma rhythms and were accurately predicted by the theory of weakly coupled oscillators revealing the underlying theoretical principles that govern gamma synchronization. Thus, synchronization through instantaneous frequency modulations represents a fundamental principle of gamma-band neural coordination that is likely generalizable to other brain rhythms.
]]></description>
<dc:creator>Eric Lowet</dc:creator>
<dc:creator>Mark Roberts</dc:creator>
<dc:creator>Alina Peter</dc:creator>
<dc:creator>Bart Gips</dc:creator>
<dc:creator>Peter De Weerd</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-08-22</dc:date>
<dc:identifier>doi:10.1101/070672</dc:identifier>
<dc:title><![CDATA[Neuronal gamma-band synchronization regulated by instantaneous modulations of the oscillation frequency]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-08-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/074609v1?rss=1">
<title>
<![CDATA[
Top-Down Beta Oscillatory Signaling Conveys Behavioral Context to Primary Visual Cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/074609v1?rss=1"
</link>
<description><![CDATA[
Top-down modulation of sensory processing is a critical neural mechanism subserving a number of important cognitive roles. Principally, top-down influences appear to inform lower-order sensory systems of the current  task at hand, and thus may convey behavioral context to these systems. Accumulating evidence indicates that top-down cortical influences are carried by directed interareal synchronization of oscillatory neuronal populations. An important question currently under investigation by a number of laboratories is whether the information conveyed by directed interareal synchronization depends on the frequency band in which it is conveyed. Recent results point to the beta frequency band as being particularly important for conveying task-related information. However, little is known about the nature of the information conveyed by top-down directed influences. To investigate the information content of top-down directed beta-frequency influences, we measured spectral Granger Causality using local field potentials recorded from microelectrodes chronically implanted in visual cortical areas V1, V4, and TEO, and then applied multivariate pattern analysis to the spatial patterns of top-down spectral Granger Causality in the visual cortex. We decoded behavioral context by discriminating patterns of top-down (V4/TEO [-&gt;] V1) beta-peak spectral Granger Causality for two different task rules governing the correct responses to visual stimuli. The results indicate that top-down directed influences in visual cortex are carried by beta oscillations, and differentiate current task demands even before visual stimulus processing. They suggest that top-down beta-frequency oscillatory processes may coordinate the processing of sensory information by conveying global knowledge states to early levels of the sensory cortical hierarchy independently of bottom-up stimulus-driven processing.
]]></description>
<dc:creator>Craig Geoffrey Richter</dc:creator>
<dc:creator>Richard Coppola</dc:creator>
<dc:creator>Steven L. Bressler</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-09-11</dc:date>
<dc:identifier>doi:10.1101/074609</dc:identifier>
<dc:title><![CDATA[Top-Down Beta Oscillatory Signaling Conveys Behavioral Context to Primary Visual Cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-09-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/060582v1?rss=1">
<title>
<![CDATA[
Gamma-rhythmic Gain Modulation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/060582v1?rss=1"
</link>
<description><![CDATA[
Cognition requires the dynamic modulation of effective connectivity, i.e. the modulation of the postsynaptic neuronal response to a given input. If postsynaptic neurons are rhythmically active, this might entail rhythmic gain modulation, such that inputs synchronized to phases of high gain benefit from enhanced effective connectivity. We show that visually induced gamma-band activity in awake macaque area V4 rhythmically modulates responses to unpredictable stimulus events. This modulation exceeded a simple additive superposition of a constant response onto ongoing gamma-rhythmic firing, demonstrating the modulation of multiplicative gain. Gamma phases leading to strongest neuronal responses also led to shortest behavioral reaction times, suggesting functional relevance of the effect. Furthermore, we find that constant optogenetic stimulation of anesthetized cat area 21a produces gamma-band activity entailing a similar gain modulation. As the gamma rhythm in area 21a did not spread backwards to area 17, this suggests that postsynaptic gamma is sufficient for gain modulation.
]]></description>
<dc:creator>Jianguang Ni</dc:creator>
<dc:creator>Thomas Wunderle</dc:creator>
<dc:creator>Christopher M. Lewis</dc:creator>
<dc:creator>Robert Desimone</dc:creator>
<dc:creator>Ilka Diester</dc:creator>
<dc:creator>Pascal Fries</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-06-24</dc:date>
<dc:identifier>doi:10.1101/060582</dc:identifier>
<dc:title><![CDATA[Gamma-rhythmic Gain Modulation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-06-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/049718v1?rss=1">
<title>
<![CDATA[
Two frequency bands contain the most stimulus-related information in visual cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/049718v1?rss=1"
</link>
<description><![CDATA[
Sensory cortices represent the world through the activity of diversely tuned cells. How the activity of single cells is coordinated within populations and across sensory hierarchies is largely unknown. Cortical oscillations may coordinate local and distributed neuronal groups. Using datasets from intracortical multi-electrode recordings and from large-scale electrocorticography (ECoG) grids, we investigated how visual features could be extracted from the local field potential (LFP) and how this compared with the information available from multi-unit activity (MUA). MUA recorded from macaque V1 contained comparable amounts of information as simultaneously recorded LFP power in two frequency bands, one in the alpha-beta band and the other in the gamma band. ECoG-LFP contained information in the same bands as microelectrode-LFP, even when identifying natural scenes. The fact that information was contained in the same bands in both intracortical and ECoG recordings suggests that oscillatory activity could play similar roles at both spatial scales.
]]></description>
<dc:creator>Christopher M Lewis</dc:creator>
<dc:creator>Conrado A Bosman</dc:creator>
<dc:creator>Nicolas M Brunet</dc:creator>
<dc:creator>Bruss Lima</dc:creator>
<dc:creator>Mark J. Roberts</dc:creator>
<dc:creator>Thilo Womelsdorf</dc:creator>
<dc:creator>Peter de Weerd</dc:creator>
<dc:creator>Sergio Neuenschwander</dc:creator>
<dc:creator>Wolf Singer</dc:creator>
<dc:creator>Pascal Fries</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-04-26</dc:date>
<dc:identifier>doi:10.1101/049718</dc:identifier>
<dc:title><![CDATA[Two frequency bands contain the most stimulus-related information in visual cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-04-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/021055v1?rss=1">
<title>
<![CDATA[
Stimulus induced visual cortical networks are recapitulated by spontaneous local and inter-areal synchronization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/021055v1?rss=1"
</link>
<description><![CDATA[
Intrinsic covariation of brain activity has been studied across many levels of brain organization. Between visual areas, neuronal activity covaries primarily among portions with similar retinotopic selectivity. We hypothesized that spontaneous inter-areal co-activation is subserved by neuronal synchronization. We performed simultaneous high-density electrocorticographic recordings across several visual areas in awake monkeys to investigate spatial patterns of local and inter-areal synchronization. We show that stimulation-induced patterns of inter-areal co-activation were reactivated in the absence of stimulation. Reactivation occurred through both, inter-areal co-fluctuation of local activity and inter-areal phase synchronization. Furthermore, the trial-by-trial covariance of the induced responses recapitulated the pattern of inter-areal coupling observed during stimulation, i.e. the signal correlation. Reactivation-related synchronization showed distinct peaks in the theta, alpha and gamma frequency bands. During passive states, this rhythmic reactivation was augmented by specific patterns of arrhythmic correspondence. These results suggest that networks of intrinsic covariation observed at multiple levels and with several recording techniques are related to synchronization and that behavioral state may affect the structure of intrinsic dynamics.
]]></description>
<dc:creator>Christopher Lewis</dc:creator>
<dc:creator>Conrado Bosmann</dc:creator>
<dc:creator>Thilo Womelsdorf</dc:creator>
<dc:creator>Pascal Fries</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-06-16</dc:date>
<dc:identifier>doi:10.1101/021055</dc:identifier>
<dc:title><![CDATA[Stimulus induced visual cortical networks are recapitulated by spontaneous local and inter-areal synchronization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-06-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/019547v1?rss=1">
<title>
<![CDATA[
Attention selectively gates afferent signal transmission to area V4 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/019547v1?rss=1"
</link>
<description><![CDATA[
Selective attention causes visual cortical neurons to act as if only one of multiple stimuli are within their receptive fields. This suggests that attention employs a, yet unknown, neuronal gating mechanism for transmitting only the information that is relevant for the current behavioral context. We introduce an experimental paradigm to causally investigate this putative gating and the mechanism underlying selective attention by determining the signal availability of two time-varying stimuli in local field potentials of V4 neurons. We find transmission of the low frequency (<20Hz) components only from the attended visual input signal and that the higher frequencies from both stimuli are attenuated. A minimal model implementing routing by synchrony replicates the attentional gating effect and explains the spectral transfer characteristics of the signals. It supports the proposal that selective gamma-band synchrony subserves signal routing in cortex and further substantiates our experimental finding that attention selectively gates signals already at the level of afferent synaptic input.
]]></description>
<dc:creator>Iris Grothe</dc:creator>
<dc:creator>David Rotermund</dc:creator>
<dc:creator>Simon D. Neitzel</dc:creator>
<dc:creator>Sunita Mandon</dc:creator>
<dc:creator>Udo A. Ernst</dc:creator>
<dc:creator>Andreas K. Kreiter</dc:creator>
<dc:creator>Klaus R. Pawelzik</dc:creator>
<dc:creator></dc:creator>
<dc:date>2015-05-19</dc:date>
<dc:identifier>doi:10.1101/019547</dc:identifier>
<dc:title><![CDATA[Attention selectively gates afferent signal transmission to area V4]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2015-05-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/005660v1?rss=1">
<title>
<![CDATA[
Practopoiesis: Or how life fosters a mind 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/005660v1?rss=1"
</link>
<description><![CDATA[
The mind is a biological phenomenon. Thus, biological principles of organization should also be the principles underlying mental operations. Practopoiesis states that the key for achieving intelligence through adaptation is an arrangement in which mechanisms laying a lower level of organization, by their operations and interaction with the environment, enable creation of mechanisms lying at a higher level of organization. When such an organizational advance of a system occurs, it is called a traverse. A case of traverse is when plasticity mechanisms (at a lower level of organization), by their operations, create a neural network anatomy (at a higher level of organization). Another case is the actual production of behavior by that network, whereby the mechanisms of neuronal activity operate to create motor actions. Practopoietic theory explains why the adaptability of a system increases with each increase in the number of traverses. With a larger number of traverses, a system can be relatively small and yet, produce a higher degree of adaptive/intelligent behavior than a system with a lower number of traverses. The present analyses indicate that the two well-known traverses--neural plasticity and neural activity--are not sufficient to explain human mental capabilities. At least one additional traverse is needed, which is named anapoiesis for its contribution in reconstructing knowledge e.g., from long-term memory into working memory. The conclusions bear implications for brain theory, the mind-body explanatory gap, and developments of artificial intelligence technologies.
]]></description>
<dc:creator>Danko Nikolic</dc:creator>
<dc:creator></dc:creator>
<dc:date>2014-05-29</dc:date>
<dc:identifier>doi:10.1101/005660</dc:identifier>
<dc:title><![CDATA[Practopoiesis: Or how life fosters a mind]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2014-05-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/005926v1?rss=1">
<title>
<![CDATA[
Untangling cross-frequency coupling in neuroscience 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/005926v1?rss=1"
</link>
<description><![CDATA[
Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.nnHighlightsFundamental caveats and confounds in the methodology of assessing CFC are discussed.nnSignificant CFC can be observed without any underlying physiological coupling.nnNon-stationarity of a time-series leads to spectral correlations interpreted as CFC.nnWe offer practical recommendations, which can relieve some of the current confounds.nnFurther theoretical and experimental work is needed to ground the CFC analysis.
]]></description>
<dc:creator>Juhan Aru</dc:creator>
<dc:creator>Jaan Aru</dc:creator>
<dc:creator>Viola Priesemann</dc:creator>
<dc:creator>Michael Wibral</dc:creator>
<dc:creator>Luiz Lana</dc:creator>
<dc:creator>Gordon Pipa</dc:creator>
<dc:creator>Wolf Singer</dc:creator>
<dc:creator>Raul Vicente</dc:creator>
<dc:creator></dc:creator>
<dc:date>2014-06-04</dc:date>
<dc:identifier>doi:10.1101/005926</dc:identifier>
<dc:title><![CDATA[Untangling cross-frequency coupling in neuroscience]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2014-06-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/004804v1?rss=1">
<title>
<![CDATA[
Visual areas exert feedforward and feedback influences through distinct frequency channels 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/004804v1?rss=1"
</link>
<description><![CDATA[
