Biology subjects

David, S. V.

Publications and source records attributed to David, S. V..

18 recordsLinked to original sources

bioRxiv2026

A low-dimensional, generalizable encoding manifold for auditory cortex

Neural populations in auditory cortex (AC) perform sensory computations that support stimulus category decoding and flexible behavior. The underlying geometry and generalizability of these computations for natural stimuli remain poorly understood, particularly at the single-neuron level, where neurons display wide-ranging tuning specificity and temporal acuity. To address this gap, we developed ACNet, a foundation model of cortical sound encoding, to predict the time-varying activity of >3000 neurons in AC of ferrets. Training data included 42 hours of natural sounds spanning over 100 categories and were collected from multiple recording sites across multiple animals. The model achieved state-of-the-art response prediction accuracy. Model activity was succinctly captured by a low-dimensional neural manifold, which generalized (>80% of variance) across animals. Analysis of ACNet activations revealed the emergence of rate-based, sparse sound coding across layers, a prominent feature of the auditory cortex. These transformations were concomitant with the emergence of more accurate and neurally aligned auditory category decoding, even though ACNet was not explicitly trained to categorize sounds. The model also revealed tuning differences between anatomically distinct cell types. Taken together, our results demonstrate that foundation models of sensory systems can reveal generalizable computations by large neural populations.

neuroscience↗

bioRxiv2025

Neurons in auditory cortex integrate information within constrained temporal windows that are invariant to the stimulus context and information rate

Much remains unknown about the computations that allow animals to flexibly integrate across multiple timescales in natural sounds. One key question is whether multiscale integration is accomplished by diverse populations of neurons, each of which integrates information within a constrained temporal window, or whether individual units effectively integrate across many different temporal scales depending on the information rate. Here, we show that responses from neurons throughout the ferret auditory cortex are nearly completely unaffected by sounds falling beyond a time-limited "integration window". This window varies substantially across cells within the auditory cortex ([~]15 to [~]150 ms), increasing substantially from primary to non-primary auditory cortex across all cortical layers, but is unaffected by the information rate of sound. These results indicate that multiscale computation is predominantly accomplished by diverse and hierarchically organized neural populations, each of which integrates information within a highly constrained temporal window.

neuroscience↗

bioRxiv2024

Convolutional neural network models describe the encoding subspace of local circuits in auditory cortex

Convolutional neural networks (CNNs) provide powerful models of neural sensory encoding, but their complexity makes it difficult to discern computations that support their performance. To address this limitation, we developed a linear-nonlinear subspace model that identifies the most informative sensory dimensions captured by a CNN. A CNN was trained on single-neuron data recorded from auditory cortex of ferrets during presentation of a large natural sound set. Each neurons linear tuning subspace was computed by applying dimensionality reduction to the gradient of CNN output relative to input. Subspace projections were combined nonlinearly to predict neural activity. The resulting model was functionally equivalent to the CNN. Analysis of trained models showed that responses of local neural populations sparsely tiled a shared stimulus subspace. Encoding properties also differed between cell types and layers, reflecting their position in the cortical circuit. More generally, these results establish a framework for interpreting deep learning-based encoding models. Significance statementAuditory cortex mediates the representation and discrimination of complex sound features. Many models have been proposed for cortical sound encoding, varying in their generality, interpretability, and ease of fitting. It has been difficult to determine if and what different functional properties are captured by different models. This study shows that two families of encoding models, convolutional neural networks (CNNs) and tuning subspace models, account for the same functional properties, providing an important analytical link between accurate models that are easy to fit (CNNs) and models that are straightforward to interpret (tuning subspace).

neuroscience↗

bioRxiv2023

Unexpected suppression of neural responses to natural foreground versus background sounds in auditory cortex

