Biology subjects

Schneider, D. M.

Publications and source records attributed to Schneider, D. M..

10 recordsLinked to original sources

bioRxiv2026

Distinct frontal cortical pathways for the long-range processing of auditory and visual stimuli

Frontal cortex neurons are sensory responsive and send long-range feedback to multiple different sensory cortices, but whether these functions are carried out by the same neurons or by distinct modality-specific circuits remains unknown. Using large-scale electrophysiology, two-photon calcium imaging, and viral circuit tracing in awake mice, we identify rich, modality-specific sensory coding in the frontal cortex (secondary motor/anterior cingulate cortex) that is largely dissociated from the neurons providing feedback to sensory cortex. Frontal cortex neurons exhibited robust sensory-evoked activity, with response magnitudes, latencies, and feature selectivity comparable to those observed in primary sensory cortex. Individual neurons displayed tuning for distinct sensory modalities, while population-level activity reliably decoded both sensory modality and stimulus identity. Anatomically, primary auditory (A1) and visual (V1) cortex axons were largely intermingled in anterior frontal cortex but more segregated in posterior regions, revealing spatial variation in the integration of sensory inputs. Frontal cortex neurons responsive to auditory stimuli were biased more anterior compared to visually-responsive neurons. Dual retrograde tracing identified distinct frontal cortex populations projecting back to A1 and V1 that were biased to the posterior and medial extent of the frontal cortex. A1- and V1-projecting frontal neurons were minimally sensory responsive, and no more likely to be responsive than other frontal neurons. Together, these findings reveal a division of labor within the frontal cortex, in which detailed sensory representations and corticocortical feedback arise from partially distinct neuronal populations. This circuit architecture provides a means through which specific sensory information can be transformed within the frontal cortex before being communicated back to the sensory cortex according to behavioral demands.

neuroscience↗

bioRxiv2026

A shared cortical manifold links sensory error to motor planning

The ability to detect and correct errors is central to learning and executing skilled behaviors such as speech and musical performance. Accordingly, sensory and motor regions of the brain must communicate and coordinate their activity in order to both detect errors and adapt in response to them. However, the neural mechanisms by which sensory feedback is transformed into updated motor plans across distributed cortical circuits remain unclear. Here, we identify how the mouse brain encodes error signals during a skilled acoustic task and how these signals guide motor adaptation. We developed a novel, sound-dependent behavior in which mice use real-time auditory feedback to adjust ongoing forelimb movements. Task performance critically depends on auditory cortex, where neurons encode error-related feedback that predicts both within-trial and across-trial behavioral adaptations. Acoustic error feedback alters secondary motor cortex (M2) dynamics, selectively pushing activity along dimensions that encode planning signals for upcoming movements. Notably, auditory cortex and M2 share a low-dimensional manifold that emerges only after learning the skilled behavior. This shared geometry is not engaged during simpler forelimb tasks, suggesting that it reflects a learned, context-dependent solution for coordinating distributed cortical activity. In trained mice, silencing auditory cortex disrupts M2 dynamics, consistent with a model of continuously coupled cortical interactions during skilled performance. Together, these findings identify a learned coordinate transformation that maps sensory error onto corrective motor plans. More broadly, they point to a candidate general principle for how learning can establish a shared cortical manifold that links distant brain regions, enabling sensory feedback to selectively reshape future actions and support flexible, goal-directed control.

neuroscience↗

bioRxiv2024

Vocal Call Locator Benchmark (VCL) for localizing rodent vocalizations from multi-channel audio

Understanding the behavioral and neural dynamics of social interactions is a goal of contemporary neuroscience. Many machine learning methods have emerged in recent years to make sense of complex video and neurophysiological data that result from these experiments. Less focus has been placed on understanding how animals process acoustic information, including social vocalizations. A critical step to bridge this gap is determining the senders and receivers of acoustic information in social interactions. While sound source localization (SSL) is a classic problem in signal processing, existing approaches are limited in their ability to localize animal-generated sounds in standard laboratory environments. Advances in deep learning methods for SSL are likely to help address these limitations, however there are currently no publicly available models, datasets, or benchmarks to systematically evaluate SSL algorithms in the domain of bioacoustics. Here, we present the VCL Benchmark: the first large-scale dataset for benchmarking SSL algorithms in rodents. We acquired synchronized video and multi-channel audio recordings of 767,295 sounds with annotated ground truth sources across 9 conditions. The dataset provides benchmarks which evaluate SSL performance on real data, simulated acoustic data, and a mixture of real and simulated data. We intend for this benchmark to facilitate knowledge transfer between the neuroscience and acoustic machine learning communities, which have had limited overlap.