Visual cortical areas are thought to form a hierarchy and to subserve cognitive functions by interacting in both feedforward and feedback directions. While feedforward influences convey sensory signals, feedback influences modulate brain responses to a given sensory stimulus according to the current behavioural context. Many studies have demonstrated effects of feedback influences on feedforward driven responses and on behaviour. Also, anatomical projections in both directions have been identified. However, although these studies have revealed the anatomical paths and the neurophysiological consequences of influences in both directions, the neurophysiological mechanisms through which these influences are exerted remain largely elusive. Here we show that in the primate visual system, feedforward influences are carried by theta-band (~4 Hz) and gamma-band (~60-80 Hz) synchronization, and feedback influences by beta-band (~14-18 Hz) synchronization. These frequency-specific asymmetries in directed influences were revealed by simultaneous local field potential recordings from eight visual areas and an analysis of Granger-causal influences across all 28 pairs of areas. The asymmetries in directed influences correlated directly with asymmetries in anatomy and enabled us to build a visual cortical hierarchy from the influence asymmetries alone. Across different task periods, most areas stayed at their hierarchical position, whereas particularly frontal areas moved dynamically. Our results demonstrate that feedforward and feedback signalling use different frequency channels, which might subserve their differential communication requirements and lead to differential local consequences. The possibility to infer hierarchical relationships through functional data alone might make it possible to derive a cortical hierarchy in the living human brain.
]]></description>
<dc:creator>Andre M Bastos</dc:creator>
<dc:creator>Julien Vezoli</dc:creator>
<dc:creator>Conrado A Bosman</dc:creator>
<dc:creator>Jan-Mathijs Schoffelen</dc:creator>
<dc:creator>Robert Oostenveld</dc:creator>
<dc:creator>Jarrod R Dowdall</dc:creator>
<dc:creator>Peter De Weerd</dc:creator>
<dc:creator>Henry Kennedy</dc:creator>
<dc:creator>Pascal Fries</dc:creator>
<dc:creator></dc:creator>
<dc:date>2014-05-06</dc:date>
<dc:identifier>doi:10.1101/004804</dc:identifier>
<dc:title><![CDATA[Visual areas exert feedforward and feedback influences through distinct frequency channels]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2014-05-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.16.492063v1?rss=1">
<title>
<![CDATA[
Quantifying rhythmicity in perceptual reports 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.16.492063v1?rss=1"
</link>
<description><![CDATA[
Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the Discrete Fourier Transform (DFT) and the Least Square Spectrum (LSS). DFT and LSS can be applied both on the averaged accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effect) or on the population (random-effect) of simulated participants. Multiple comparisons across frequencies were corrected using False-Discovery-Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated Sensitivity, Specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the averaged-accuracy-time-course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved Sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest Specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effect approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.
]]></description>
<dc:creator>Tosato, T.</dc:creator>
<dc:creator>Rohenkohl, G.</dc:creator>
<dc:creator>Dowdall, J. R.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2022-05-16</dc:date>
<dc:identifier>doi:10.1101/2022.05.16.492063</dc:identifier>
<dc:title><![CDATA[Quantifying rhythmicity in perceptual reports]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.10.491373v1?rss=1">
<title>
<![CDATA[
The statistical power of three monkeys 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.10.491373v1?rss=1"
</link>
<description><![CDATA[
Neuroscience studies in non-human primates (NHP) often follow the rule of thumb that results observed in one animal must be replicated in at least one other. However, we lack a statistical justification for this rule of thumb, or an analysis of whether including three or more animals is better than including two. Yet, a formal statistical framework for experiments with few subjects would be crucial for experimental design, ethical justification, and data analysis. Also, including three or four animals in a study creates the possibility that the results observed in one animal will differ from those observed in the others: we need a statistically justified rule to resolve such situations. Here, I present a statistical framework to address these issues. This framework assumes that conducting an experiment will produce a similar result for a large proportion of the population (termed  representative), but will produce spurious results for a substantial proportion of animals (termed  outliers); the fractions of  representative and  outliers animals being defined by a prior distribution. I propose a procedure in which experimenters collect results from M animals and accept results that are observed in at least N of them ( N-out-of-M procedure). I show how to compute the risks  (of reaching an incorrect conclusion) and {beta} (of failing to reach a conclusion) for any prior distribution, and as a function of N and M. Strikingly, I find that the N-out-of-M model leads to a similar conclusion across a wide range of prior distributions: recordings from two animals lowers the risk  and therefore ensures reliable result, but leaves a large risk {beta}; and recordings from three animals and accepting results observed in two of them strikes an efficient balance between acceptable risks  and {beta}. This framework gives a formal justification for the rule of thumb of using at least two animals in NHP studies, suggests that recording from three animals when possible markedly improves statistical power, provides a statistical solution for situations where results are not consistent between all animals, and may apply to other types of studies involving few animals.
]]></description>
<dc:creator>Laurens, J.</dc:creator>
<dc:date>2022-05-10</dc:date>
<dc:identifier>doi:10.1101/2022.05.10.491373</dc:identifier>
<dc:title><![CDATA[The statistical power of three monkeys]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.04.28.489865v1?rss=1">
<title>
<![CDATA[
High resolution quantitative and functional MRI indicate lower myelination of thin and thick stripes in human secondary visual cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.04.28.489865v1?rss=1"
</link>
<description><![CDATA[
The characterization of cortical myelination is essential for the study of structure-function relationships in the human brain. However, knowledge about cortical myelination is largely based on post mortem histology, which generally renders direct comparison to function impossible. The repeating pattern of pale-thin-pale-thick stripes of cytochrome oxidase (CO) activity in the primate secondary visual cortex (V2) is a prominent columnar system, in which histology also indicates different myelination of thin/thick versus pale stripes. We used quantitative magnetic resonance imaging (qMRI) in conjunction with functional magnetic resonance imaging (fMRI) at ultra-high field strength (7 T) to localize and study myelination of stripes in four human participants at sub-millimeter resolution in vivo. Thin and thick stripes were functionally localized by exploiting their sensitivity to color and binocular disparity, respectively. Resulting functional activation maps showed robust stripe patterns in V2 which enabled further comparison of quantitative relaxation parameters between stripe types. Thereby, we found lower longitudinal relaxation rates (R1) of thin and thick stripes compared to surrounding gray matter in the order of 1-2%, indicating higher myelination of pale stripes. No consistent differences were found for effective transverse relaxation rates [Formula]. The study demonstrates the feasibility to investigate structure-function relationships in living humans within one cortical area at the level of columnar systems using qMRI.
]]></description>
<dc:creator>Haenelt, D.</dc:creator>
<dc:creator>Trampel, R.</dc:creator>
<dc:creator>Nasr, S.</dc:creator>
<dc:creator>Polimeni, J.</dc:creator>
<dc:creator>Tootell, R. B.</dc:creator>
<dc:creator>Sereno, M. I.</dc:creator>
<dc:creator>Pine, K.</dc:creator>
<dc:creator>Edwards, L. J.</dc:creator>
<dc:creator>Helbling, S.</dc:creator>
<dc:creator>Weiskopf, N.</dc:creator>
<dc:date>2022-04-29</dc:date>
<dc:identifier>doi:10.1101/2022.04.28.489865</dc:identifier>
<dc:title><![CDATA[High resolution quantitative and functional MRI indicate lower myelination of thin and thick stripes in human secondary visual cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-04-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.04.05.487135v1?rss=1">
<title>
<![CDATA[
The pitfalls of measuring representational similarityusing representational similarity analysis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.04.05.487135v1?rss=1"
</link>
<description><![CDATA[
Representational Similarity Analysis (RSA) is an innovative approach used to compare neural representations across individuals, species and computational models. Despite its popularity within neuroscience, psychology and artificial intelligence, this approach has led to difficult-to-reconcile and contradictory findings, particularly when comparing primate visual representations with deep neural networks (DNNs). Here, we demonstrate how such contradictory findings could arise due to incorrect inferences about mechanism when comparing complex systems processing high-dimensional stimuli. In a series of studies comparing computational models, primate cortex and human cortex we find two problematic phenomena: a "mimic effect", where confounds in stimuli can lead to high RSA-scores between provably dissimilar systems, and a "modulation effect", where RSA- scores become dependent on stimuli used for testing. Since our results bear on a number of influential findings, we provide recommendations to avoid these pitfalls and sketch a way forward to a more solid science of representation in cognitive systems.
]]></description>
<dc:creator>Dujmovic, M.</dc:creator>
<dc:creator>Bowers, J.</dc:creator>
<dc:creator>Adolfi, F.</dc:creator>
<dc:creator>Malhotra, G.</dc:creator>
<dc:date>2022-04-07</dc:date>
<dc:identifier>doi:10.1101/2022.04.05.487135</dc:identifier>
<dc:title><![CDATA[The pitfalls of measuring representational similarityusing representational similarity analysis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-04-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.04.01.486685v1?rss=1">
<title>
<![CDATA[
Explaining flexible continuous speech comprehension from individual motor rhythms 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.04.01.486685v1?rss=1"
</link>
<description><![CDATA[
When speech is too fast, the tracking of the acoustic signal along the auditory pathway deteriorates, leading to suboptimal speech segmentation and decoding of speech information. Thus, speech comprehension is limited by the temporal constraints of the auditory system. Here we ask whether individual differences in auditory-motor coupling strength in part shape these temporal constraints. In two behavioral experiments, we characterize individual differences in the comprehension of naturalistic speech as function of the individual synchronization between the auditory and motor systems and the preferred frequencies of the systems. Obviously, speech comprehension declined at higher speech rates. Importantly, however, both higher auditory-motor synchronization and higher spontaneous speech motor production rates were predictive of better speech-comprehension performance. Furthermore, performance increased with higher working memory capacity (Digit Span) and higher linguistic, model-based sentence predictability - particularly so at higher speech rates and for individuals with high auditory-motor synchronization. These findings support the notion of an individual preferred auditory- motor regime that allows for optimal speech processing. The data provide evidence for a model that assigns a central role to motor-system-dependent individual flexibility in continuous speech comprehension.
]]></description>
<dc:creator>Lubinus, C.</dc:creator>
<dc:creator>Keitel, A.</dc:creator>
<dc:creator>Obleser, J.</dc:creator>
<dc:creator>Poeppel, D.</dc:creator>
<dc:creator>Rimmele, J.</dc:creator>
<dc:date>2022-04-02</dc:date>
<dc:identifier>doi:10.1101/2022.04.01.486685</dc:identifier>
<dc:title><![CDATA[Explaining flexible continuous speech comprehension from individual motor rhythms]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-04-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.03.27.485924v1?rss=1">
<title>
<![CDATA[
Nonlinear EEG signatures of mind wandering during breath focus meditation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.03.27.485924v1?rss=1"
</link>
<description><![CDATA[
In meditation practices that involve focused attention to a specific object, novice practitioners often experience moments of distraction (i.e., mind wandering). Previous studies have investigated the neural correlates of mind wandering during meditation practice through Electroencephalography (EEG) using linear metrics (e.g., oscillatory power). However, their results are not fully consistent. Since the brain is known to be a chaotic/nonlinear system, it is possible that linear metrics cannot fully capture complex dynamics present in the EEG signal. In this study, we assess whether nonlinear EEG signatures can be used to characterize mind wandering during breath focus meditation in novice practitioners. For that purpose, we adopted an experience sampling paradigm in which 25 participants were iteratively interrupted during meditation practice to report whether they were focusing on the breath or thinking about something else. We compared the complexity of EEG signals during mind wandering and breath focus states using three different algorithms: Higuchis fractal dimension (HFD), Lempel-Ziv complexity (LZC), and Sample entropy (SampEn). Our results showed that EEG complexity was generally reduced during mind wandering relative to breath focus states. We conclude that EEG complexity metrics are appropriate to disentangle mind wandering from breath focus states in novice meditation practitioners, and therefore, they could be used in future EEG neurofeedback protocols to facilitate meditation practice.
]]></description>
<dc:creator>Lu, Y.</dc:creator>
<dc:creator>Rodriguez-Larios, J.</dc:creator>
<dc:date>2022-03-27</dc:date>
<dc:identifier>doi:10.1101/2022.03.27.485924</dc:identifier>
<dc:title><![CDATA[Nonlinear EEG signatures of mind wandering during breath focus meditation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-03-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.03.14.484223v1?rss=1">
<title>
<![CDATA[
Selective V1-to-V4 communication of attended stimuli mediated by attentional effects in V1 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.03.14.484223v1?rss=1"
</link>
<description><![CDATA[
Selective attention implements preferential routing of attended stimuli, likely through increasing the influence of the respective synaptic inputs on higher-area neurons. As the inputs of competing stimuli converge onto postsynaptic neurons, presynaptic circuits might offer the best target for attentional top-down influences. If those influences enabled presynaptic circuits to selectively entrain postsynaptic neurons, this might explain selective routing. Indeed, when two visual stimuli induce two gamma rhythms in V1, only the gamma induced by the attended stimulus entrains gamma in V4. Here, we modeled induced responses with a Dynamic Causal Model for Cross-Spectral Densities and found that selective entrainment can be explained by attentional modulation of intrinsic V1 connections. Specifically, local inhibition was decreased in the granular input layer and increased in the supragranular output layer of the V1 circuit that processed the attended stimulus. Thus, presynaptic attentional influences and ensuing entrainment were sufficient to mediate selective routing.