In everyday hearing, listeners encounter complex auditory scenes containing overlapping sounds that must be grouped into meaningful sources, or streamed, to be perceived accurately. A common example of this problem is the perception of a behaviorally relevant foreground stimulus (speech, vocalizations) in complex background noise (environmental, machine noise). Studies using a foreground/background contrast have shown that high-order areas of auditory cortex in humans pre-attentively form an enhanced representation of the foreground over background stimulus. Achieving this invariant foreground representation requires identifying and grouping the features that comprise the background noise so that they can be removed from the representation of the foreground. To study the cortical computations underlying representation of concurrent background (BG) and foreground (FG) stimuli, we recorded single unit responses in the auditory cortex (AC) of ferrets during presentation of natural sound excerpts from these two categories. In primary and secondary AC, we found overall suppression of responses when BGs and FGs were presented concurrently relative to the sum of responses to the same stimuli in isolation. Surprisingly, and in contrast to percepts that emphasize dynamic FGs, responses to FG sounds were suppressed relative to the paired BG sound. The degree of relative FG suppression could be explained by spectro-temporal statistics unique to each natural sound. Moreover, systematic degradation of the same spectro-temporal features decreased FG suppression as the sound categories became progressively less statistically distinct. The strongly suppressed representation of FG sounds in single units of AC in the presence of BG sound reveals a novel insight into how complex acoustic scenes are encoded at early stages of auditory processing.

neuroscience↗

bioRxiv2023

Task-specific invariant representation in auditory cortex

Categorical sensory representations are critical for many behaviors, including speech perception. In the auditory system, categorical information is thought to arise hierarchically, becoming increasingly prominent in higher order cortical regions. The neural mechanisms that support this robust and flexible computation remain poorly understood. Here, we studied sound representations in primary and non-primary auditory cortex while animals engaged in a challenging sound discrimination task. Population-level decoding of simultaneously recorded single neurons revealed that task engagement caused categorical sound representations to emerge in non-primary auditory cortex. In primary auditory cortex, task engagement caused a general enhancement of sound decoding that was not specific to task-relevant categories. These findings are consistent with mixed selectivity models of neural disentanglement, in which early sensory regions build an overcomplete representation of the world and allow neurons in downstream brain regions to flexibly and selectively read out behaviorally relevant, categorical information.

neuroscience↗

bioRxiv2023

Sensory context for coding of natural sounds in auditory cortex

Accurate sound perception can require integrating information over hundreds of milliseconds or even seconds. Spectro-temporal models of sound coding by single neurons in auditory cortex indicate that the majority of sound-evoked activity can be attributed to stimuli with a few tens of milliseconds. It remains uncertain how the auditory system integrates information about sensory context on a longer timescale. Here we characterized long-lasting contextual effects in auditory cortex (AC) using a diverse set of natural sound stimuli. We measured context effects as the difference in a neurons response to a single probe sound following two different context sounds. Many AC neurons showed context effects lasting longer than the temporal window of a traditional spectro-temporal receptive field. The duration and magnitude of context effects varied substantially across neurons and stimuli. This diversity of context effects formed a sparse code across the neural population that encoded a wider range of contexts than any constituent neuron. Encoding model analysis indicates that context effects can be explained by activity in the local neural population, suggesting that recurrent local circuits support a long-lasting representation of sensory context in auditory cortex.

neuroscience↗

bioRxiv2022

Can Deep Learning Provide a Generalizable Model for Dynamic Sound Encoding in Auditory Cortex?

Convolutional neural networks (CNNs) can provide powerful and flexible models of neural sensory processing. However, the utility of CNNs in studying the auditory system has been limited by their requirement for large datasets and the complex response properties of single auditory neurons. To address these limitations, we developed a population encoding model: a CNN that simultaneously predicts activity of several hundred neurons recorded during presentation of a large set of natural sounds. This approach defines a shared spectro-temporal space and pools statistical power across neurons. Population models of varying architecture performed consistently better than traditional linear-nonlinear models on data from primary and non-primary auditory cortex. Moreover, population models were highly generalizable. The output layer of a model pre-trained on one population of neurons could be fit to novel single units, achieving performance equivalent to that of neurons in the original fit data. This ability to generalize suggests that population encoding models capture a general set of computations performed by auditory cortex.