neuroscience↗

bioRxiv2024

Desegregation of neuronal predictive processing

Neural circuits construct internal world-models to guide behavior. The predictive processing framework posits that neural activity signaling sensory predictions and concurrently computing prediction-errors is a signature of those internal models. Here, to understand how the brain generates predictions for complex sensorimotor signals, we investigate the emergence of high-dimensional, multi-modal predictive representations in recurrent networks. We find that robust predictive processing arises in a network with loose excitatory/inhibitory balance. Contrary to previous proposals of functionally specialized cell-types, the network exhibits desegregation of stimulus and prediction-error representations. We confirmed these model predictions by experimentally probing predictive-coding circuits using a rich stimulus-set to violate learned expectations. When constrained by data, our model further reveals and makes concrete testable experimental predictions for the distinct functional roles of excitatory and inhibitory neurons, and of neurons in different layers along a laminar hierarchy, in computing multi-modal predictions. These results together imply that in natural conditions, neural representations of internal models are highly distributed, yet structured to allow flexible readout of behaviorally-relevant information. The generality of our model advances the understanding of computation of internal models across species, by incorporating different types of predictive computations into a unified framework.

neuroscience↗

bioRxiv2024

Learning within a sensory-motor circuit links action to expected outcome

The cortex integrates sound- and movement-related signals to predict the acoustic consequences of behavior and detect violations from expectations. Although expectation- and prediction-related activity has been observed in the auditory cortex of humans, monkeys, and mice during vocal and non-vocal acoustic behaviors, the specific cortical circuitry required for forming memories, recalling expectations, and making predictions remains unknown. By combining closed-loop behavior, electrophysiological recordings, longitudinal pharmacology, and targeted optogenetic circuit activation, we identify a cortical locus for the emergence of expectation and error signals. Movement-related expectation signals and sound-related error signals emerge in parallel in the auditory cortex and are concentrated in largely distinct neurons, consistent with a compartmentalization of different prediction-related computations. On a trial-by-trial basis, expectation and error signals are correlated in auditory cortex, consistent with a local circuit implementation of an internal model. Silencing the auditory cortex during motor-sensory learning prevents the emergence of expectation signals and error signals, revealing the auditory cortex as a necessary node for learning to make predictions. Prediction-like signals can be experimentally induced in the auditory cortex, even in the absence of behavioral experience, by pairing optogenetic motor cortical activation with sound playback, indicating that cortical circuits are sufficient for movement-like predictive processing. Finally, motor-sensory experience realigns the manifold dimensions in which auditory cortical populations encode movement and sound, consistent with predictive processing. These findings show that prediction-related signals reshape auditory cortex dynamics during behavior and reveal a cortical locus for the emergence of expectation and error.

neuroscience↗

bioRxiv2023

Sensation and expectation are embedded in mouse motor cortical activity

During behavior, the motor cortex sends copies of motor-related signals to sensory cortices. It remains unclear whether these corollary discharge signals strictly encode movement or whether they also encode sensory experience and expectation. Here, we combine closed-loop behavior with large-scale physiology, projection-pattern specific recordings, and circuit perturbations to show that neurons in mouse secondary motor cortex (M2) encode sensation and are influenced by expectation. When a movement unexpectedly produces a sound, M2 becomes dominated by sound-evoked activity. Sound responses in M2 are inherited partially from the auditory cortex and are routed back to the auditory cortex, providing a path for the dynamic exchange of sensory-motor information during behavior. When the acoustic consequences of a movement become predictable, M2 responses to self-generated sounds are selectively gated off. These changes in single-cell responses are reflected in population dynamics, which are influenced by both sensation and expectation. Together, these findings reveal the rich embedding of sensory and expectation signals in motor cortical activity.

neuroscience↗

bioRxiv2023

Movement-related modulation in mouse auditory cortex is widespread yet locally diverse