HIGHLIGHTSO_LIWe model selective visual attention in macaques using Dynamic Causal Modeling.
C_LIO_LIIntrinsic V1 modulation can explain attention effects in V1-V4 communication.
C_LIO_LIModulation of superficial and granular inhibition is key to induce the effects.
C_LIO_LIThose modulations increase V1-V4 communication in a feedforward manner.
C_LI

GRAPHICAL ABSTRACT

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/484223v2_ufig1.gif" ALT="Figure 1">
View larger version (32K):
org.highwire.dtl.DTLVardef@1cc79a7org.highwire.dtl.DTLVardef@146e6bdorg.highwire.dtl.DTLVardef@1d6bd6borg.highwire.dtl.DTLVardef@12dd663_HPS_FORMAT_FIGEXP  M_FIG C_FIG
]]></description>
<dc:creator>Katsanevaki, C.</dc:creator>
<dc:creator>Bastos, A. M.</dc:creator>
<dc:creator>Cagnan, H.</dc:creator>
<dc:creator>Bosman, C. A.</dc:creator>
<dc:creator>Friston, K. J.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2022-03-17</dc:date>
<dc:identifier>doi:10.1101/2022.03.14.484223</dc:identifier>
<dc:title><![CDATA[Selective V1-to-V4 communication of attended stimuli mediated by attentional effects in V1]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-03-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.31.494173v1?rss=1">
<title>
<![CDATA[
Top-down information flow drives lexical access when listening to continuous speech 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.31.494173v1?rss=1"
</link>
<description><![CDATA[
Speech is often structurally and semantically ambiguous. Here we study how the human brain uses sentence context to resolve lexical ambiguity. Twenty-one participants listened to spoken narratives while magneto-encephalography (MEG) was recorded. Stories were annotated for grammatical word class (noun, verb, adjective) under two hypothesised sources of information:  bottom-up: the most common word class given the words phonology;  top-down: the correct word class given the context. We trained a classifier on trials where the hypotheses matched (about 90%) and tested the classifier on trials where they mismatched. The classifier predicted top-down word class labels, and anti-correlated with bottom-up labels. Effects peaked [~]100ms after word onset over mid-frontal MEG sensors. Phonetic information was encoded in parallel, though peaking later ([~]200ms). Our results support that during continuous speech processing, lexical representations are quickly built in a context-sensitive manner. We showcase multivariate analyses for teasing apart subtle representational distinctions from neural time series.
]]></description>
<dc:creator>Gwilliams, L.</dc:creator>
<dc:creator>Marantz, A.</dc:creator>
<dc:creator>Poeppel, D.</dc:creator>
<dc:creator>King, J.-R.</dc:creator>
<dc:date>2022-05-31</dc:date>
<dc:identifier>doi:10.1101/2022.05.31.494173</dc:identifier>
<dc:title><![CDATA[Top-down information flow drives lexical access when listening to continuous speech]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.05.30.494046v1?rss=1">
<title>
<![CDATA[
Speech imagery decoding as a window to speech planning and production 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.05.30.494046v1?rss=1"
</link>
<description><![CDATA[
Speech imagery (the ability to generate internally quasi-perceptual experiences of speech) is a fundamental ability linked to cognitive functions such as inner speech, phonological working memory, and predictive processing. Speech imagery is also considered an ideal tool to test theories of overt speech. The study of speech imagery is challenging, primarily because of the absence of overt behavioral output as well as the difficulty in temporally aligning imagery events across trials and individuals. We used magnetoencephalography (MEG) paired with temporal-generalization-based neural decoding and a simple behavioral protocol to determine the processing stages underlying speech imagery. We monitored participants lip and jaw micromovements during mental imagery of syllable production using electromyography. Decoding participants imagined syllables revealed a sequence of task-elicited representations. Importantly, participants micromovements did not discriminate between syllables. The decoded sequence of neuronal patterns maps well onto the predictions of current computational models of overt speech motor control and provides evidence for hypothesized internal and external feedback loops for speech planning and production, respectively. Additionally, the results expose the compressed nature of representations during planning which contrasts with the natural rate at which internal productions unfold. We conjecture that the same sequence underlies the motor-based generation of sensory predictions that modulate speech perception as well as the hypothesized articulatory loop of phonological working memory. The results underscore the potential of speech imagery, based on new experimental approaches and analytical methods, and further pave the way for successful non-invasive brain-computer interfaces.
]]></description>
<dc:creator>Orpella, J.</dc:creator>
<dc:creator>Mantegna, F.</dc:creator>
<dc:creator>Assaneo, F.</dc:creator>
<dc:creator>Poeppel, D.</dc:creator>
<dc:date>2022-05-31</dc:date>
<dc:identifier>doi:10.1101/2022.05.30.494046</dc:identifier>
<dc:title><![CDATA[Speech imagery decoding as a window to speech planning and production]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-05-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.07.03.187260v1?rss=1">
<title>
<![CDATA[
Population-level differences in the neural substrates supporting Statistical Learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.07.03.187260v1?rss=1"
</link>
<description><![CDATA[
The ability to extract regularities from the environment is arguably an adaptive characteristic of intelligent systems. In the context of speech, statistical learning is thought to be an important mechanism for language acquisition. By considering individual differences in speech auditory-motor synchronization, an independent component analysis of fMRI data revealed that the neural substrates of statistical word form learning are not fully shared across individuals. While a network of auditory and superior pre/motor regions is universally activated in the process of learning, a fronto-parietal network is instead additionally and selectively engaged by some individuals, boosting their performance. Furthermore, interfering with the use of this network via articulatory suppression (producing irrelevant speech during learning) normalizes performance across the entire sample. Our work provides novel insights on language-related statistical learning and reconciles previous contrasting findings, while highlighting the need to factor in fundamental individual differences for a precise characterization of cognitive phenomena.
]]></description>
<dc:creator>Assaneo, M. F.</dc:creator>
<dc:creator>Orpella, J.</dc:creator>
<dc:creator>Ripolles, P.</dc:creator>
<dc:creator>Noejovich, L.</dc:creator>
<dc:creator>Lopez-Barroso, D.</dc:creator>
<dc:creator>de Diego Balaguer, R.</dc:creator>
<dc:creator>Poeppel, D.</dc:creator>
<dc:date>2020-07-04</dc:date>
<dc:identifier>doi:10.1101/2020.07.03.187260</dc:identifier>
<dc:title><![CDATA[Population-level differences in the neural substrates supporting Statistical Learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-07-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.10.08.463190v1?rss=1">
<title>
<![CDATA[
A bias generating temporal distortions in serial perception 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.10.08.463190v1?rss=1"
</link>
<description><![CDATA[
Precisely estimating event timing is essential for survival, yet temporal distortions are ubiquitous in our daily sensory experience. Here, we tested whether the relative position, relative duration and relative distance in time of two sequentially-organized events --standard S, with constant duration, and comparison C, varying trial-by-trial-- are causal factors in generating temporal distortions. We found that temporal distortions emerge when the first event is shorter than the second event. Importantly, a significant interaction suggests that a longer ISI helps counteracting such serial distortion effect only the constant S is in first position, but not if the unpredictable C is in first position. These results suggest the existence of a perceptual bias in perceiving ordered event durations, mechanistically contributing to distortion in time perception. We simulated our behavioral results with a Bayesian model and replicated the finding that participants disproportionately expand first-position dynamic (unpredictable) short events. Our results clarify the mechanics generating time distortions by identifying a hitherto unknown duration-dependent encoding inefficiency in human serial temporal perception, akin to a strong prior that can be overridden for highly predictable sensory events but unfolds for unpredictable ones.
]]></description>
<dc:creator>Sierra, F.</dc:creator>
<dc:creator>Poeppel, D.</dc:creator>
<dc:creator>Tavano, A.</dc:creator>
<dc:date>2021-10-09</dc:date>
<dc:identifier>doi:10.1101/2021.10.08.463190</dc:identifier>
<dc:title><![CDATA[A bias generating temporal distortions in serial perception]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-10-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.06.29.494500v1?rss=1">
<title>
<![CDATA[
Modelling the contributions to hyperexcitability in a mouse model of Alzheimer's disease 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.06.29.494500v1?rss=1"
</link>
<description><![CDATA[
Neuronal hyperexcitability is a feature of Alzheimers disease (AD). Three main mechanisms have been proposed to explain it: i), dendritic degeneration leading to increased input resistance, ii), ion channel changes leading to enhanced intrinsic excitability, and iii), synaptic changes leading to excitation-inhibition (E/I) imbalance. However, the relative contribution of these mechanisms is not fully understood. Therefore, we performed biophysically realistic multi-compartmental modelling of excitability in reconstructed CA1 pyramidal neurons of wild-type and APP/PS1 mice, a well-established animal model of AD. We show that, for synaptic activation, the excitability promoting effects of dendritic degeneration are cancelled out by excitability decreasing effects of synaptic loss. We find an interesting balance of excitability regulation with enhanced degeneration in the basal dendrites of APP/PS1 cells potentially leading to increased excitation by the apical but decreased excitation by the basal Schaffer collateral pathway. Furthermore, our simulations reveal that three additional pathomechanistic scenarios can account for the experimentally observed increase in firing and bursting of CA1 pyramidal neurons in APP/PS1 mice. Scenario 1: increased excitatory burst input; scenario 2: enhanced E/I ratio and scenario 3: alteration of intrinsic ion channels (IAHP down-regulated; INap, INa and ICaT up-regulated) in addition to enhanced E/I ratio. Our work supports the hypothesis that pathological network and ion channel changes are major contributors to neuronal hyperexcitability in AD. Overall, our results are in line with the concept of multi-causality and degeneracy according to which multiple different disruptions are separately sufficient but no single disruption is necessary for neuronal hyperexcitability.

In briefUsing a computational model, we find that changes in the extrinsic network and intrinsic biophysical neuronal properties rather than dendritic degeneration alone explain the altered firing behaviour observed in Alzheimers disease (AD).

HighlightsO_LISimulations of synaptically driven responses in PCs with AD-related dendritic degeneration.
C_LIO_LIDendritic degeneration alone alters PC responses to layer-specific input but additional pathomechanistic scenarios are required to explain neuronal hyperexcitability in AD.
C_LIO_LIPossible scenario 1: Burst hyperactivity of the surrounding network can explain hyper-excitability of PCs during AD.
C_LIO_LIPossible scenario 2: AD-related increased excitatory input together with decreased inhibitory input (E/I imbalance) can lead to hyperexcitability in PCs.
C_LIO_LIPossible scenario 3: Changes in E/I balance combined with altered ion channel properties can account for hyperexcitability in AD.
C_LI
]]></description>
<dc:creator>Mittag, M.</dc:creator>
<dc:creator>Mediavilla, L.</dc:creator>
<dc:creator>Remy, S.</dc:creator>
<dc:creator>Cuntz, H.</dc:creator>
<dc:creator>Jedlicka, P.</dc:creator>
<dc:date>2022-07-02</dc:date>
<dc:identifier>doi:10.1101/2022.06.29.494500</dc:identifier>
<dc:title><![CDATA[Modelling the contributions to hyperexcitability in a mouse model of Alzheimer's disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-07-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.04.442120v1?rss=1">
<title>
<![CDATA[
Biological complexity facilitates tuning of the neuronal parameter space 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.04.442120v1?rss=1"
</link>
<description><![CDATA[
The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown. Here, we generated large stochastic populations of biophysically realistic hippocampal granule cell models comparing those with all 15 ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were more frequent and more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve target excitability.

Significance statementOver the course of billions of years, evolution has led to a wide variety of biological systems. The emergence of the more complex among these seems surprising in the light of the high demands of searching for viable solutions in a correspondingly high-dimensional parameter space. In realistic neuron models with their inherently complex ion channel composition, we find a surprisingly large number of viable solutions when selecting parameters randomly. This effect is strongly reduced in models with fewer ion channel types but is recovered when inserting additional artificial ion channels. Because concepts from probability theory provide a plausible explanation for this improved distribution of valid model parameters, we propose that this may generalise to evolutionary selection in other complex biological systems.

In briefStudying ion channel diversity in neuronal models we show how robust biological systems may evolve not despite but because of their complexity.

HighlightsO_LI15 channel model of hippocampal granule cells (GCs) reduces to 5 ion channels without loss of spiking behaviour.
C_LIO_LIBut knocking out ion channels can be compensated only in the full model.
C_LIO_LIRandom sampling leads to ~ 6% solutions in full but only ~ 1% in reduced model.
C_LIO_LILaw of large numbers generalises our observations to other complex biological systems.
C_LI
]]></description>
<dc:creator>Schneider, M.</dc:creator>
<dc:creator>Gidon, A.</dc:creator>
<dc:creator>Triesch, J.</dc:creator>
<dc:creator>Jedlicka, P.</dc:creator>
<dc:creator>Cuntz, H.</dc:creator>
<dc:date>2021-05-04</dc:date>
<dc:identifier>doi:10.1101/2021.05.04.442120</dc:identifier>
<dc:title><![CDATA[Biological complexity facilitates tuning of the neuronal parameter space]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.08.02.501857v1?rss=1">
<title>
<![CDATA[
Global Motor Inhibition Precedes Stuttering Events 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.08.02.501857v1?rss=1"
</link>
<description><![CDATA[
Research points to neurofunctional differences underlying fluent speech between stutterers and non-stutterers. Considerably less work has focused on processes that underlie stuttered vs. fluent speech. Additionally, most of this research has focused on speech motor processes despite contributions from cognitive processes that occur prior to the onset of stuttered speech. We used MEG to test the hypothesis that reactive inhibitory control is triggered prior to stuttered speech. Twenty-nine stutterers completed a delayed-response task that featured a cue (prior to a go cue) signaling the imminent requirement to produce a word that was either stuttered or fluent. Consistent with our hypothesis, we observed increased beta power in the R-preSMA -an area implicated in reactive inhibitory control- in response to the cue preceding stuttered vs. fluent productions. Beta power differences between stuttered and fluent trials correlated with stuttering severity and participants percentage of trials stuttered increased exponentially with beta power in the R-preSMA. Trial-by-trial beta power modulations in the R-preSMA following the cue predicted whether a trial would be stuttered or fluent. Stuttered trials were also associated with delayed speech onset suggesting an overall slowing or freezing of the speech motor system that may be a consequence of inhibitory control. Post-hoc analyses revealed that independently-generated anticipated words were associated with greater beta power and more stuttering than researcher-assisted anticipated words, pointing to a relationship between self-perceived likelihood of stuttering (i.e., anticipation) and inhibitory control. This work offers a neurocognitive account of stuttering by characterizing the cognitive processes that precede overt stuttering events.
]]></description>
<dc:creator>Orpella, J.</dc:creator>
<dc:creator>Flick, G.</dc:creator>
<dc:creator>Assaneo, F.</dc:creator>
<dc:creator>Pylkkanen, L.</dc:creator>
<dc:creator>Poeppel, D.</dc:creator>
<dc:creator>Jackson, E.</dc:creator>
<dc:date>2022-08-03</dc:date>
<dc:identifier>doi:10.1101/2022.08.02.501857</dc:identifier>
<dc:title><![CDATA[Global Motor Inhibition Precedes Stuttering Events]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-08-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.09.26.509017v1?rss=1">
<title>
<![CDATA[
Perplexity about periodicity repeats perpetually: A response to Brookshire 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.09.26.509017v1?rss=1"
</link>
<description><![CDATA[
Brookshire (2022) claims that previous analyses of periodicity in detection performance after a reset event suffer from extreme false-positive rates. Here we show that this conclusion is based on an incorrect implemention of a null-hypothesis of aperiodicity, and that a correct implementation confirms low false-positive rates. Furthermore, we clarify that the previously used method of shuffling-in-time, and thereby shuffling-in-phase, cleanly implements the null hypothesis of no temporal structure after the reset, and thereby of no phase locking to the reset. Moving from a corresponding phase-locking spectrum to an inference on the periodicity of the underlying process can be accomplished by parameterizing the spectrum. This can separate periodic from non-periodic components, and quantify the strength of periodicity.
]]></description>
<dc:creator>Re, D.</dc:creator>
<dc:creator>Tosato, T.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Landau, A. N.</dc:creator>
<dc:date>2022-09-28</dc:date>
<dc:identifier>doi:10.1101/2022.09.26.509017</dc:identifier>
<dc:title><![CDATA[Perplexity about periodicity repeats perpetually: A response to Brookshire]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-09-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.14.512299v1?rss=1">
<title>
<![CDATA[
Tracking the online construction of linguistic meaning through negation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.14.512299v1?rss=1"
</link>
<description><![CDATA[
Combinatoric linguistic operations underpin human language processes, but how meaning is composed and refined in the mind of the reader is not well understood. We address this puzzle by exploiting the ubiquitous function of negation. We track the online effects of negation ("not") and intensifiers ("really") on the representation of scalar adjectives (e.g., "good") in parametrically designed behavioral and neurophysiological (MEG) experiments. The behavioral data show that participants first interpret negated adjectives as affirmative and later modify their interpretation towards, but never exactly as, the opposite meaning. Decoding analyses of neural activity further reveal significant above chance decoding accuracy for negated adjectives within 600 ms from adjective onset, suggesting that negation does not invert the representation of adjectives (i.e., "not bad" represented as "good"); furthermore, decoding accuracy for negated adjectives is found to be significantly lower than that for affirmative adjectives. Overall, these results suggest that negation mitigates rather than inverts the neural representations of adjectives. This putative suppression mechanism of negation is supported by increased synchronization of beta-band neural activity in sensorimotor areas. The analysis of negation provides a steppingstone to understand how the human brain represents changes of meaning over time.
]]></description>
<dc:creator>Zuanazzi, A.</dc:creator>
<dc:creator>Ripolles, P.</dc:creator>
<dc:creator>Lin, W. M.</dc:creator>
<dc:creator>Gwilliams, L.</dc:creator>
<dc:creator>King, J.-R.</dc:creator>
<dc:creator>Poeppel, D.</dc:creator>
<dc:date>2022-10-18</dc:date>
<dc:identifier>doi:10.1101/2022.10.14.512299</dc:identifier>
<dc:title><![CDATA[Tracking the online construction of linguistic meaning through negation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.31.514570v1?rss=1">
<title>
<![CDATA[
Phase leads between oscillatory visual stimuli induce a salience illusion 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.31.514570v1?rss=1"
</link>
<description><![CDATA[
Neuronal populations often engage in oscillations, and corresponding theories and computational models propose that this affects their impact on other neurons. Specifically, when two neuronal populations oscillating at similar frequencies compete for impact, the phase-leading one might have an advantage by providing inputs to target neurons earlier. Here, we provide direct empirical support by driving visual neuronal responses with oscillating stimuli. We superimposed two orthogonal grating stimuli of temporally oscillating intensities, whose phase relations were precisely controlled. The leading stimulus was perceived as oscillating more intensely. This held for phase leads of a few milliseconds, and for each participant and almost every trial. Thus, we found a strong perceptual illusion directly predicted by theories on the functional role of oscillations for neuronal interactions.
]]></description>
<dc:creator>Stauch, B. J.</dc:creator>
<dc:creator>Psarou, E.</dc:creator>
<dc:creator>Roese, R.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2022-10-31</dc:date>
<dc:identifier>doi:10.1101/2022.10.31.514570</dc:identifier>
<dc:title><![CDATA[Phase leads between oscillatory visual stimuli induce a salience illusion]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.10.31.466667v1?rss=1">
<title>
<![CDATA[
Sequence anticipation and STDP emerge from a voltage-based predictive learning rule 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.10.31.466667v1?rss=1"
</link>
<description><![CDATA[
Intelligent behavior depends on the brains ability to anticipate future events. However, the learning rules that enable neurons to predict and fire ahead of sensory inputs remain largely unknown. We propose a plasticity rule based on predictive processing, where the neuron learns a low-rank model of the synaptic input dynamics in its membrane potential. Neurons thereby amplify those synapses that maximally predict other synaptic inputs based on their temporal relations, which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local information. Consequently, neurons learn sequences over long timescales and shift their spikes towards the first inputs in a sequence. We show that this mechanism can explain the development of anticipatory signalling and recall in a recurrent network. Furthermore, we demonstrate that the learning rule gives rise to several experimentally observed STDP (spike-timing-dependent plasticity) mechanisms. These findings suggest prediction as a guiding principle to orchestrate learning and synaptic plasticity in single neurons.
]]></description>
<dc:creator>Saponati, M.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2021-11-03</dc:date>
<dc:identifier>doi:10.1101/2021.10.31.466667</dc:identifier>
<dc:title><![CDATA[Sequence anticipation and STDP emerge from a voltage-based predictive learning rule]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-11-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.04.515185v1?rss=1">
<title>
<![CDATA[
Distinct feedforward and feedback pathways for cell-type specific attention effects in macaque V4. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.04.515185v1?rss=1"
</link>
<description><![CDATA[
Spatial attention selectively enhances neural responses to visual stimuli. There are two long-standing hypotheses about how top-down feedback enhances sensory responses in areas like V4: First, by amplifying V1-to-V4 feedforward communication via 30-80Hz gamma-coherence. Second, via top-down feedback to V4 supra- and infra-granular layers. To test these hypotheses, we recorded distinct cell-types across macaque V1 and V4 layers. Attention increased both V1-V4 gamma-coherence and V4 spike-rates, yet with distinct laminar and cell-type profiles. Surprisingly, V1 gamma did not engage V4 excitatory neurons, but only Layer-4 fast-spiking interneurons. Similar observations were made in mouse visual-cortex, where feedforward gamma-influences preferentially recruit optogenetically-tagged PV+ and narrowwaveform SSt+ interneurons. By contrast, attention enhanced V4 spike-rates in both excitatory neurons and fast-spiking interneurons, with the strongest and earliest modulation in Layer-2/3, consistent with a feedback influence. These findings reveal distinct feedforward and feedback pathways for the attentional modulation of inter-areal coherence and spike rates, respectively.
]]></description>
<dc:creator>Spyropoulos, G.</dc:creator>
<dc:creator>Schneider, M.</dc:creator>
<dc:creator>van Kempen, J.</dc:creator>
<dc:creator>Gieselmann, M. A.</dc:creator>
<dc:creator>Thiele, A.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2022-11-04</dc:date>
<dc:identifier>doi:10.1101/2022.11.04.515185</dc:identifier>
<dc:title><![CDATA[Distinct feedforward and feedback pathways for cell-type specific attention effects in macaque V4.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.04.515143v1?rss=1">
<title>
<![CDATA[
Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.04.515143v1?rss=1"
</link>
<description><![CDATA[
Pattern separation is a valuable computational function performed by neuronal circuits, such as the dentate gyrus, where dissimilarity between inputs is increased, reducing noise and increasing the storage capacity of downstream networks. Pattern separation is studied from both in vivo experimental and computational perspectives and, a number of different measures (such as orthogonalisation, decorrelation, or spike train distance) have been applied to quantify the process of pattern separation. However, these are known to give conclusions that can differ qualitatively depending on the choice of measure and the parameters used to calculate it. We here demonstrate that arbitrarily increasing sparsity, a noticeable feature of dentate granule cell firing and one that is believed to be key to pattern separation, typically leads to improved classical measures for pattern separation even, inappropriately, up to the point where almost all information about the inputs is lost. Standard measures therefore both cannot differentiate between pattern separation and pattern destruction, and give results that may depend on arbitrary parameter choices. We propose that techniques from information theory, in particular mutual information, transfer entropy, and redundancy, should be applied to penalise the potential for lost information (often due to increased sparsity) that is neglected by existing measures. We compare five commonly-used measures of pattern separation with three novel techniques based on information theory, showing that the latter can be applied in a principled way and provide a robust and reliable measure for comparing the pattern separation performance of different neurons and networks. We demonstrate our new measures on detailed compartmental models of individual dentate granule cells and a dentate microcircuit, and show how structural changes associated with epilepsy affect pattern separation performance. We also demonstrate how our measures of pattern separation can predict pattern completion accuracy. Overall, our measures solve a widely acknowledged problem in assessing the pattern separation of neural circuits such as the dentate gyrus, as well as the cerebellum and mushroom body. Finally we provide a publicly available toolbox allowing for easy analysis of pattern separation in spike train ensembles.

