Biology subjects

Englitz, B.

Publications and source records attributed to Englitz, B..

17 recordsLinked to original sources

bioRxiv2026

The representation of vocalizations in structured noise in the mouse auditory cortex

Efficient processing of complex acoustic signals within noisy environments is a fundamental challenge for the auditory system. This study investigates the cortical representation of structured noise and vocalizations embedded in it using widefield calcium imaging with the high-speed Calcium indicator jGCaMP8m in the mouse auditory cortex. The spectrotemporal statistics of the structured noise were controllable, including its temporal variance and cross-frequency correlations. We find that structured noise invokes spatially and temporally complex neural responses across the auditory cortex that reflect the spectrotemporal properties of the noise. We identified partially distinct noise response components: noise onset responses showed stronger overlap with primary than secondary areas, while later suppression components did not show a significant primary-secondary bias. Vocalization-evoked responses were significantly attenuated in the presence of noise compared to silent contexts, exhibiting reduced onset magnitude and altered adaptation dynamics. Using decoding we find the neural representation of vocalizations to become more discriminable with longer preceding noise exposure. Furthermore, structured noise cochleograms could be reconstructed from neural population activity, with performance depending on stimulus statistics, tending to be higher in primary than secondary auditory cortex while remaining substantial across both. These findings suggest that the mouse auditory cortex represents acoustic targets in noise through context-dependent and spatially distributed population activity, shaped by both the statistical structure and temporal history of the acoustic background.

neuroscience↗

bioRxiv2026

Whole-Brain Co-Mapping of Gene Expression and NeuronalActivity at Cellular Resolution in Behaving Zebrafish

The brains capabilities rely on both the molecular properties of individual cells and their interactions across brain-wide networks. However, relating gene expression to activity in individual neurons across the entire brain remains elusive. Here we developed an experimental-computational platform, WARP, for whole-brain imaging of neuronal activity during behavior, expansion-assisted spatial transcriptomics, and cellular-level registration of these two modalities. Through joint analysis of whole-brain neuronal activity during multiple behaviors, cellular gene expression, and anatomy, we identified functions of molecularly defined populations--including luminance coding in a cckb-pou4f2 midbrain population and task-structured activity in pvalb7-eomesa hippocampal-like neurons--and defined over 2,000 other function-gene-anatomy subpopulations. Analysis of this unprecedented multimodal dataset also revealed that most gene-matched neurons showed stronger activity correlations, highlighting a brain-wide role for gene expression in functional organization. WARP establishes a foundational platform and open-access dataset for cross-experiment discovery, high-throughput function-to-gene mapping, unification of cell biology and systems neuroscience, and scalable circuit modeling at the whole-brain scale.

neuroscience↗

bioRxiv2025

SpotDMix: informed mRNA transcript assignment using mixture models

Unveiling the genetic profiles of spatially distinguished cells is an important aspect in many areas of brain research, as the genetic identity contains information about a cells physiological properties and internal state. On top of this, knowledge of the genetic details of each cell can reveal structural organization within tissue. As image-based spatial transcriptomics moves toward applications in tissues with dense cellular packing, accurate assignment of detected mRNA transcripts ("spots") to correct segmented cells becomes increasingly difficult, rendering simple methods insufficient with many incorrect assignments to neighboring cells. Here we introduce SpotDMix, a statistical model for assigning spots to cells by modeling spots as coming from a mixture model of distributions matching segmented cell shapes, with assignment probabilities and shape parameters optimized using the Expectation Maximization algorithm. Performance is assessed and compared against several simple methods in various scenarios on both surrogate data and larval zebrafish data. In all tested scenarios SpotDMix outperforms the simple methods on all evaluated metrics, including individual transcript assignment accuracy, total assigned number of spots per cell error and cell type classification. Further, SpotDMix produces a higher degree of exclusivity between genes which are known to not or rarely co-express.

neuroscience↗

bioRxiv2025

Neural integration of acoustic statistics enables detecting acoustic targets in noise