neuroscience↗

bioRxiv2021

Dimensionality reduction for neural population decoding

Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex.

neuroscience↗

bioRxiv2021

A complex feature-based representation of vocalizations emerges in the superficial layers of primary auditory cortex

Early in auditory processing, neural responses faithfully reflect acoustic input. At higher stages of auditory processing, however, neurons become selective for particular call types, eventually leading to specialized regions of cortex that preferentially process calls at the highest auditory processing stages. We previously proposed that an intermediate step in how non-selective responses are transformed into call-selective responses is the detection of informative call features. But how neural selectivity for informative call features emerges from non-selective inputs, whether feature selectivity gradually emerges over the processing hierarchy, and how stimulus information is represented in non-selective and feature-selective populations remain open questions. In this study, using unanesthetized guinea pigs, a highly vocal and social rodent, as an animal model, we characterized the neural representation of calls in three auditory processing stages - the thalamus (vMGB), and thalamorecipient (L4) and superficial layers (L2/3) of primary auditory cortex (A1). We found that neurons in vMGB and A1 L4 did not exhibit call-selective responses and responded throughout the call durations. However, A1 L2/3 neurons showed high call-selectivity with about a third of neurons responding to only one or two call types. These A1 L2/3 neurons only responded to restricted portions of calls suggesting that they were highly selective for call features. Receptive fields of these A1 L2/3 neurons showed complex spectrotemporal structures that could underlie their high call feature selectivity. Information theoretic analysis revealed that in A1 L4 stimulus information was distributed over the population and was spread out over the call durations. In contrast, in A1 L2/3, individual neurons showed brief bursts of high stimulus-specific information, and conveyed high levels of information per spike. These data demonstrate that a transformation in the neural representation of calls occurs between A1 L4 and A1 L2/3, leading to the emergence of a feature-based representation of calls in A1 L2/3. Our data thus suggest that observed cortical specializations for call processing emerge in A1, and set the stage for further mechanistic studies.

neuroscience↗

bioRxiv2020

Selective effects of arousal on population coding of natural sounds in primary auditory cortex

The ability to discriminate between complex natural sounds is critical for survival. Changes in arousal and other aspects of behavioral state can impact the accuracy of sensory coding, affecting both the reliability of single neuron responses and the degree of correlated noise between neurons. However, it is unclear how these effects interact to influence coding of diverse natural stimuli. We recorded the spiking activity of neural populations in primary auditory cortex (A1) evoked by a large library of natural sounds while monitoring changes in pupil size as an index of arousal. Heightened arousal increased response magnitude and reduced noise correlations between neurons, improving coding accuracy on average. Rather than suppressing shared noise along all dimensions of neural activity, the change in noise correlations occurred via coherent, low-dimensional modulation of response variability in A1. The modulation targeted a different group of neurons from those undergoing changes in response magnitude. Thus, changes in response magnitude and correlation are mediated by distinct mechanisms. The degree to which these low-dimensional changes were aligned with the high-dimensional natural sound-evoked activity was variable, resulting in stimulus-dependent improvements in coding accuracy.

neuroscience↗

bioRxiv2020

Dissociation of task engagement and arousal effects in auditory cortex and midbrain

Both generalized arousal and engagement in a specific task influence sensory neural processing. To isolate effects of these state variables in the auditory system, we recorded single-unit activity from primary auditory cortex (A1) and inferior colliculus (IC) of ferrets during a tone detection task, while monitoring arousal via changes in pupil size. We used a generalized linear model to assess the influence of task engagement and pupil size on sound-evoked activity. In both areas, these two variables affected independent neural populations. Pupil size effects were more prominent in IC, while pupil and task engagement effects were equally likely in A1. Task engagement was correlated with larger pupil; thus, some apparent effects of task engagement should in fact be attributed to fluctuations in pupil size. These results indicate a hierarchy of auditory processing, where generalized arousal enhances activity in midbrain, and effects specific to task engagement become more prominent in cortex.