Author summaryThe hippocampus is a region of the brain strongly associated with spatial navigation and encoding of episodic memories. To perform these functions effectively it makes use of circuits that perform pattern separation, where redundant structure is removed from neural representations leaving only the most salient information. Pattern separation allows downstream pattern completion networks to better distinguish between similar situations. Pathological changes, caused by Alzheimers, schizophrenia, or epilepsy, to the circuits that perform pattern separation are associated with reduced discriminative ability in both animal models and humans. Traditionally, pattern separation has been described alongside the complementary process of pattern completion, but more recent studies have focussed on the detailed neuronal and circuit features that contribute to pattern separation alone. We here show that traditional measures of pattern separation are inappropriate in this case, as they do not give consistent conclusions when parameters are changed and can confound pattern separation with the loss of important information. We show that directly accounting for the information throughput of a pattern separation circuit can provide new measures of pattern separation that are robust and consistent, and allow for nuanced analysis of the structure-function relationship of such circuits and how this may be perturbed by pathology.
]]></description>
<dc:creator>Bird, A. D.</dc:creator>
<dc:creator>Cuntz, H.</dc:creator>
<dc:creator>Jedlicka, P.</dc:creator>
<dc:date>2022-11-04</dc:date>
<dc:identifier>doi:10.1101/2022.11.04.515143</dc:identifier>
<dc:title><![CDATA[Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.09.515849v1?rss=1">
<title>
<![CDATA[
Modular, cement-free, customized headpost and connector-chamber implants for macaques 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.09.515849v1?rss=1"
</link>
<description><![CDATA[
BackgroundNeurophysiological studies with awake macaques typically require chronic cranial implants. Headpost and connector-chamber implants are used to allow head stabilization and to house connectors of chronically implanted electrodes, respectively.

New MethodWe present long- lasting, modular, cement-free headpost implants made of titanium that consist of two pieces: a baseplate and a top part. The baseplate is implanted first, covered by muscle and skin and allowed to heal and osseointegrate for several weeks to months. The percutaneous part is added in a second, brief surgery. Using a punch tool, a perfectly round skin cut is achieved providing a tight fit around the implant without any sutures. We describe the design, planning and production of manually bent and CNC-milled baseplates. We also developed a remote headposting technique that increases handling safety. Finally, we present a modular, footless connector chamber that is implanted in a similar two- step approach and achieves a minimized footprint on the skull.

ResultsTwelve adult male macaques were successfully implanted with a headpost and one with the connector chamber. To date, we report no implant failure, great headpost stability and implant condition, in four cases even more than 9 years post-implantation.

Comparison with Existing MethodsThe methods presented here build on several related previous methods and provide additional refinements to further increase implant longevity and handling safety.

ConclusionsOptimized implants can remain stable and healthy for at least 9 years and thereby exceed the typical experiment durations. This minimizes implant-related complications and corrective surgeries and thereby significantly improves animal welfare.