Sound detection amidst noise presents an important challenge in audition. Many naturally occurring sounds (rain, wind) can be described and predicted only statistically, so-called sound textures. Previous research has demonstrated the human ability to leverage this statistical predictability for sound recognition, but the neural mechanisms remain elusive. We trained mice to detect vocalizations embedded in sound textures with different statistical predictability, while recording and optogenetically modulating the neural activity in the auditory cortex. Mice showed improved performance and neural encoding if they could sample the statistics longer per trial. Textures with more exploitable structure, specifically higher cross-frequency correlations improved behavioral performance as well as neural representation of background and vocalization. Activating parvalbumin-positive (PV) interneurons had an asymmetric effect, improving detection and neural encoding of vocalizations for low correlations, and impoverishing them for high cross-frequency correlations. In summary, mice exploit stimulus statistics to improve sound detection in naturalistic background noise, reflected in behavioral performance and neural activity, relying on PV interneurons for temporal integration. HighlightsO_LIMice integrate statistical information indicated by behavior and neural activity C_LIO_LIEncoding of background sounds stays stable in A1, while vocalizations are enhanced C_LIO_LIHigh cross-frequency correlations improve target detection and neural encoding C_LIO_LIActivating PV cells improves detection of sounds with low cross-frequency correlations C_LI

neuroscience↗

bioRxiv2024

How the layer-dependent ratio of excitatory to inhibitory cells shapes cortical coding in balanced networks

The cerebral cortex exhibits a sophisticated neural architecture across its six layers. Recently, it was found that these layers exhibit different ratios of excitatory to inhibitory (EI) neurons, ranging from 4 to 9. This ratio is a key factor for achieving the often reported balance of excitation and inhibition, a hallmark of cortical computation. Although previous studies have explored various biologically realistic aspects of classical balanced networks, such as the effects of more complex neuronal properties including spike frequency adaptation using adaptive exponential integrate and fire (AdEx) neuron models (1), the temporal evolution of randomly connected balanced networks through second order mean field approximations (2), predictions of nonlinear dynamics across diverse neuron models ranging from integrate and fire to Hodgkin-Huxley formulations, thus bridging population dynamics to neuronal electrophysiology (3), the impact of spatial heterogeneity in balanced networks (4-6), and variations in synaptic timescales (7), the specific effects of differences in excitatory-inhibitory (EI) ratios on layer-specific dynamics and computational properties have, to the best of our knowledge, not yet been addressed. We investigate this question using a sparsely connected network model of excitatory and inhibitory neurons. To keep the network in a physiological range of firing rates, we varied the inhibitory firing threshold or the synaptic strength between excitatory and inhibitory neurons. We find that decreasing the EI ratio allows the network to explore a higher-dimensional space and enhance its capacity to represent complex input. By comparing the empirical EI ratios of layer 2/3 and layer 4 in the rodent barrel cortex, we predict that layer 2/3 has a higher dimensionality and coding capacity than layer 4. Furthermore, our analysis of primary visual cortex data from the Allen Brain Institute corroborates these modeling results, also demonstrating increased dimensionality and coding capabilities of layer 2/3.

neuroscience↗

bioRxiv2024

Stopping Speed as State, Not Trait: Exploring Within-Animal Varying Stopping Speeds in a Multi-Session Stop-Signal Task

Being able to reactively stop ongoing movements is important for safe navigation through the environment. Reactive stopping is typically studied using the stop-signal task, where participants are occasionally instructed to stop initiated movements. The speed of stopping, also referred to as the stop-signal reaction time (SSRT), is not observable because successful stopping lacks a response, but can be estimated. Researchers most often acquire one session of data per participant to estimate the speed of stopping, but sometimes more sessions of data are acquired to maximize the signal-to-noise ratio, for example when the task is combined with neural recordings such as electrophysiology. However, it is unknown whether the estimated stopping speed is a fixed trait or a state that can vary under identical experimental conditions. In this study, we investigate whether a separately estimated SSRT for each acquired session is statistically meaningful compared to estimating an across-session SSRT, by collecting many sessions in which male rats performed a stop-signal task. Results revealed that within-animal stopping speeds meaningfully changed from session to session and were not following a trend over time (e.g., due to task learning). Single-session SSRT estimates with lower reliabilities were associated with higher go trial response time variabilities, lower skewness levels of the go trial response time distribution, and lower stop accuracies. We also explored which factors explained changing SSRTs, and showed that motivation, shared motor dynamics, and attention could play a role. In conclusion, we encourage researchers to treat SSRTs as state-like variables when collecting multi-session stop-signal task data, as our results have convincingly shown that stopping speeds are far from trait-like under identical experimental conditions. This session-by-session approach will help future research in which neural signatures of reactive stopping need to be extracted in a time-precise manner, because time-locking stop-related neural activity to session-specific SSRTs is expected to capture the signature more precisely as opposed to an across-session SSRT.