neuroscience↗

bioRxiv2020

Complementary effects of adaptation and gain control on sound encoding in primary auditory cortex

An important step toward understanding how the brain represents complex natural sounds is to develop accurate models of auditory coding by single neurons. A common model for auditory coding is the linear-nonlinear spectro-temporal receptive field (LN model). The LN model accounts for many features of auditory tuning, but it cannot account for long-lasting effects of sensory context on sound-evoked activity. Two mechanisms that may support these contextual effects are short-term plasticity (STP) and contrast-dependent gain control (GC), each of which has inspired an expanded version of the LN model. Both of these models improve performance over the LN model, but they have never been compared directly. Thus, it is unclear whether they account for distinct processes or describe the same phenomenon in different ways. To address this question, we recorded activity of neurons in primary auditory cortex of awake ferrets during presentation of natural sounds. We then fit models incorporating one nonlinear mechanism (GC or STP) or both (GC+STP) using this single dataset, and measured the correlation between the models predictions and the recorded neural activity. Both the STP and GC models performed significantly better than the LN model, but the GC+STP model performed better than either individual model. We also quantified the similarity between STP and GC model predictions and found only modest equivalence between them. Similar results were observed for a smaller dataset collected in clean and noisy acoustic contexts. These results suggest that the STP and GC models describe distinct, complementary processes in the auditory system. Significance StatementComputational models are used widely to study neural sensory coding. However, models developed in separate studies are often difficult to compare because of differences in stimuli and experimental preparation. This study develops an approach for making systematic comparisons between models that measures the net benefit of incorporating additional nonlinear elements into models of auditory encoding. This approach was then used to compare two different hypotheses for how sensory context, that is, slow changes in the statistics of the acoustic environment, influences activity in auditory cortex. Both models accounted for complementary aspects of the neural response, indicating that a hybrid model incorporating elements of both models provides the most complete characterization of auditory processing.

neuroscience↗

bioRxiv2019

Short-term Effects of Vagus Nerve Stimulationon Learning and Evoked Activity in AuditoryCortex

Chronic vagus nerve stimulation (VNS) can facilitate learning of sensory and motor behaviors. VNS is believed to trigger release of neuromodulators, including norepinephrine and acetylcholine, which can mediate cortical plasticity associated with learning. Most previous work has studied effects of VNS over many days, and less is known about how acute VNS influences neural coding and behavior over the shorter term. To explore this question, we measured effects of VNS on learning of an auditory discrimination over 1-2 days. Ferrets implanted with cuff electrodes on the vagus nerve were trained by classical conditioning on a tone frequency-reward association. One tone was associated with reward while another tone, was not. The frequencies and reward associations of the tones were changed every two days, requiring learning of a new relationship. When the tones (both rewarded and non-rewarded) were paired with VNS, rates of learning increased on the first day following a change in reward association. To examine VNS effects on auditory coding, we recorded single- and multi-unit neural activity in primary auditory cortex (A1) of passively listening animals following brief periods of VNS (20 trials/session) paired with tones. Because afferent VNS induces changes in pupil size associated with fluctuations in neuromodulation, we also measured pupil during recordings. After pairing VNS with a neurons best-frequency (BF) tone, responses in a subpopulation of neurons were reduced. Pairing with an off-BF tone or performing VNS during the inter-trial interval had no effect on responses. We separated the change in A1 activity into two components, one that could be predicted by fluctuations in pupil and one that persisted after VNS and was not accounted for by pupil. The BF-specific reduction in neural responses remained, even after regressing out changes that could be explained by pupil. In addition, the size of VNS-mediated changes in pupil predicted the magnitude of persistent changes in the neural response. This interaction suggests that changes in neuromodulation associated with arousal gate the long-term effects of VNS on neural activity. Taken together, these results support a role for VNS in auditory learning and help establish VNS as a tool to facilitate neural plasticity.