HighlightsO_LILong-lasting titanium implants for non-human primates
C_LIO_LIRefined implantation techniques that reduce post-operative complications
C_LIO_LIMinimized, footless connector chamber to house connectors of chronic arrays
C_LIO_LITwelve adult male macaques were implanted with long-lasting headpost implants
C_LIO_LIHeadpost implants so far without failure and with longevity up to > 9 years
C_LI
]]></description>
<dc:creator>Psarou, E.</dc:creator>
<dc:creator>Vezoli, J.</dc:creator>
<dc:creator>Schölvinck, M. L.</dc:creator>
<dc:creator>Ferracci, P.-A.</dc:creator>
<dc:creator>Zhang, Y.</dc:creator>
<dc:creator>Grothe, I.</dc:creator>
<dc:creator>Roese, R.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2022-11-09</dc:date>
<dc:identifier>doi:10.1101/2022.11.09.515849</dc:identifier>
<dc:title><![CDATA[Modular, cement-free, customized headpost and connector-chamber implants for macaques]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.29.518360v1?rss=1">
<title>
<![CDATA[
A biology-inspired recurrent oscillator network for computations in high-dimensional state space 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.29.518360v1?rss=1"
</link>
<description><![CDATA[
The dynamics of neuronal systems are characterized by hallmark features such as oscillations and synchrony. However, it has remained unclear whether these characteristics are epiphenomena or are exploited for computation. Due to the challenge of specifically interfering with oscillatory network dynamics in neuronal systems, we simulated recurrent networks (RNNs) of damped harmonic oscillators in which oscillatory activity is enforced in each node, a choice well-supported by experimental findings. When trained on standard pattern recognition tasks, these harmonic oscillator networks (HORNs) outperformed non-oscillatory architectures with respect to learning speed, noise tolerance, and parameter efficiency. HORNs also reproduced a substantial number of characteristic features of neuronal systems such as the cerebral cortex and the hippocampus. In trained HORNs, stimulus-induced interference patterns holistically represent the result of comparing sensory evidence with priors stored in recurrent connection weights, and learning-induced weight changes are compatible with Hebbian principles. Implementing additional features characteristic of natural networks, such as heterogeneous oscillation frequencies, inhomogeneous conduction delays, and network modularity, further enhanced HORN performance without requiring additional parameters. Taken together, our model allows us to give plausible a posteriori explanations for features of natural networks whose computational role has remained elusive. We conclude that neuronal systems are likely to exploit the unique dynamics of recurrent oscillators networks whose computational superiority critically depends on the oscillatory patterning of their nodal dynamics. Implementing the proposed computational principles in analog hardware is expected to enable the design of highly energy-efficient and self-adapting devices that could ideally complement existing digital technologies.
]]></description>
<dc:creator>Effenberger, F.</dc:creator>
<dc:creator>Carvalho, P.</dc:creator>
<dc:creator>Dubinin, I.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:date>2022-11-29</dc:date>
<dc:identifier>doi:10.1101/2022.11.29.518360</dc:identifier>
<dc:title><![CDATA[A biology-inspired recurrent oscillator network for computations in high-dimensional state space]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.28.518242v1?rss=1">
<title>
<![CDATA[
Performance modulations phase-locked to action depend on internal state 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.28.518242v1?rss=1"
</link>
<description><![CDATA[
Several studies have probed perceptual performance at different times after a self-paced motor action and found frequency-specific modulations of perceptual performance phase-locked to the action. Such action-related modulation has been reported for various frequencies and modulation strengths. In an attempt to establish a basic effect at the population level, we had a relatively large number of participants (n=50) perform a self-paced button press followed by a detection task at threshold, and we applied both fixed- and random-effects tests. The combined data of all trials and participants surprisingly did not show any significant action-related modulation. However, based on previous studies, we explored the possibility that such modulation depends on the participants internal state. Indeed, when we split trials based on performance in neighboring trials, then trials in periods of low performance showed an action-related modulation at {approx}17 Hz. When we split trials based on the performance in the preceding trial, we found that trials following a "miss" showed an action-related modulation at {approx}17 Hz. Finally, when we split participants based on their false-alarm rate, we found that participants with no false alarms showed an action-related modulation at {approx}17 Hz. All these effects were significant in random-effects tests, supporting an inference on the population. Together, these findings indicate that action-related modulations are not always detectable. However, the results suggest that specific internal states such as lower attentional engagement and/or higher decision criterion are characterized by a modulation in the beta-frequency range.
]]></description>
<dc:creator>Tosato, T.</dc:creator>
<dc:creator>Rohenkohl, G.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2022-11-29</dc:date>
<dc:identifier>doi:10.1101/2022.11.28.518242</dc:identifier>
<dc:title><![CDATA[Performance modulations phase-locked to action depend on internal state]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.06.03.131573v1?rss=1">
<title>
<![CDATA[
A geometry of spike sequences: Fast, unsupervised discovery of high-dimensional neural spiking patterns based on optimal transport theory 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.06.03.131573v1?rss=1"
</link>
<description><![CDATA[
Neural coding and memory formation depend on temporal spiking sequences that span high-dimensional neural ensembles. The unsupervised discovery and characterization of these spiking sequences requires a suitable dissimilarity measure to spiking patterns, which can then be used for clustering and decoding. Here, we present a new dissimilarity measure based on optimal transport theory called SpikeShip, which compares multineuron spiking patterns based on all the relative spike-timing relationships among neurons. SpikeShip computes the optimal transport cost to make all the relative spike-timing relationships (across neurons) identical between two spiking patterns. We show that this transport cost can be decomposed into a temporal rigid translation term, which captures global latency shifts, and a vector of neuron-specific transport flows, which reflect inter-neuronal spike timing differences. SpikeShip can be effectively computed for high-dimensional neuronal ensembles, has a low (linear) computational cost that has the same order as the spike count, and is sensitive to higher-order correlations. Furthermore SpikeShip is binless, can handle any form of spike time distributions, is not affected by firing rate fluctuations, can detect patterns with a low signal-to-noise ratio, and can be effectively combined with a sliding window approach. We compare the advantages and differences between SpikeShip and other measures like SPIKE and Victor-Purpura distance. We applied SpikeShip to large-scale Neuropixel recordings during spontaneous activity and visual encoding. We show that high-dimensional spiking sequences detected via SpikeShip reliably distinguish between different natural images and different behavioral states. These spiking sequences carried complementary information to conventional firing rate codes. SpikeShip opens new avenues for studying neural coding and memory consolidation by rapid and unsupervised detection of temporal spiking patterns in high-dimensional neural ensembles.
]]></description>
<dc:creator>Sotomayor-Gomez, B.</dc:creator>
<dc:creator>Battaglia, F. P.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2020-06-04</dc:date>
<dc:identifier>doi:10.1101/2020.06.03.131573</dc:identifier>
<dc:title><![CDATA[A geometry of spike sequences: Fast, unsupervised discovery of high-dimensional neural spiking patterns based on optimal transport theory]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-06-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.12.13.519734v1?rss=1">
<title>
<![CDATA[
Predictive coding during action observation - an Electrocorticographic study 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.12.13.519734v1?rss=1"
</link>
<description><![CDATA[
Predictive coding is a theoretical framework that has received much attention for its ability to generate testable hypotheses on how multiple brain regions integrate information during cognitive functions. Given relatively large sensorimotor delays, during social interactions, predicting the behavior of others is crucial to enable joint actions or provide competitive advantages. The action observation network (AON) has been extensively studied, but how information is integrated across its main nodes remains poorly understood. Here we leverage the high spatial and temporal resolution of intracranial Electrocorticography (ECoG), to characterize how the key nodes of the AON - including precentral, supramarginal and visual areas - exchange information. We found more top-down beta oscillation from precentral to supramarginal contacts during the observation of predictable actions while more bottom-up gamma oscillation from visual to supramarginal contacts were measured for unpredictable actions. These results, in line with predictive coding, provide critical evidence towards an understanding of how nodes of the AON integrate information to process the actions of others.
]]></description>
<dc:creator>Qin, C.</dc:creator>
<dc:creator>Michon, F.</dc:creator>
<dc:creator>Onuki, Y.</dc:creator>
<dc:creator>Ishishita, Y.</dc:creator>
<dc:creator>Otani, K.</dc:creator>
<dc:creator>Kawai, K.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Gazzola, V.</dc:creator>
<dc:creator>Keysers, C.</dc:creator>
<dc:date>2022-12-13</dc:date>
<dc:identifier>doi:10.1101/2022.12.13.519734</dc:identifier>
<dc:title><![CDATA[Predictive coding during action observation - an Electrocorticographic study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-12-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.01.12.523735v1?rss=1">
<title>
<![CDATA[
Distributed representations of prediction error signals across the cortical hierarchy are synergistic 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.01.12.523735v1?rss=1"
</link>
<description><![CDATA[
An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. brain signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded redundant and synergistic information during auditory prediction error processing. In both tasks, we observed multiple patterns of synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between auditory and frontal regions. Using a brain-constrained neural network, we simulated the spatio-temporal patterns of synergy and redundancy observed in the experimental results and further demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback and feedforward connections. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.
]]></description>
<dc:creator>Gelens, F.</dc:creator>
<dc:creator>Komatsu, M.</dc:creator>
<dc:creator>Uran, C.</dc:creator>
<dc:creator>Jensen, M. A.</dc:creator>
<dc:creator>Miller, K. J.</dc:creator>
<dc:creator>Ince, R. A. A.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:creator>Canales-Johnson, A.</dc:creator>
<dc:date>2023-01-13</dc:date>
<dc:identifier>doi:10.1101/2023.01.12.523735</dc:identifier>
<dc:title><![CDATA[Distributed representations of prediction error signals across the cortical hierarchy are synergistic]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-01-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.01.04.522738v1?rss=1">
<title>
<![CDATA[
Cell-type-specific propagation of visual flicker 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.01.04.522738v1?rss=1"
</link>
<description><![CDATA[
Rhythmic flicker stimulation has gained interest as a treatment for neurodegenerative diseases and a method for frequency tagging neural activity in human EEG/MEG recordings. Yet, little is known about the way in which flicker-induced synchronization propagates across cortical levels and impacts different cell types. Here, we used Neuropixels to simultaneously record from LGN, V1, and CA1 while presenting visual flicker stimuli at different frequencies. LGN neurons showed strong phase locking up to 40Hz, whereas phase locking was substantially weaker in V1 units and absent in CA1 units. Laminar analyses revealed an attenuation of phase locking at 40Hz for each processing stage, with substantially weaker phase locking in the superficial layers of V1. Gamma-rhythmic flicker predominantly entrained fast-spiking interneurons. Optotagging experiments showed that these neurons correspond to either PV+ or narrow-waveform Sst+ neurons. A computational model could explain the observed differences in phase locking based on the neurons capacitative low-pass filtering properties. In summary, the propagation of synchronized activity and its effect on distinct cell types strongly depend on its frequency.
]]></description>
<dc:creator>Schneider, M.</dc:creator>
<dc:creator>Tzanou, A.</dc:creator>
<dc:creator>Uran, C.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2023-01-04</dc:date>
<dc:identifier>doi:10.1101/2023.01.04.522738</dc:identifier>
<dc:title><![CDATA[Cell-type-specific propagation of visual flicker]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-01-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.06.531093v1?rss=1">
<title>
<![CDATA[
Enhanced Behavioral Performance through Interareal Gamma and Beta Synchronization 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.06.531093v1?rss=1"
</link>
<description><![CDATA[
Cognitive functioning requires coordination between brain areas. Between visual areas, feedforward gamma synchronization improves behavioral performance. Here, we investigate whether similar principles hold across brain regions and frequency bands, using simultaneous local field potential recordings from 15 areas during performance of a selective attention task. Short behavioral reaction times (RTs), an index of efficient interareal communication, occurred when occipital areas V1, V2, V4, DP showed gamma synchronization, and fronto-central areas S1, 5, F1, F2, F4 showed beta synchronization. For both area clusters and corresponding frequency bands, deviations from the typically observed phase relations increased RTs. Across clusters and frequency bands, good phase relations occurred in a correlated manner specifically when they processed the behaviorally relevant stimulus. Furthermore, the fronto- central cluster exerted a beta-band influence onto the occipital cluster whose strength predicted short RTs. These results suggest that local gamma and beta synchronization and their inter-regional coordination jointly improve behavioral performance.
]]></description>
<dc:creator>Parto-Dezfouli, M.</dc:creator>
<dc:creator>Vezoli, J.</dc:creator>
<dc:creator>Bosman, C. A.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2023-03-06</dc:date>
<dc:identifier>doi:10.1101/2023.03.06.531093</dc:identifier>
<dc:title><![CDATA[Enhanced Behavioral Performance through Interareal Gamma and Beta Synchronization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.15.532740v1?rss=1">
<title>
<![CDATA[
Skewed distribution of spines is independent of presynaptic transmitter release and synaptic plasticity and emerges early during adult neurogenesis 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.15.532740v1?rss=1"
</link>
<description><![CDATA[
Dendritic spines are crucial for excitatory synaptic transmission as the size of a spine head correlates with the strength of its synapse. The distribution of spine head sizes follows a lognormal-like distribution with more small spines than large ones. We analysed the impact of synaptic activity and plasticity on the spine size distribution in adult-born hippocampal granule cells from rats with induced homo- and heterosynaptic long-term plasticity in vivo and CA1 pyramidal cells from Munc-13-1-Munc13-2 knockout mice with completely blocked synaptic transmission. Neither induction of extrinsic synaptic plasticity nor the blockage of presynaptic activity degrades the lognormal-like distribution but changes its mean, variance and skewness. The skewed distribution develops early in the life of the neuron. Our findings and their computational modelling support the idea that intrinsic synaptic plasticity is sufficient for the generation, while a combination of intrinsic and extrinsic synaptic plasticity maintains lognormal like distribution of spines.
]]></description>
<dc:creator>Roessler, N.</dc:creator>
<dc:creator>Jungenitz, T.</dc:creator>
<dc:creator>Sigler, A.</dc:creator>
<dc:creator>Bird, A.</dc:creator>
<dc:creator>Mittag, M.</dc:creator>
<dc:creator>Rhee, J.</dc:creator>
<dc:creator>Deller, T.</dc:creator>
<dc:creator>Cuntz, H. J.</dc:creator>
<dc:creator>Brose, N.</dc:creator>
<dc:creator>Schwarzacher, S.</dc:creator>
<dc:creator>Jedlicka, P.</dc:creator>
<dc:date>2023-03-15</dc:date>
<dc:identifier>doi:10.1101/2023.03.15.532740</dc:identifier>
<dc:title><![CDATA[Skewed distribution of spines is independent of presynaptic transmitter release and synaptic plasticity and emerges early during adult neurogenesis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.21.533636v1?rss=1">
<title>
<![CDATA[
Paying attention to natural scenes in area V1 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.21.533636v1?rss=1"
</link>
<description><![CDATA[
Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. Here, we hypothesized that attentional and contextual top-down signals interact in V1, in a manner that primarily benefits the representation of natural visual stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we found that attention enhanced the decodability of stimulus identity from population responses evoked by natural scenes but, critically, not by synthetic stimuli in which higher-order statistical regularities were eliminated. Population analysis revealed that neuronal responses converged to a low dimensional subspace for natural but not for synthetic images. Critically, we determined that the attentional enhancement in stimulus decodability was captured by the dominant low dimensional subspace, suggesting an alignment between the attentional and natural stimulus variance. The alignment was pronounced for late evoked responses but not for early transient responses of V1 neurons, supporting the notion that top-down feedback was required. We argue that attention and perception share top-down pathways, which mediate hierarchical interactions optimized for natural vision.
]]></description>
<dc:creator>Lazar, A.</dc:creator>
<dc:creator>Klein, L.</dc:creator>
<dc:creator>Klon-Lipok, J.</dc:creator>
<dc:creator>Banyai, M.</dc:creator>
<dc:creator>Orban, G.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:date>2023-03-21</dc:date>
<dc:identifier>doi:10.1101/2023.03.21.533636</dc:identifier>
<dc:title><![CDATA[Paying attention to natural scenes in area V1]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.04.10.536138v1?rss=1">
<title>
<![CDATA[
Optogenetic stimulation reveals frequency-dependent resonance and encoding in V1 excitatory and inhibitory neurons 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.04.10.536138v1?rss=1"
</link>
<description><![CDATA[
Cortical information processing is thought to be facilitated by the resonant properties of individual neurons and neuronal networks, which selectively amplify inputs at specific frequencies. We used optogenetics to test how different input frequencies are encoded by excitatory cells and parvalbumin-expressing (PV) interneurons in mouse V1. Spike phase-locking and power increased with frequency, reaching a broad peak around 80-100Hz. This effect was observed only for Chronos, a fast-kinetic opsin, but not for Channelrhodopsin-2. Surprisingly, neurons did not exhibit narrow-band resonance in specific frequency-ranges, and showed reliably phase-locking up to 140Hz. Strong phase-locking at high frequencies reflected non-linear input/output transformations, with neurons firing only in a narrow part of the cycle. By contrast, low-frequency inputs were encoded in a more continuous manner. Correspondingly, spectral coherence and firing rates showed little dependence on frequency and did not reflect transferred power. To investigate whether strong phase-locking facilitated the reliable encoding of inputs, we analyzed various spike-train distances and Fano factor. Interestingly, responses to lower rather than higher frequencies had more globally reliable spike-counts and timing structure. These findings have various practical implications for understanding the effects of optogenetic stimulation and choice of opsin. Furthermore, they show both PV and excitatory neurons respond with more local precision, i.e. phase-locking, to high-frequency inputs, but have more globally reliable responses to low-frequency inputs, suggesting differential coding regimes for these frequencies.
]]></description>
<dc:creator>Broggini, A. C.</dc:creator>
<dc:creator>Onorato, I.</dc:creator>
<dc:creator>Tzanou, A.</dc:creator>
<dc:creator>Sotomayor, B.</dc:creator>
<dc:creator>Uran, C.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2023-04-11</dc:date>
<dc:identifier>doi:10.1101/2023.04.10.536138</dc:identifier>
<dc:title><![CDATA[Optogenetic stimulation reveals frequency-dependent resonance and encoding in V1 excitatory and inhibitory neurons]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-04-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.04.08.535291v1?rss=1">
<title>
<![CDATA[
Distinct roles of PV and Sst interneurons in visually-induced gamma oscillations 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.04.08.535291v1?rss=1"
</link>
<description><![CDATA[
Sensory processing relies on interactions between excitatory and inhibitory neurons, which are often coordinated by 30-80Hz gamma oscillations. However, the specific contributions of distinct interneurons to gamma synchronization remain unclear. We performed high-density recordings from V1 in awake mice and used optogenetics to identify PV+ (Parvalbumin) and Sst+ (Somatostatin) interneurons. PV interneurons were highly phase-locked to visually-induced gamma oscillations. Sst cells were heterogeneous, with only a subset of narrow-waveform cells showing strong gamma phase-locking. Interestingly, PV interneurons consistently fired at an earlier phase in the gamma cycle ({approx}6ms or 60 degrees) than Sst interneurons. Consequently, PV and Sst activity showed differential temporal relations with excitatory cells. In particular, the 1st and 2nd spikes in burst events, which were strongly gamma phase-locked, shortly preceded PV and Sst activity, respectively. These findings indicate a primary role of PV interneurons in synchronizing excitatory cells and suggest that PV and Sst interneurons control the excitability of somatic and dendritic neural compartments with precise time delays coordinated by gamma oscillations.
]]></description>
<dc:creator>Onorato, I.</dc:creator>
<dc:creator>Tzanou, A.</dc:creator>
<dc:creator>Schneider, M.</dc:creator>
<dc:creator>Uran, C.</dc:creator>
<dc:creator>Broggini, A. C.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2023-04-09</dc:date>
<dc:identifier>doi:10.1101/2023.04.08.535291</dc:identifier>
<dc:title><![CDATA[Distinct roles of PV and Sst interneurons in visually-induced gamma oscillations]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-04-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.05.09.539989v1?rss=1">
<title>
<![CDATA[
Flexible control of vocal timing in bats enables escape from acoustic interference 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.05.09.539989v1?rss=1"
</link>
<description><![CDATA[
In natural environments, background noise can degrade the integrity of acoustic signals, posing a problem for animals that rely on their vocalizations for communication and navigation. A simple behavioral strategy to combat acoustic interference would be to restrict call emissions to periods of low-amplitude or no noise. Using audio playback and computational tools for the automated detection of over 2.5 million vocalizations from groups of freely vocalizing bats, we show that bats (Carollia perspicillata) can dynamically adapt the timing of their calls to avoid acoustic jamming in both predictably and unpredictably patterned noise. This study demonstrates that bats spontaneously seek out temporal windows of opportunity for vocalizing in acoustically crowded environments, providing a mechanism for efficient echolocation and communication in cluttered acoustic landscapes.

One Sentence SummaryBats avoid acoustic interference by rapidly adjusting the timing of vocalizations to the temporal pattern of varying noise.
]]></description>
<dc:creator>Kiai, A.</dc:creator>
<dc:creator>Clemens, J.</dc:creator>
<dc:creator>Kössl, M.</dc:creator>
<dc:creator>Poeppel, D.</dc:creator>
<dc:creator>Hechavarria, J. C.</dc:creator>
<dc:date>2023-05-09</dc:date>
<dc:identifier>doi:10.1101/2023.05.09.539989</dc:identifier>
<dc:title><![CDATA[Flexible control of vocal timing in bats enables escape from acoustic interference]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-05-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.07.03.547519v1?rss=1">
<title>
<![CDATA[
Oscillatory waveform shape and temporal spike correlations differ across bat frontal and auditory cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.07.03.547519v1?rss=1"
</link>
<description><![CDATA[
Neural oscillations are associated with diverse computations in the mammalian brain. The waveform shape of oscillatory activity measured in cortex relates to local physiology, and can be informative about aberrant or dynamically changing states. However, how waveform shape differs across distant yet functionally and anatomically related cortical regions is largely unknown. In this study, we capitalize on simultaneous recordings of local field potentials (LFPs) in the auditory and frontal cortices of awake, male Carollia perspicillata bats to examine, on a cycle-by-cycle basis, waveform shape differences across cortical regions. We find that waveform shape differs markedly in the fronto-auditory circuit even for temporally correlated rhythmic activity in comparable frequency ranges (i.e. in the delta and gamma bands) during spontaneous activity. In addition, we report consistent differences between areas in the variability of waveform shape across individual cycles. A conceptual model predicts higher spike-spike and spike-LFP correlations in regions with more asymmetric shape, a phenomenon that was observed in the data: spike-spike and spike-LFP correlations were higher in frontal cortex. The model suggests a relationship between waveform shape differences and differences in spike correlations across cortical areas. Altogether, these results indicate that oscillatory activity in frontal and auditory cortex possess distinct dynamics related to the anatomical and functional diversity of the fronto-auditory circuit.