animal behavior and cognition↗

bioRxiv2024

The Recurrent Temporal Restricted Boltzmann Machine Captures Neural Assembly Dynamics in Whole-brain Activity

Animal behaviour alternates between stochastic exploration and goal-directed actions, which are generated by the underlying neural dynamics. Previously, we demonstrated that the compositional Restricted Boltzmann Machine (cRBM) can decompose whole-brain activity of larval zebrafish data at the neural level into a small number ([~]100-200) of assemblies that can account for the stochasticity of the neural activity (van der Plas et al., eLife, 2023). Here we advance this representation by extending to a combined stochastic-dynamical representation to account for both aspects using the Recurrent Temporal RBM (RTRBM) and transfer-learning based on the cRBM estimate. We demonstrate that the functional advantage of the RTRBM is captured in the temporal weights on the hidden units, representing neural assemblies, for both simulated and experimental data. Our results show that the temporal expansion outperforms the stochastic-only cRBM in terms of generalisation error and achieves a more accurate representation of the moments in time. Lastly, we demonstrate that we can identify the original time-scale of assembly dynamics by estimating multiple RTRBMs at different temporal resolutions. Together, we propose that RTRBMs are a valuable tool for capturing the combined stochastic and time-predictive dynamics of large-scale data sets.

neuroscience↗

bioRxiv2023

Decoding contextual influences on auditory perception from primary auditory cortex

Perception can be highly dependent on stimulus context, but whether and how sensory areas encode the context remains uncertain. We used an ambiguous auditory stimulus - a tritone pair - to investigate the neural activity associated with a preceding contextual stimulus that strongly influenced the tritone pairs perception: either as an ascending or a descending step in pitch. We recorded single-unit responses from a population of auditory cortical cells in awake ferrets listening to the tritone pairs preceded by the contextual stimulus. We find that the responses adapt locally to the contextual stimulus, consistent with human MEG recordings from the auditory cortex under the same conditions. Decoding the population responses demonstrates that cells responding to pitch-class-changes are able to predict well the context-sensitive percept of the tritone pairs. Conversely, decoding the individual pitch-class representations and taking their distance in the circular Shepard tone space predicts the opposite of the percept. The various percepts can be readily captured and explained by a neural model of cortical activity based on populations of adapting, pitch-class and pitch-class-direction cells, aligned with the neurophysiological responses. Together, these decoding and model results suggest that contextual influences on perception may well be already encoded at the level of the primary sensory cortices, reflecting basic neural response properties commonly found in these areas.

neuroscience↗

bioRxiv2023

Rodent ultrasonic vocal interaction resolved with millimeter precision using hybrid beamforming

Ultrasonic vocalizations (USVs) fulfill an important role in communication and navigation in many species. Because of their social and affective significance, rodent USVs are increasingly used as a behavioral measure in neurodevelopmental and neurolinguistic research. Reliably attributing USVs to their emitter during close interactions has emerged as a difficult, key challenge. If addressed, all subsequent analyses gain substantial confidence. We present a hybrid ultrasonic tracking system, HyVL, that synergistically integrates a high-resolution acoustic camera with high-quality ultrasonic microphones. HyVL is the first to achieve millimeter precision (~3.4-4.8mm, 91% assigned) in localizing USVs, ~3x better than other systems, approaching the physical limits (mouse snout ~ 10mm). We analyze mouse courtship interactions and demonstrate that males and females vocalize in starkly different relative spatial positions, and that the fraction of female vocalizations has likely been overestimated previously due to imprecise localization. Further, we find that male mice vocalize more intensely when interacting with two mice, an effect mostly driven by the dominant male. HyVL substantially improves the precision with which social communication between rodents can be studied. It is also affordable, open-source, easy to set up, can be integrated with existing setups, and reduces the required number of experiments and animals. Data & Code AvailabilityDuring the review process, reviewers can access all Data and Code via the link below: https://data.donders.ru.nl/loqin/reviewer-208072048/iJ4c-oRNlPIp3vArKiYQ0lAW9FipiHL8foxSzwt1FDAUpon acceptance, these materials will be made available to the public.

animal behavior and cognition↗

bioRxiv2021

Compositional Restricted Boltzmann Machines Unveil the Brain-Wide Organization of Neural Assemblies

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here we recorded the activity from ~ 40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven generative model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine (cRBM), unveils ~ 200 neural assemblies, which compose neurophysiological circuits and whose various com-binations form successive brain states. We then performed in silico perturbation experiments to determine the interregional functional connectivity, which is conserved across individual animals and correlates well with structural connectivity. Our results showcase how cRBMs can capture the coarse-grained organization of the zebrafish brain. Notably, this generative model can readily be deployed to parse neural data obtained by other large-scale recording techniques.