neuroscience↗

bioRxiv2019

Optimizing auditory brainstem response acquisition using interleaved frequencies

Auditory brainstem responses (ABRs) require averaging responses to hundreds or thousands of repetitions of a stimulus (e.g., tone pip) to obtain a measurable evoked response at the scalp. Fast repetition rates lead to changes in ABR amplitude and latency due to adaptation. To minimize the effect of adaptation, stimulus rates are sometimes as low as 10 to 13.3 stimuli per second, requiring long acquisition times. The trade-off between reducing acquisition time and minimizing the effect of adaptation on ABR responses is an especially important consideration for studies of cochlear synaptopathy, which use the amplitude of short latency responses (wave 1) to assess auditory nerve survival. It has been proposed that adaptation during ABR acquisition can be reduced by interleaving tones at different frequencies, rather than testing each frequency serially. With careful ordering of frequencies and levels in the stimulus train, adaptation in the auditory nerve can be minimized, thereby permitting an increase in the rate at which tone bursts are presented. However, widespread adoption of this stimulus design has been hindered by lack of available software. Here, we develop and validate an interleaved stimulus design to optimize the rate of ABR measurement while minimizing adaptation. We implement this method in an open-source data acquisition software tool that permits either serial or interleaved ABR measurements. The open-source software library, psiexperiment, is compatible with widely-used ABR hardware. Consistent with previous studies, careful design of an interleaved stimulus train can reduce ABR acquisition time by more than half, with minimal effect on ABR thresholds and wave 1 latency, while improving measures of wave 1 amplitude.

neuroscience↗

bioRxiv2019

Streaming of repeated noise in primary and secondary fields of auditory cortex

Statistical regularities in natural sounds facilitate the perceptual segregation of auditory sources, or streams. Repetition is one cue that drives stream segregation in humans, but the neural basis of this perceptual phenomenon remains unknown. We demonstrated a similar perceptual ability in animals by training ferrets to detect a stream of repeating noise samples (foreground) embedded in a stream of random samples (background). During passive listening, we recorded neural activity in primary (A1) and secondary (PEG) fields of auditory cortex. We used two context-dependent encoding models to test for evidence of streaming of the repeating stimulus. The first was based on average evoked activity per noise sample and the second on the spectro-temporal receptive field (STRF). Both approaches tested whether changes in the neural response to repeating versus random stimuli were better modeled by scaling the response to both streams equally (global gain) or by separately scaling the response to the foreground versus background stream (stream-specific gain). Consistent with previous observations of adaptation, we found an overall reduction in global gain when the stimulus began to repeat. However, when we measured stream-specific changes in gain, responses to the foreground were enhanced relative to the background. This enhancement was stronger in PEG than A1. In A1, enhancement was strongest in units with low sparseness (i.e., broad sensory tuning) and with tuning selective for the repeated sample. Enhancement of responses to the foreground relative to the background provides evidence for stream segregation that emerges in A1 and is refined in PEG.

neuroscience↗

bioRxiv2019

Pupil-associated states modulate excitability but not stimulus selectivity in primary auditory cortex