Significance statementThe brain activity of many animals displays intricate oscillations, which are usually characterized in terms of their frequency and amplitude. Here, we study oscillations from the bat frontal and auditory cortices on a cycle-by-cycle basis, additionally focusing on their characteristic waveform shape. The study reveals clear differences across regions in waveform shape and oscillatory regularity, even when the frequency of the oscillations is similar. A conceptual model predicts that more asymmetric waveforms result from stronger correlations between neural spikes and electrical field activity. Such predictions were supported by the data. The findings shed light onto the unique properties of different cortical areas, providing key insights into the distinctive physiology and functional diversity within the fronto-auditory circuit.
]]></description>
<dc:creator>Garcia-Rosales, F.</dc:creator>
<dc:creator>Schaworonkow, N.</dc:creator>
<dc:creator>Hechavarria, J. C.</dc:creator>
<dc:date>2023-07-03</dc:date>
<dc:identifier>doi:10.1101/2023.07.03.547519</dc:identifier>
<dc:title><![CDATA[Oscillatory waveform shape and temporal spike correlations differ across bat frontal and auditory cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-07-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.05.18.541327v1?rss=1">
<title>
<![CDATA[
A gradual transition from veridical to categorical representations along the visual hierarchy during working memory, but not perception. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.05.18.541327v1?rss=1"
</link>
<description><![CDATA[
The ability to stably maintain visual information over brief delays is central to healthy cognitive functioning, as is the ability to differentiate such internal representations from external inputs. One possible way to achieve both is via multiple concurrent mnemonic representations along the visual hierarchy that differ systematically from the representations of perceptual inputs. To test this possibility, we examine orientation representations along the visual hierarchy during perception and working memory. Human participants directly viewed, or held in mind, oriented grating patterns, and the similarity between fMRI activation patterns for different orientations was calculated throughout retinotopic cortex. During direct viewing of grating stimuli, similarity was relatively evenly distributed amongst all orientations, while during working memory the similarity was higher around oblique orientations. We modeled these differences in representational geometry based on the known distribution of orientation information in the natural world: The "veridical" model uses an efficient coding framework to capture hypothesized representations during visual perception. The "categorical" model assumes that different "psychological distances" between orientations result in orientation categorization relative to cardinal axes. During direct perception, the veridical model explained the data well. During working memory, the categorical model gradually gained explanatory power over the veridical model for increasingly anterior retinotopic regions. Thus, directly viewed images are represented veridically, but once visual information is no longer tethered to the sensory world there is a gradual progression to more categorical mnemonic formats along the visual hierarchy.
]]></description>
<dc:creator>Chunharas, C.</dc:creator>
<dc:creator>Hettwer, M. D.</dc:creator>
<dc:creator>Wolff, M. J.</dc:creator>
<dc:creator>Rademaker, R. L.</dc:creator>
<dc:date>2023-05-18</dc:date>
<dc:identifier>doi:10.1101/2023.05.18.541327</dc:identifier>
<dc:title><![CDATA[A gradual transition from veridical to categorical representations along the visual hierarchy during working memory, but not perception.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-05-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.06.15.545190v1?rss=1">
<title>
<![CDATA[
A biologically inspired repair mechanism for neuronal reconstructions with a focus on human dendrites 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.06.15.545190v1?rss=1"
</link>
<description><![CDATA[
Investigating and modelling the functionality of human neurons remains challenging due to the technical limitations, resulting in scarce and incomplete 3D anatomical reconstructions. Here we used a morphological modelling approach based on optimal wiring to repair the parts of a dendritic morphology that were lost due to incomplete tissue samples. In Drosophila, where dendritic regrowth has been studied experimentally using laser ablation, we found that modelling the regrowth reproduced a bimodal distribution between regeneration of cut branches and invasion by neighbouring branches. Interestingly, our repair model followed growth rules similar to those for the generation of a new dendritic tree. To generalise the repair algorithm from Drosophila to mammalian neurons, we artificially sectioned reconstructed dendrites from mouse and human hippocampal pyramidal cell morphologies, and showed that the regrown dendrites were morphologically similar to the original ones. Furthermore, we were able to restore their electrophysiological functionality, as evidenced by the recovery of their firing behaviour. Importantly, we show that such repairs also apply to other neuron types including hippocampal granule cells and cerebellar Purkinje cells. We then extrapolated the repair to incomplete human CA1 pyramidal neurons, where the anatomical boundaries of the particular brain areas innervated by the neurons in question were known. Interestingly, the repair of incomplete human dendrites helped to simulate the recently observed increased synaptic thresholds for dendritic NMDA spikes in human versus mouse dendrites. To make the repair tool available to the neuroscience community, we have developed an intuitive and simple graphical user interface (GUI), which is available in the TREES Toolbox (www.treestoolbox.org).

In briefWe use morphological modelling inspired by the regeneration of various artificially cut neuron types and repair incomplete human and nonhuman neuronal dendritic reconstructions.

Author summaryReconstructing neuronal dendrites by drawing their 3D branching structures in the computer has proven to be crucial for interpreting the flow of electrical signals and therefore the computations that dendrites implement on their inputs. These reconstructions are tedious and prone to disruptive limitations imposed by experimental procedures. In recent years, complementary computational procedures have emerged that reproduce the fine details of morphology in theoretical models. These models allow, for example, to populate large-scale neural networks and to study structure-function relationships. In this work we use a morphological model based on optimised wiring for signal conduction and material cost to repair faulty reconstructions, in particular those of human hippocampal dendrites, which are rare and precious but often cut due to technical limitations. Interestingly, we find that our synthetic repair mechanism reproduces the two distinct modes of repair observed in real dendrites: regeneration from the severed branch and invasion from neighbouring branches. Our model therefore provides both a useful tool for single-cell electrophysiological simulations and a useful theoretical concept for studying the biology of dendrite repair.

HighlightsO_LIOptimal wiring-based growth algorithm replicates regrowth of artificially cut dendrites
C_LIO_LIThe growth algorithm repairs cut dendrites in incomplete reconstructions
C_LIO_LIThe algorithm works for diverse neuron types in multiple species
C_LIO_LIThe repair of morphology restores original electrophysiology
C_LIO_LIThe repair of morphology supports simulations of high synaptic thresholds for NMDA spikes in human dendrites
C_LIO_LIThe repair tool with user interface is available in the TREES Toolbox
C_LI
]]></description>
<dc:creator>Groden, M.</dc:creator>
<dc:creator>Moessinger, H. M.</dc:creator>
<dc:creator>Schaffran, B.</dc:creator>
<dc:creator>DeFelipe, J. D.</dc:creator>
<dc:creator>Benavides-Piccione, R.</dc:creator>
<dc:creator>Cuntz, H.</dc:creator>
<dc:creator>Jedlicka, P.</dc:creator>
<dc:date>2023-06-16</dc:date>
<dc:identifier>doi:10.1101/2023.06.15.545190</dc:identifier>
<dc:title><![CDATA[A biologically inspired repair mechanism for neuronal reconstructions with a focus on human dendrites]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-06-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.06.30.547194v1?rss=1">
<title>
<![CDATA[
In vivo magnetic recording of single-neuron action potentials 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.06.30.547194v1?rss=1"
</link>
<description><![CDATA[
Measuring fast neuronal signals is the domain of electrophysiology and magnetophysiology. While electrophysiology is easier to perform, magnetophysiology avoids tissue-based distortions and measures a signal with directional information. At the macroscale, magnetoencephalography (MEG) is established, and at the mesoscale, visually evoked magnetic fields have been reported. At the microscale however, while benefits of recording magnetic counterparts of electric spikes would be numerous, they are also highly challenging in vivo. Here, we combine magnetic and electric recordings of neuronal action potentials in anesthetized rats using miniaturized giant magneto-resistance (GMR) sensors. We reveal the magnetic signature of action potentials of well-isolated single units. The recorded magnetic signals showed a distinct waveform and considerable signal strength. This demonstration of in vivo magnetic action potentials opens a wide field of possibilities to profit from the combined power of magnetic and electric recordings and thus to significantly advance the understanding of neuronal circuits.

Significance statementElectrophysiological tools allow the measurement of single-neuron action potentials with high temporal resolution. Magnetophysiological measurements would add valuable information, but are particularly hard to achieve for single neurons. Established technology for non-invasive magnetic brain signal measurements can currently not be used inside living tissue. Here, we demonstrate that miniaturized magnetic sensors based on giant magneto-resistance enable the measurement of the magnetic counterpart of single-neuron action potentials in vivo. This proof-of-principle shows a way towards integrating magnetic and electric recordings in future experiments and thus to profit from the complementary information measured by the two modalities.
]]></description>
<dc:creator>Klein, F. J.</dc:creator>
<dc:creator>Jendritza, P.</dc:creator>
<dc:creator>Chopin, C.</dc:creator>
<dc:creator>Parto-Dezfouli, M.</dc:creator>
<dc:creator>Solignac, A.</dc:creator>
<dc:creator>Fermon, C.</dc:creator>
<dc:creator>Pannetier-Lecoeur, M.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2023-07-02</dc:date>
<dc:identifier>doi:10.1101/2023.06.30.547194</dc:identifier>
<dc:title><![CDATA[In vivo magnetic recording of single-neuron action potentials]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-07-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.08.552339v1?rss=1">
<title>
<![CDATA[
Eccentricity-Dependent Saccadic Reaction Time: The Roles of Foveal Magnification and Attentional Orienting 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.08.552339v1?rss=1"
</link>
<description><![CDATA[
A hallmark of primate vision is the emphasis on foveal processing, accompanied by frequent saccades that bring the fovea to salient parts of the scene, or to newly appearing stimuli. A saccade to a new stimulus is one of the most fundamental sensory-motor transformations. In macaque monkeys, we show that foveal magnification is not only the reason for saccades, but it also affects the dynamics of saccade initiation. In a task where the monkeys made saccades to peripheral target onsets, saccadic reaction time (SRT) increased with target eccentricity. Notably, we effectively eliminated this increase by scaling the target size according to the foveal magnification factor in the superior colliculus. We repeated the comparison between non-scaled and scaled targets, while changing the task to a delayed saccade task. In this task, the target was presented long before the saccade, and the saccade was triggered by foveal fixation offset rather than target onset, such that target onset long before fixation offset was essentially irrelevant for SRT. In this task, we found that SRT increased with target eccentricity, with similar rate for both non-scaled and scaled targets. Furthermore, this increase survived the addition of a salient distracting flash resetting attention to the foveal. The results obtained with the delayed saccades task are consistent with an attentional scan from the fovea to the target, a recently hypothesized general mechanism of attention.
]]></description>
<dc:creator>Zhang, Y.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2023-08-12</dc:date>
<dc:identifier>doi:10.1101/2023.08.08.552339</dc:identifier>
<dc:title><![CDATA[Eccentricity-Dependent Saccadic Reaction Time: The Roles of Foveal Magnification and Attentional Orienting]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.26.554928v1?rss=1">
<title>
<![CDATA[
Inhibitory feedback enables predictive learning of multiple sequences in neural networks 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.26.554928v1?rss=1"
</link>
<description><![CDATA[
Anticipating future events is a key computational task for neuronal networks. Experimental evidence suggests that reliable temporal sequences in neural activity play a functional role in the association and anticipation of events in time. However, how neurons can differentiate and anticipate multiple spike sequences remains largely unknown. We implement a learning rule based on predictive processing, where neurons exclusively fire for the initial, unpredictable inputs in a spiking sequence, leading to an efficient representation with reduced post-synaptic firing. Combining this mechanism with inhibitory feedback leads to sparse firing in the network, enabling neurons to selectively anticipate different sequences in the input. We demonstrate that intermediate levels of inhibition are optimal to decorrelate neuronal activity and to enable the prediction of future inputs. Notably, each sequence is independently encoded in the sparse, anticipatory firing of the network. Overall, our results demonstrate that the interplay of self-supervised predictive learning rules and inhibitory feedback enables fast and efficient classification of different input sequences.
]]></description>
<dc:creator>Saponati, M.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2023-08-27</dc:date>
<dc:identifier>doi:10.1101/2023.08.26.554928</dc:identifier>
<dc:title><![CDATA[Inhibitory feedback enables predictive learning of multiple sequences in neural networks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.06.27.546669v1?rss=1">
<title>
<![CDATA[
Differential population coding of natural movies through spike counts and temporal sequences 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.06.27.546669v1?rss=1"
</link>
<description><![CDATA[
Information in the nervous system is encoded by the spiking patterns of large populations of neurons. The analysis of such high-dimensional data is typically restricted to simple, arbitrarily defined features like spike rates, which discards information in the temporal structure of spike trains. Here, we use a recently developed method called SpikeShip based on optimal transport theory, which captures information from all of the relative spike-timing relations among neurons. We compared spike-rate and spike-timing codes in neural ensembles from six visual areas during natural video presentations. Temporal spiking sequences conveyed substantially more information about natural movies than population spike-rate vectors, especially for larger number of neurons. As previously, shown, population rate vectors exhibited substantial drift across repetitions and between blocks. Conversely, encoding through temporal sequences was stable over time, and did not show representational drift both within and between blocks. These findings reveal a purely spike-based neural code that is based on relative spike timing relations in neural ensembles alone.
]]></description>
<dc:creator>Sotomayor-Gomez, B.</dc:creator>
<dc:creator>Battaglia, F.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2023-06-29</dc:date>
<dc:identifier>doi:10.1101/2023.06.27.546669</dc:identifier>
<dc:title><![CDATA[Differential population coding of natural movies through spike counts and temporal sequences]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-06-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.27.559710v1?rss=1">
<title>
<![CDATA[
Primate Saccade Rhythmicity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.27.559710v1?rss=1"
</link>
<description><![CDATA[
Active sensing behaviors in rodents display theta (4-8 Hz) rhythmicity. Whether similar rhythmicity exists in primate saccadic eye movements has remained a matter of debate. We studied saccade dynamics in 22 human participants and two macaque monkeys, examining the influence of different visual stimuli and tasks. Inter-saccadic intervals (ISIs) reliably revealed a characteristic duration and under certain conditions clear theta rhythmicity. Rhythmicity was strongest for saccades with short ISIs. Surprisingly, the degree of rhythmicity was not due to spatial regularity of the visual scene, but it was shaped by task demands. Macro- and micro-saccade ISIs shared similar characteristic durations. Naturally occurring micro-saccades provided evidence that ISIs can become more regular without becoming faster. During free-viewing, subsequent ISIs show long-range correlation structure and the visual system switched between states of low and high rhythmicity. Humans and macaques showed similar saccade dynamics, suggesting a potential common evolutionary trait in primate active visual sensing.
]]></description>
<dc:creator>Näher, T.</dc:creator>
<dc:creator>Zhang, Y.</dc:creator>
<dc:creator>Pandinelli, M.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2023-09-27</dc:date>
<dc:identifier>doi:10.1101/2023.09.27.559710</dc:identifier>
<dc:title><![CDATA[Primate Saccade Rhythmicity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-09-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.06.565858v1?rss=1">
<title>
<![CDATA[
Dynamic Fading Memory and Expectancy Effects in Monkey Primary Visual Cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.06.565858v1?rss=1"
</link>
<description><![CDATA[
In order to investigate the involvement of primary visual cortex (V1) in working memory (WM), parallel, multisite recordings of multiunit activity were obtained from monkey V1 while the animals performed a delayed match-to-sample (DMS) task. During the delay period, V1 population firing rate vectors maintained a lingering trace of the sample stimulus that could be reactivated by intervening impulse stimuli that enhanced neuronal firing. This fading trace of the sample did not require active engagement of the monkeys in the DMS task and likely reflects the intrinsic dynamics of recurrent cortical networks in lower visual areas. This renders an active, attention-dependent involvement of V1 in the maintenance of working memory contents unlikely. By contrast, population responses to the test stimulus depended on the probabilistic contingencies between sample and test stimuli. Responses to tests that matched expectations were reduced which agrees with concepts of predictive coding.
]]></description>
<dc:creator>Yang, Y.</dc:creator>
<dc:creator>Klon-Lipok, J.</dc:creator>
<dc:creator>Shapcott, K.</dc:creator>
<dc:creator>Lazar, A.</dc:creator>
<dc:creator>Singer, W.</dc:creator>
<dc:date>2023-11-06</dc:date>
<dc:identifier>doi:10.1101/2023.11.06.565858</dc:identifier>
<dc:title><![CDATA[Dynamic Fading Memory and Expectancy Effects in Monkey Primary Visual Cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.01.24.577055v1?rss=1">
<title>
<![CDATA[
Thoughtful faces: inferring internal states across species using facial features 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.01.24.577055v1?rss=1"
</link>
<description><![CDATA[
Animal behaviour is shaped to a large degree by internal cognitive states, but it is unknown whether these states are similar across species. To address this question, here we develop a virtual reality setup in which male mice and macaques engage in the same naturalistic visual foraging task. We exploit the richness of a wide range of facial features extracted from video recordings during the task, to train a Markov-Switching Linear Regression (MSLR). By doing so, we identify, on a singletrial basis, a set of internal states that reliably predicts when the animals are going to react to the presented stimuli. Even though the model is trained purely on reaction times, it can also predict task outcome, supporting the behavioural relevance of the inferred states. The relationship of the identified states to task performance is comparable between mice and monkeys. Furthermore, each state corresponds to a characteristic pattern of facial features that partially overlaps between species, highlighting the importance of facial expressions as manifestations of internal cognitive states across species.
]]></description>
<dc:creator>Tlaie, A.</dc:creator>
<dc:creator>Abd El Hay, M. Y.</dc:creator>
<dc:creator>Mert, B.</dc:creator>
<dc:creator>Taylor, R.</dc:creator>
<dc:creator>Ferracci, P. A.</dc:creator>
<dc:creator>Shapcott, K. A.</dc:creator>
<dc:creator>Glukhova, M.</dc:creator>
<dc:creator>Pillow, J. W.</dc:creator>
<dc:creator>Havenith, M. N.</dc:creator>
<dc:creator>Scholvinck, M.</dc:creator>
<dc:date>2024-01-29</dc:date>
<dc:identifier>doi:10.1101/2024.01.24.577055</dc:identifier>
<dc:title><![CDATA[Thoughtful faces: inferring internal states across species using facial features]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-01-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.01.31.578040v1?rss=1">
<title>
<![CDATA[
Model mimicry limits conclusions about neural tuning and can mistakenly imply unlikely priors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.01.31.578040v1?rss=1"
</link>
<description><![CDATA[
In a recent issue of Nature Communications, Harrison, Bays, and Rideaux1 use electroencephalography (EEG) to infer population tuning properties from human visual cortex, and deliver a major update to existing knowledge about the most elemental building block of visual perception - orientation tuning. Using EEG together with simulations in an approach they refer to as "generative forward modeling", the authors adjudicate between two competing population tuning schemes for orientation tuning in visual cortex. They claim that a redistribution of orientation tuning curves can explain their observed pattern of EEG results, and that this tuning scheme embeds a prior of natural image statistics that exhibits a previously undiscovered anisotropy between vertical and horizontal orientations. If correct, this approach could become widely used to find unique neural coding solutions to population response data (e.g., from EEG) and to yield a "true" population tuning scheme deemed generalizable to other instances. However, here we identify major flaws that invalidate the promise of this approach, which we argue should not be used at all. First, we will examine the premise of Harrison and colleagues1, to subsequently explain why "generative forward modeling" cannot circumvent model mimicry pitfalls and can deliver many possible solutions of unknowable correctness. Finally, we show a tentative alternative explanation for the data.