neuroscience↗

bioRxiv2021

High-precision spatial analysis of mouse courtship vocalization behavior reveals sex and strain differences

Mice display a wide repertoire of vocalizations that varies with sex, strain, and context. Especially during social interaction, mice emit sequences of ultrasonic vocalizations (USVs) of high complexity. As animals of both sexes vocalize, a reliable attribution of USVs to their emitter is essential. The state-of-the-art in sound localization for USVs in 2D allows spatial localization at a resolution of multiple centimeters. However, animals interact at closer ranges, e.g. snout-to-snout. Hence, improved algorithms are required to reliably assign USVs. We present a novel algorithm, SLIM (Sound Localization via Intersecting Manifolds), that achieves a 3-fold improvement in accuracy (12-14.3mm) using only 4 microphones and extends to many microphones and localization in 3D. This accuracy allows reliable assignment of 84.3% of all USVs in our dataset. We apply SLIM to courtship interactions between adult C57Bl/6J wildtype mice and those carrying a heterozygous Foxp2 variant (R552H). The improved spatial accuracy reveals detailed vocalization preferences for specific spatial relations between the mice. Specifically, vocalization probability, duration, Wiener entropy, and frequency level differed in particular spatial relations between WT females, Foxp2-R552H and WT males. In conclusion, the improved attribution of vocalizations to their emitters provides a foundation for better understanding social vocal behaviors.

animal behavior and cognition↗

bioRxiv2020

Information transfer and recovery for the sense of touch

Transformation of postsynaptic potentials (PSPs) into action potentials (APs) is the rate-limiting step of communication in neural networks. The efficiency of this intracellular information transfer also powerfully shapes stimulus representations in sensory cortices. Using whole-cell recordings and information-theoretic measures, we show herein that somatic PSPs accurately represent stimulus location on a trial-by-trial basis in single neurons even 4 synapses away from the sensory periphery in the whisker system. This information is largely lost during AP generation but can be rapidly (<20 ms) recovered using complementary information in local populations in a cell-type-specific manner. These results show that as sensory information is transferred from one neural locus to another, the circuits reconstruct the stimulus with high fidelity so that sensory representations of single neurons faithfully represent the stimulus in the periphery, but only in their PSPs, resulting in lossless information processing for the sense of touch in the primary somatosensory cortex.

neuroscience↗

bioRxiv2020

Large- and multi-scale networks in the rodent brain during novelty exploration

Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, due to limited data availability and to the difficulty of analyzing rich multivariate datasets. Here we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (~4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration. Significance statementNeural activity is synchronized over space, time, and frequency. To characterize the dynamics of large-scale networks spanning multiple brain regions, we recorded data from the prefrontal cortex, parietal cortex, and hippocampus in awake behaving mice, and pooled data from spiking activity and local field potentials into one data matrix. Frequency-specific multivariate decomposition methods revealed a cornucopia of neural networks defined by coherent spatiotemporal patterns over time. These findings reveal a rich, dynamic, and multivariate landscape of large-scale neural activity patterns during foraging behavior.

neuroscience↗

bioRxiv2020

Dynamics of neural microstates in the VTA-striatal-prefrontal loop during novelty exploration in the rat

EEG microstates refer to quasi-stable spatial patterns of scalp potentials, and their dynamics have been linked to cognitive and behavioral states. Neural activity at single and multiunit levels also exhibit spatiotemporal coordination, but this spatial scale is difficult to relate to EEG. Here, we translated EEG microstate analysis to triple-area local field potential (LFP) recordings from up to 192 electrodes in rats to investigate the mesoscopic dynamics of neural microstates within and across brain regions. We performed simultaneous recordings from the prefrontal cortex (PFC), striatum (STR), and ventral tegmental area (VTA) during awake behavior (object novelty and exploration). We found that the LFP data can be accounted for by multiple, recurring, quasi-stable spatial activity patterns with an average period of stability of ~60-100 ms. The top four maps accounted for 60-80% of the total variance, compared to ~25% for shuffled data. Cross-correlation of the microstate time-series across brain regions revealed rhythmic patterns of microstate activations, which we interpret as a novel indicator of inter-regional, mesoscale synchronization. Furthermore, microstate features, and patterns of temporal correlations across microstates, were modulated by behavioural states such as movement and novel object exploration. These results support the existence of a functional mesoscopic organization across multiple brain areas, and open up the opportunity to investigate their relation to EEG microstates, of particular interest to the human research community. Significance StatementThe coordination of neural activity across the entire brain has remained elusive. Here we combine large-scale neural recordings at fine spatial resolution with the analysis of microstates, i.e. short-lived, recurring spatial patterns of neural activity. We demonstrate that the local activity in different brain areas can be accounted for by only a few microstates per region. These microstates exhibited temporal dynamics that were correlated across regions in rhythmic patterns. We demonstrate that these microstates are linked to behavior and exhibit different properties in the frequency domain during different behavioural states. In summary, LFP microstates provide an insightful approach to studying both mesoscopic and large-scale brain activation within and across regions.