Neurons in the mouse auditory cortex are strongly influenced by behavior, including both suppression and enhancement of sound-evoked responses during movement. The mouse auditory cortex comprises multiple fields with different roles in sound processing and distinct connectivity to movement-related centers of the brain. Here, we asked whether movement-related modulation might differ across auditory cortical fields, thereby contributing to the heterogeneity of movement-related modulation at the single-cell level. We used wide-field calcium imaging to identify distinct cortical fields followed by cellular-resolution two-photon calcium imaging to visualize the activity of layer 2/3 excitatory neurons within each field. We measured each neurons responses to three sound categories (pure tones, chirps, and amplitude modulated white noise) as mice rested and ran on a non-motorized treadmill. We found that individual neurons in each cortical field typically respond to just one sound category. Some neurons are only active during rest and others during locomotion, and those that are responsive across conditions retain their sound-category tuning. The effects of locomotion on sound-evoked responses vary at the single cell level, with both suppression and enhancement of neural responses, and the net modulatory effect of locomotion is largely conserved across cortical fields. Movement-related modulation in auditory cortex also reflects more complex behavioral patterns, including instantaneous running speed and non-locomotor movements such as grooming and postural adjustments, with similar patterns seen across all auditory cortical fields. Our findings underscore the complexity of movement-related modulation throughout the mouse auditory cortex and indicate that movement related modulation is a widespread phenomenon. SIGNIFICANCE STATEMENTThroughout the sensory cortex, neural activity is influenced by behavior. It remains unknown whether primary and higher-order sensory cortical centers are similarly or differentially influenced by movement. We show that movement-related modulation in the mouse auditory cortex is locally complex and heterogeneous, but that at a more macroscopic level, the net effect of movement on primary and higher-order auditory cortex is largely conserved. These data highlight the widespread nature of movement-related modulation and suggest that movement signals may inform neural computations throughout multiple nodes of the sensory cortex.

neuroscience↗

bioRxiv2023

Unsupervised discovery of family specific vocal usage in the Mongolian gerbil

In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.

animal behavior and cognition↗

bioRxiv2023

Stimulus-specific prediction error neurons in mouse auditory cortex

Comparing expectation with experience is an important neural computation performed throughout the brain and is a hallmark of predictive processing. Experiments that alter the sensory outcome of an animals behavior reveal enhanced neural responses to unexpected self-generated stimuli, indicating that populations of neurons in sensory cortex may reflect prediction errors - mismatches between expectation and experience. However, enhanced neural responses to self-generated stimuli could also arise through non-predictive mechanisms, such as the movement-based facilitation of a neurons inherent sound responses. If sensory prediction error neurons exist in sensory cortex, it is unknown whether they manifest as general error responses, or respond with specificity to errors in distinct stimulus dimensions. To answer these questions, we trained mice to expect the outcome of a simple sound-generating behavior and recorded auditory cortex activity as mice heard either the expected sound or sounds that deviated from expectation in one of multiple distinct dimensions. Our data reveal that the auditory cortex learns to suppress responses to self-generated sounds along multiple acoustic dimensions simultaneously. We identify a distinct population of auditory cortex neurons that are not responsive to passive sounds or to the expected sound but that explicitly encode prediction errors. These prediction error neurons are abundant only in animals with a learned motor-sensory expectation, and encode one or two specific violations rather than a generic error signal.

neuroscience↗

bioRxiv2021

Temporally precise movement-based predictions in the mouse auditory cortex

Many of the sensations experienced by an organism are caused by their own actions, and accurately anticipating both the sensory features and timing of self-generated stimuli is crucial to a variety of behaviors. In the auditory cortex, neural responses to self-generated sounds exhibit frequency-specific suppression, suggesting that movement-based predictions may be implemented early in sensory processing. Yet it remains unknown whether this modulation results from a behaviorally specific and temporally precise prediction, nor is it known whether corresponding expectation signals are present locally in the auditory cortex. To address these questions, we trained mice to expect the precisely timed acoustic outcome of a forelimb movement using a closed-loop sound-generating lever. Dense neuronal recordings in the auditory cortex revealed suppression of responses to self-generated sounds that was specific to the expected acoustic features, specific to a precise time within the movement, and specific to the movement that was coupled to sound during training. Predictive suppression was concentrated in L2/3 and L5, where deviations from expectation also recruited a population of prediction-error neurons that was otherwise unresponsive. Recording in the absence of sound revealed abundant movement signals in deep layers that were biased toward neurons tuned to the expected sound, as well as temporal expectation signals that were present throughout the cortex and peaked at the time of expected auditory feedback. Together, these findings reveal that predictive processing in the mouse auditory cortex is consistent with a learned internal model linking a specific action to its temporally precise acoustic outcome, while identifying distinct populations of neurons that anticipate expected stimuli and differentially process expected versus unexpected outcomes.

neuroscience↗