Conflict of interestThe authors declare no conflict of interest
]]></description>
<dc:creator>Wolff, M. J.</dc:creator>
<dc:creator>Rademaker, R. L.</dc:creator>
<dc:date>2024-02-02</dc:date>
<dc:identifier>doi:10.1101/2024.01.31.578040</dc:identifier>
<dc:title><![CDATA[Model mimicry limits conclusions about neural tuning and can mistakenly imply unlikely priors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-02-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.02.26.582029v1?rss=1">
<title>
<![CDATA[
The DREAM implant: A Lightweight, Modular and Cost-Effective Implant System for Chronic Electrophysiology in Head-fixed and Freely Behaving Mice 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.02.26.582029v1?rss=1"
</link>
<description><![CDATA[
Chronic electrophysiological recordings in rodents have significantly improved our understanding of neuronal dynamics and their behavioral relevance. However, current methods for chronically implanting probes present steep trade-offs between cost, ease of use, size, adaptability and long-term stability.

SUMMARYIntroducing a lightweight, cost-effective probe implant system for chronic electrophysiology in rodents, optimized for ease of use, probe recovery, experimental versatility and compatibility with behavior.

This protocol introduces a novel chronic probe implant system for mice called the DREAM (Dynamic, Recoverable, Economical, Adaptable and Modular), designed to overcome the trade-offs associated with currently available options. The system provides a lightweight, modular and cost-effective solution with standardized hardware elements that can be combined and implanted in straightforward steps and explanted safely for recovery and multiple re-use of probes, significantly reducing experimental costs.

The DREAM implant system integrates three hardware modules: (1) a microdrive that can carry all standard silicon probes, allowing experimenters to adjust recording depth across a travel distance of up to 7mm; (2) a 3D-printable, open-source design for a wearable Faraday cage covered in copper mesh for electrical shielding, impact protection and connector placement, and (3) a miniaturized head-fixation system for improved animal welfare and ease of use. The corresponding surgery protocol was optimized for speed (total duration: 2 hours), probe safety and animal welfare.

The resulting implants had minimal impact on animals behavioral repertoire, were easily applicable in freely moving and head-fixed contexts and delivered clearly identifiable spike waveforms and healthy neuronal responses for weeks of data collection post-implant. Infections and other surgery complications were extremely rare.

As such, the DREAM implant system is a versatile, cost-effective solution for chronic electrophysiology in mice, enhancing animal well-being, and enabling more ethologically sound experiments. Its design simplifies experimental procedures across various research needs, increasing accessibility of chronic electrophysiology in rodents to a wide range of research labs.
]]></description>
<dc:creator>Schroeder, T.</dc:creator>
<dc:creator>Taylor, R.</dc:creator>
<dc:creator>Abd El Hay, M.</dc:creator>
<dc:creator>Nemri, A.</dc:creator>
<dc:creator>Franca, A.</dc:creator>
<dc:creator>Battaglia, F.</dc:creator>
<dc:creator>Tiesinga, P.</dc:creator>
<dc:creator>Schoelvinck, M. L.</dc:creator>
<dc:creator>Havenith, M. N.</dc:creator>
<dc:date>2024-02-28</dc:date>
<dc:identifier>doi:10.1101/2024.02.26.582029</dc:identifier>
<dc:title><![CDATA[The DREAM implant: A Lightweight, Modular and Cost-Effective Implant System for Chronic Electrophysiology in Head-fixed and Freely Behaving Mice]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-02-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.03.11.584345v1?rss=1">
<title>
<![CDATA[
Heterogeneity of layer 4 in visual areas of rhesus macaque cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.03.11.584345v1?rss=1"
</link>
<description><![CDATA[
Non-human primates like rhesus macaques are pivotal models for decoding human visual cortex physiology and disorders. We introduce BrainSPACE, an innovative pipeline for rapid and precise brain tissue banking, to sample visual cortex areas: V1, V2, V4, MT, and TEO. Applying snRNA-seq to V1 and V4 (95,071 nuclei), we uncovered conserved GABAergic neuron profiles but stark area-specific diversity in principal neurons, featuring seven unique layer 4 subtypes in V1 and one in V4. Complementary smFISH validated transcriptional gradients across these areas, aligning with ventral and dorsal stream hierarchies. Gene ontology analyses highlighted plasticity-related pathways in unique layer 4 subtypes, with genes like NTNG1 and NLGN1 linked to neurodevelopmental disorders such as autism and schizophrenia. Our insights bridge molecular architecture to visual processing, offering an interactive atlas for community use. By revealing how layer 4 heterogeneity drives hierarchical specialization, our work advances primate brain mapping and informs therapeutic strategies for vision-related pathologies.
]]></description>
<dc:creator>Guenther, D. M.</dc:creator>
<dc:creator>Batiuk, M. Y.</dc:creator>
<dc:creator>Petukhov, V.</dc:creator>
<dc:creator>De Oliveira, R.</dc:creator>
<dc:creator>Wunderle, T.</dc:creator>
<dc:creator>Buchholz, C. J.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Khodosevich, K.</dc:creator>
<dc:date>2024-03-13</dc:date>
<dc:identifier>doi:10.1101/2024.03.11.584345</dc:identifier>
<dc:title><![CDATA[Heterogeneity of layer 4 in visual areas of rhesus macaque cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-03-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.04.11.589006v1?rss=1">
<title>
<![CDATA[
Top-down modulation of visual cortical stimulus encoding and gamma independent of firing rates 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.04.11.589006v1?rss=1"
</link>
<description><![CDATA[
Neurons in primary visual cortex integrate sensory input with signals reflecting the animals internal state to support flexible behavior. Internal variables, such as expectation, attention, or current goals, are imposed in a top-down manner via extensive feedback projections from higher-order areas. We optogenetically activated a high-order visual area, area 21a, in the lightly anesthetized cat (OptoTD), while recording from neuronal populations in V1. OptoTD induced strong, up to several fold, changes in gamma-band synchronization together with much smaller changes in firing rate, and the two effects showed no correlation. OptoTD effects showed specificity for the features of the simultaneously presented visual stimuli. OptoTD-induced changes in gamma synchronization, but not firing rates, were predictive of simultaneous changes in the amount of encoded stimulus information. Our findings suggest that one important role of top-down signals is to modulate synchronization and the information encoded by populations of sensory neurons.
]]></description>
<dc:creator>Lewis, C. M.</dc:creator>
<dc:creator>Wunderle, T.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2024-04-12</dc:date>
<dc:identifier>doi:10.1101/2024.04.11.589006</dc:identifier>
<dc:title><![CDATA[Top-down modulation of visual cortical stimulus encoding and gamma independent of firing rates]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-04-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.04.18.590059v1?rss=1">
<title>
<![CDATA[
Hippocampal place cell sequences during a visual discrimination task: recapitulation of paths near the chosen reward site and independence from perirhinal activity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.04.18.590059v1?rss=1"
</link>
<description><![CDATA[
Compressed hippocampal place-cell sequences have been associated with memory storage, retrieval and planning, but it remains unclear how they align with activity in the parahippocampal cortex. In a visuospatial discrimination task, we found a wide repertoire of hippocampal place cell sequences, which recapitulated paths across the task environment. Place cell sequences generated at reward sites predominantly reiterated trajectories near the chosen maze side, whereas trajectories associated with the side chosen in the previous trial were underrepresented. We hypothesized that neurons in the perirhinal cortex, which during the task display broad firing fields correlated with the animals location, might reactivate in concert with hippocampal sequences. However, we found no evidence of significant perirhinal engagement during virtual trajectories, indicating that these hippocampal memory-related operations can occur independently of the perirhinal cortex.
]]></description>
<dc:creator>Marchesi, P.</dc:creator>
<dc:creator>Bos, J.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:creator>Pennartz, C.</dc:creator>
<dc:date>2024-04-18</dc:date>
<dc:identifier>doi:10.1101/2024.04.18.590059</dc:identifier>
<dc:title><![CDATA[Hippocampal place cell sequences during a visual discrimination task: recapitulation of paths near the chosen reward site and independence from perirhinal activity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-04-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.04.19.590280v1?rss=1">
<title>
<![CDATA[
Hierarchical dynamic coding coordinates speech comprehension in the brain 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.04.19.590280v1?rss=1"
</link>
<description><![CDATA[
Speech comprehension involves transforming an acoustic waveform into meaning. To do so, the human brain generates a hierarchy of features that converts the sensory input into increasingly abstract language properties. However, little is known about how rapid incoming sequences of hierarchical features are continuously coordinated. Here, we propose that each language feature is supported by a dynamic neural code, which represents the sequence history of hierarchical features in parallel. To test this  Hierarchical Dynamic Coding (HDC) hypothesis, we use time-resolved decoding of brain activity to track the construction, maintenance, and update of a comprehensive hierarchy of language features spanning phonetic, word form, lexical-syntactic, syntactic and semantic representations. For this, we recorded 21 native English participants with magnetoencephalography (MEG), while they listened to two hours of short stories in English. Our analyses reveal three main findings. First, the brain represents and simultaneously maintains a sequence of hierarchical features. Second, the duration of these representations depends on their level in the language hierarchy. Third, each representation is maintained by a dynamic neural code, which evolves at a speed commensurate with its corresponding linguistic level. This HDC preserves the maintenance of information over time while limiting destructive interference between successive features. Overall, HDC reveals how the human brain maintains and updates the continuously unfolding language hierarchy during natural speech comprehension, thereby anchoring linguistic theories to their biological implementations.
]]></description>
<dc:creator>Gwilliams, L.</dc:creator>
<dc:creator>Marantz, A.</dc:creator>
<dc:creator>Poeppel, D.</dc:creator>
<dc:creator>KING, J.-R.</dc:creator>
<dc:date>2024-04-19</dc:date>
<dc:identifier>doi:10.1101/2024.04.19.590280</dc:identifier>
<dc:title><![CDATA[Hierarchical dynamic coding coordinates speech comprehension in the brain]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-04-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.04.15.589590v1?rss=1">
<title>
<![CDATA[
Systems Neuroscience Computing in Python (SyNCoPy): A Python Package for Large-scale Analysis of Electrophysiological Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.04.15.589590v1?rss=1"
</link>
<description><![CDATA[
We introduce an open-source Python package for the analysis of large-scale electrophysiological data called SyNCoPy, for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g. time-lock analysis), frequency (e.g. power spectrum), and connectivity (e.g. coherence) domains. It enables user-friendly data analysis on both laptop-based and high-performance computing systems. SyNCoPy is designed to facilitate trial-parallel workflows (parallel processing of trials) making it an ideal tool for large-scale analysis of electrophysiological data. Based on parallel processing of trials, the software can support very large-scale datasets via innovative out-of-core computation techniques. It also provides seamless interoperability with other standard software packages through a range of file format importers and exporters and open file formats. The naming of the user functions closely follows the well-established FieldTrip framework, which is an open-source Matlab toolbox for advanced analysis of electrophysiological data.
]]></description>
<dc:creator>Moenke, G.</dc:creator>
<dc:creator>Schaefer, T.</dc:creator>
<dc:creator>Parto-Dezfouli, M.</dc:creator>
<dc:creator>Kajal, D. S.</dc:creator>
<dc:creator>Fuertinger, S.</dc:creator>
<dc:creator>Schmiedt, J. T.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2024-04-19</dc:date>
<dc:identifier>doi:10.1101/2024.04.15.589590</dc:identifier>
<dc:title><![CDATA[Systems Neuroscience Computing in Python (SyNCoPy): A Python Package for Large-scale Analysis of Electrophysiological Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-04-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.06.03.597080v1?rss=1">
<title>
<![CDATA[
Stimulus-specificity of surround-induced responses in primary visual cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.06.03.597080v1?rss=1"
</link>
<description><![CDATA[
Recent work suggests that stimuli in the surround can drive V1 neurons even without direct visual input to the classical receptive field (RF). These surround-induced responses may represent a prediction of the occluded stimulus, a prediction error, or alternatively, a representation of the gray patch covering the RF. Using Neuropixels recordings in mouse V1, we found that a distal surround stimulus increased V1 firing rates for gray patches up to 90{degrees} in diameter, while LGN firing rates decreased for the same stimuli. These responses occurred across a wide range of conditions: they were elicited by both moving and stationary surround stimuli, did not require spatial continuity or motion coherence, and persisted even for large gray patches (90{degrees}) where there was no mismatch between the classical RF stimulus ([~]20{degrees}) and the near surround. They also emerged when the gray patch appeared as a salient object against a uniform black or white background. Additionally, response magnitudes and latencies were highly similar for black/white uniform surface stimuli on a gray background, with latencies increasing with the gray-patch diameter. These findings are difficult to reconcile with the predictive coding interpretation and fit best with the hypothesis that surround-induced responses reflect the representation of the uniform surface itself and may thereby contribute to image segmentation processes.
]]></description>
<dc:creator>Cuevas, N.</dc:creator>
<dc:creator>Sotomayor-Gomez, B.</dc:creator>
<dc:creator>Tzanou, A.</dc:creator>
<dc:creator>Broggini, A.</dc:creator>
<dc:creator>Uran, C.</dc:creator>
<dc:creator>Vinck, M.</dc:creator>
<dc:date>2024-06-06</dc:date>
<dc:identifier>doi:10.1101/2024.06.03.597080</dc:identifier>
<dc:title><![CDATA[Stimulus-specificity of surround-induced responses in primary visual cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-06-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.07.03.601851v1?rss=1">
<title>
<![CDATA[
Multi-scale modelling of location- and frequency-dependent synaptic plasticity induced by transcranial magnetic stimulation in the dendrites of pyramidal neurons 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.07.03.601851v1?rss=1"
</link>
<description><![CDATA[
BackgroundRepetitive transcranial magnetic stimulation (rTMS) induces long-term changes of synapses, but the mechanisms behind these modifications are not fully understood. Al- though there has been progress in the development of multi-scale modeling tools, no com- prehensive module for simulating rTMS-induced synaptic plasticity in biophysically realistic neurons exists..