neuroscience↗

bioRxiv2018

Functional connectivity with short-term dynamics explains diverse patterns of excitatory spike transmission in vivo

Information transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as non-synaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and non-synaptic effects based only on observed pre- and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a thalamothalamic connection in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multi-electrode recordings to illustrate how such an approach has the potential to reveal cell-type specific differences in spike transmission in vivo. Although short-term synaptic plasticity parameters estimated from ongoing pre- and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses. Significance StatementAlthough synaptic dynamics have been extensively studied and modeled using intracellular recordings of post-synaptic currents and potentials, inferring synaptic effects from extracellular spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking depends not only on the amplitude of the current, but also on many other factors, including the activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic neuron, and how recently the postsynaptic neuron has spiked. Here we developed a model that, using only observations of pre- and postsynaptic spiking, aims to describe the dynamics of in vivo spike transmission by modeling both short-term synaptic plasticity and non-synaptic effects. This approach may provide a novel description of fast, structured changes in spike transmission.

neuroscience↗

bioRxiv2018

Classification of mouse ultrasonic vocalizations using deep learning

Vocalizations are a widespread means of communication in the animal kingdom. Mice use a large repertoire of ultrasonic vocalizations (USVs) in different social contexts, for instance courtship, territorial dispute, dominance and mother-pup interaction. Previous studies have pointed to differences in the USVs in different context, sexes, strains and individuals, however, in many cases the outcomes of the analyses remained inconclusive.\n\nWe here provide a more general approach to automatically classify USVs using deep neural networks (DNN). We classified the sex of the emitting mouse (C57Bl/6) based on the vocalizations spectrogram, reaching unprecedented performance (~84% correct) in comparison with other techniques (Support Vector Machines: 64%, Ridge regression: 52%). Vocalization characteristics of individual mice only contribute mildly, and sex-only classification reaches ~78%. The performance can only partially be explained by a set of classical shape features, with duration, volume and bandwidth being the most useful predictors. Splitting estimation into two DNNs, from spectrograms to features (57-82%) and features to sex (67%) does not reach the single-step performance.\n\nIn summary, the emitters sex can be successfully predicted from their spectrograms using DNNs, excelling over other classification techniques. In contrast to previous research, this suggests that male and female vocalizations differ in their spectrotemporal structure, recognizable even in single vocalizations.

bioinformatics↗

bioRxiv2018

Laminar profile of task-related plasticity in ferret primary auditory cortex

Rapid task-related plasticity is a neural correlate of selective attention in primary auditory cortex (A1). Top-down feedback from higher-order cortex may drive task-related plasticity in A1, characterized by enhanced neural representation of behaviorally meaningful sounds during auditory task performance. Since intracortical connectivity is greater within A1 layers 2/3 (L2/3) than in layers 4-6 (L4-6), we hypothesized that enhanced representation of behaviorally meaningful sounds might be greater in A1 L2/3 than L4-6. To test this hypothesis and study the laminar profile of task-related plasticity, we trained 2 ferrets to detect pure tones while we recorded laminar activity across a 1.8 mm depth in A1. In each experiment, we analyzed current-source densities (CSDs), high-gamma local field potentials (LFPs), and multi-unit spiking in response to identical acoustic stimuli during both passive listening and active task performance. We found that neural responses to auditory targets were enhanced during task performance, and target enhancement was greater in L2/3 than in L4-6. Spectrotemporal receptive fields (STRFs) computed from CSDs, high-gamma LFPs, and multi-unit spiking showed similar increases in auditory target selectivity, also greatest in L2/3. Our results suggest that activity within intracortical networks plays a key role in shaping the underlying neural mechanisms of selective attention.

neuroscience↗