ObjectiveWe developed a modelling framework that allows the replication and detailed prediction of long-term changes of excitatory synapses in neurons stimulated by rTMS.

MethodsWe implemented a voltage-dependent plasticity model that has been previously established for simulating frequency-, time-, and compartment-dependent spatio-temporal changes of excitatory synapses in neuronal dendrites. The plasticity model can be incorporated into biophysical neuronal models and coupled to electrical field simulations.

ResultsWe show that the plasticity modelling framework replicates long-term potentiation (LTP)-like plasticity in hippocampal CA1 pyramidal cells evoked by 10-Hz repetitive magnetic stimulation (rMS). This plasticity was strongly distance dependent and concentrated at the proximal synapses of the neuron. We predicted a decrease in the plasticity amplitude for 5 Hz and 1 Hz protocols with decreasing frequency. Finally, we successfully modelled plasticity in distal synapses upon local electrical theta-burst stimulation (TBS) and predicted proximal and distal plasticity for rMS TBS. Notably, the rMS TBS-evoked synaptic plasticity exhibited robust facilitation by dendritic spikes and low sensitivity to inhibitory suppression.

ConclusionThe plasticity modelling framework enables precise simulations of LTP-like cellular effects with high spatio-temporal resolution, enhancing the efficiency of parameter screening and the development of plasticity-inducing rTMS protocols.

HighlightsO_LIFirst rigorously validated model of TMS-induced long-term synaptic plasticity in ex- tended neuronal dendrites that goes beyond point-neuron and mean-field modelling
C_LIO_LIRobust simulations of experimental data on LTP-like plasticity in the proximal dendrites of CA1 hippocampal pyramidal cells evoked by 10 Hz repetitive magnetic stimulation (rMS)
C_LIO_LIReplication of distal synaptic plasticity for a local electrical theta burst stimulation (TBS) protocol
C_LIO_LIPrediction of distal and proximal LTP-like plasticity for rMS TBS
C_LIO_LI1 Hz rMS does not induce long-term depression
C_LI
]]></description>
<dc:creator>Hananeia, N.</dc:creator>
<dc:creator>Ebner, C.</dc:creator>
<dc:creator>Galanis, C.</dc:creator>
<dc:creator>Cuntz, H.</dc:creator>
<dc:creator>Opitz, A.</dc:creator>
<dc:creator>Vlachos, A.</dc:creator>
<dc:creator>Jedlicka, P.</dc:creator>
<dc:date>2024-07-05</dc:date>
<dc:identifier>doi:10.1101/2024.07.03.601851</dc:identifier>
<dc:title><![CDATA[Multi-scale modelling of location- and frequency-dependent synaptic plasticity induced by transcranial magnetic stimulation in the dendrites of pyramidal neurons]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-07-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.08.26.609821v1?rss=1">
<title>
<![CDATA[
Would you agree if N is three?On statistical inference for small N. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.08.26.609821v1?rss=1"
</link>
<description><![CDATA[
Non-human primate studies traditionally use two or three animals. We previously used standard statistics to argue for using either one animal, for an inference about that sample, or five or more animals, for a useful inference about the population. A recently proposed framework argued for testing three animals and accepting the outcome found in the majority as the outcome that is most representative for the population. The proposal tests this framework under various assumptions about the true probability of the representative outcome in the population, i.e. its typicality. On this basis, it argues that the framework is valid across a wide range of typicalities. Here, we show (1) that the error rate of the framework depends strongly on the typicality of the representative outcome, (2) that an acceptable error rate requires this typicality to be very high (87% for a single type of outlier), which actually renders empirical testing beyond a single animal obsolete, (3) that moving from one to three animals decreases error rates mainly for typicality values of 70-90%, and much less for both lower and higher values. Furthermore, we use conjunction analysis to demonstrate that two out of three animals with a given outcome only allow to infer a lower bound to typicality of 9%, which is of limited value. Thus, the use of two or three animals does not allow a useful inference about the population, and if this option is nevertheless chosen, the inferred lower bound of typicality should be reported.
]]></description>
<dc:creator>Psarou, E.</dc:creator>
<dc:creator>Katsanevaki, C.</dc:creator>
<dc:creator>Maris, E.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2024-08-27</dc:date>
<dc:identifier>doi:10.1101/2024.08.26.609821</dc:identifier>
<dc:title><![CDATA[Would you agree if N is three?On statistical inference for small N.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-08-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.09.06.611347v1?rss=1">
<title>
<![CDATA[
Riemannian Geometry for the Classification of Brain States with fNIRS 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.09.06.611347v1?rss=1"
</link>
<description><![CDATA[
BackgroundFunctional near-infrared spectroscopy (fNIRS) has recently gained momentum as a reliable and accurate tool for assessing brain states. This increase in popularity is due to its robustness to movement, non-invasive nature, portability, and user-friendly application. However, compared to functional magnetic resonance imaging (fMRI), fNIRS is less sensitive to deeper brain activity and offers less coverage. Additionally, due to fewer advancements in method development, the performance of fNIRS-based brain-state classification still lags behind more prevalent methods like fMRI.

MethodsWe introduce a novel classification approach grounded in Riemannian geometry for the classification of kernel matrices, leveraging the temporal and spatial channel relationships and inherent duality of fNIRS signals--more specifically, oxygenated and deoxygenated hemoglobin. For the Riemannian geometry-based models, we compared different kernel matrix estimators and two classifiers: Riemannian Support Vector Classifier and Tangent Space Logistic Regression. These were benchmarked against four models employing traditional feature extraction methods. Our approach was tested in two brain-state classification scenarios based on the same fNIRS dataset: an 8-choice classification, which includes seven established plus an individually selected imagery task, and a 2-choice classification of all possible 28 2-task combinations.

ResultsThe novel approach achieved a mean 8-choice classification accuracy of 65%, significantly surpassing the mean accuracy of 42% obtained with traditional methods. Additionally, the best-performing model achieved an average accuracy of 96% for 2-choice classification across all possible 28 task combinations, compared to 78% with traditional models.

ConclusionTo our knowledge, we are the first to demonstrate that the proposed Riemannian geometry-based classification approach is both powerful and viable for fNIRS data, considerably increasing the accuracy in binary and multi-class classification of brain activation patterns.
]]></description>
<dc:creator>Näher, T.</dc:creator>
<dc:creator>Bastian, L.</dc:creator>
<dc:creator>Vorreuther, A.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:creator>Goebel, R. W.</dc:creator>
<dc:creator>Sorger, B.</dc:creator>
<dc:date>2024-09-10</dc:date>
<dc:identifier>doi:10.1101/2024.09.06.611347</dc:identifier>
<dc:title><![CDATA[Riemannian Geometry for the Classification of Brain States with fNIRS]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-09-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.09.16.613302v1?rss=1">
<title>
<![CDATA[
Manipulating attentional priority creates a trade-off between memory and sensory representations in human visual cortex 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.09.16.613302v1?rss=1"
</link>
<description><![CDATA[
People often remember visual information over brief delays while actively engaging with ongoing inputs from the surrounding visual environment. Depending on the situation, one might prioritize mnemonic contents (i.e., remembering details of a past event), or preferentially attend sensory inputs (i.e., minding traffic while crossing a street). Previous fMRI work has shown that early sensory regions can simultaneously represent both mnemonic and passively viewed sensory information. Here we test the limits of such simultaneity by manipulating attention towards sensory distractors during a working memory task performed by human subjects during fMRI scanning. Participants remembered the orientation of a target grating while a distractor grating was shown during the middle portion of the memory delay. Critically, there were several subtle changes in the contrast and the orientation of the distractor, and participants were cued to either ignore the distractor, detect a change in contrast, or detect a change in orientation. Despite sensory stimulation being matched in all three conditions, the fidelity of memory representations in early visual cortex was highest when the distractor was ignored, intermediate when participants attended distractor contrast, and lowest when participants attended the orientation of the distractor during the delay. In contrast, the fidelity of distractor representations was lowest when ignoring the distractor, intermediate when attending distractor-contrast, and highest when attending distractor-orientation. These data suggest a trade-off in early sensory representations when engaging top-down feedback to attend both seen and remembered features and may partially explain memory failures that occur when subjects are distracted by external events.
]]></description>
<dc:creator>Rademaker, R. L.</dc:creator>
<dc:creator>Serences, J.</dc:creator>
<dc:date>2024-09-16</dc:date>
<dc:identifier>doi:10.1101/2024.09.16.613302</dc:identifier>
<dc:title><![CDATA[Manipulating attentional priority creates a trade-off between memory and sensory representations in human visual cortex]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-09-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.12.06.627165v1?rss=1">
<title>
<![CDATA[
Stimulus-repetition effects on macaque V1 and V4 microcircuits explain gamma-synchronization increase 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.12.06.627165v1?rss=1"
</link>
<description><![CDATA[
Under natural conditions, animals repeatedly encounter the same visual scenes, objects or patterns repeatedly. These repetitions constitute statistical regularities, which the brain captures in an internal model through learning. A signature of such learning in primate visual areas V1 and V4 is the gradual strengthening of gamma synchronization. We used a V1-V4 Dynamic Causal Model (DCM) to explain visually induced responses in early and late epochs from a sequence of several hundred grating presentations. The DCM reproduced the empirical increase in local and inter-areal gamma synchronization, revealing specific intrinsic connectivity effects that could explain the phenomenon. In a sensitivity analysis, the isolated modulation of several connection strengths induced increased gamma. Comparison of alternative models showed that empirical gamma increases are better explained by (1) repetition effects in both V1 and V4 intrinsic connectivity (alone or together with extrinsic) than in extrinsic connectivity alone, and (2) repetition effects on V1 and V4 population input rather than output gain. The best input gain model included effects in V1 granular and superficial excitatory populations and in V4 granular and deep excitatory populations. Our findings are consistent with gamma reflecting bottom-up signal precision, which increases with repetition and, therefore, with predictability and learning.

HighlightsO_LIWe model learning effects in macaque visual cortex using Dynamic Causal Modeling.
C_LIO_LIMicrocircuit-level changes explain the repetition-induced gamma increases.
C_LIO_LIThe best models include changes 1) within V1 and V4 and 2) in neuronal input gain.
C_LIO_LIGamma may reflect bottom-up signal precision.
C_LI
]]></description>
<dc:creator>Katsanevaki, C.</dc:creator>
<dc:creator>Bosman, C. A.</dc:creator>
<dc:creator>Friston, K. J.</dc:creator>
<dc:creator>Fries, P.</dc:creator>
<dc:date>2024-12-11</dc:date>
<dc:identifier>doi:10.1101/2024.12.06.627165</dc:identifier>
<dc:title><![CDATA[Stimulus-repetition effects on macaque V1 and V4 microcircuits explain gamma-synchronization increase]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-12-11</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