Recent research in mice indicates that luminance-independent fluctuations in pupil size predict variability in spontaneous and evoked activity of single neurons in auditory and visual cortex. These findings suggest that pupil is an indicator of large-scale changes in arousal state that affect sensory processing. However, it is not known whether pupil-related state also influences the selectivity of auditory neurons. We recorded pupil size and single-unit spiking activity in the primary auditory cortex (A1) of non-anesthetized male and female ferrets during presentation of natural vocalizations and tone stimuli that allow measurement of frequency and level tuning. Neurons showed a systematic increase in both spontaneous and sound-evoked activity when pupil was large, as well as desynchronization and a decrease in trial-to-trial variability. Relationships between pupil size and firing rate were non-monotonic in some cells. In most neurons, several measurements of tuning, including acoustic threshold, spectral bandwidth, and best frequency, remained stable across large changes in pupil size. Across the population, however, there was a small but significant decrease in acoustic threshold when pupil was dilated. In some recordings, we observed rapid, saccade-like eye movements during sustained pupil constriction, which may indicate sleep. Including the presence of this state as a separate variable in a regression model of neural variability accounted for some, but not all, of the variability and non-monotonicity associated with changes in pupil size.\n\nNew & NoteworthyCortical neurons vary in their response to repeated stimuli, and some portion of the variability is due to fluctuations in network state. By simultaneously recording pupil and single-neuron activity in auditory cortex of ferrets, we provide new evidence that network state affects the excitability of auditory neurons, but not sensory selectivity. In addition, we report the occurrence of possible sleep states, adding to evidence that pupil provides an index of both sleep and physiological arousal.

neuroscience↗

bioRxiv2019

Spectral tuning of adaptation supports coding of sensory context in auditory cortex

Perception of vocalizations and other behaviorally relevant sounds requires integrating acoustic information over hundreds of milliseconds, but the latency of sound-evoked activity in auditory cortex typically has much shorter latency. It has been observed that the acoustic context, i.e., sound history, can modulate sound evoked activity. Contextual effects are attributed to modulatory phenomena, such as stimulus-specific adaption and contrast gain control. However, an encoding model that links context to natural sound processing has yet to be established. We tested whether a model in which spectrally tuned inputs undergo adaptation mimicking short-term synaptic plasticity can account for contextual effects during natural sound processing. Single-unit activity was recorded from primary auditory cortex of awake ferrets during presentation of noise with natural temporal dynamics and fully natural sounds. Encoding properties were characterized by a standard linear-nonlinear spectro-temporal receptive field model (LN STRF) and STRF variants that incorporated STP-like adaptation. In two models, STP was applied either globally across all spectral channels or locally to subsets of channels. For most neurons, STRFs incorporating locally tuned STP predicted neural activity as well or better than the LN and global STP STRF. The strength of nonlinear adaptation varied across neurons. Within neurons, adaptation was generally stronger for activation with excitatory than inhibitory gain. Neurons showing improved STP model performance also tended to undergo stimulus-specific adaptation, suggesting a common mechanism for these phenomena. When STP STRFs were compared between passive and active behavior conditions, response gain often changed, but average STP parameters were stable. Thus, spectrally and temporally heterogeneous adaptation, subserved by a mechanism with STP-like dynamics, may support representation of the diverse spectro-temporal patterns that comprise natural sounds. Author summarySuccessfully discriminating between behaviorally relevant sounds such as vocalizations and environmental noise requires processing how acoustic information changes over many tens to hundreds of milliseconds. The sound-evoked activity measured for most auditory cortical neurons is relatively short (< 50 ms), so it is not clear how the auditory cortex encodes sound information over longer periods. In this study, we propose that nonlinear adaptation, mimicking the effects of short-term synaptic plasticity (STP), enables auditory neurons to encode longer and more complex spectro-temporal patterns. A model in which sound history is stored in the latent state of plastic synapses is able to describe responses of single cortical neurons to natural sounds better than a standard encoding model that does not include nonlinear adaptation. Moreover, STP-like adaptation can account for contextual effects on sound evoked activity that cannot be accounted for by standard encoding models.

neuroscience↗

bioRxiv2018

Intellectual Synthesis in Mentorship Determines Success in Academic Careers

As academic careers become more competitive, junior scientists need to understand the value that mentorship brings to their success in academia. Previous research has found that, unsurprisingly, successful mentors tend to train successful students. But what characteristics of this relationship predict success, and how? We analyzed an open-access database of about 20,000 researchers who have undergone both graduate and postdoctoral training, compiled across several fields of biomedical science. Our results show that postdoctoral mentors were more instrumental to trainees success compared to graduate mentors. A trainees success in academia was predicted by the degree of intellectual synthesis with their mentors, resulting from fusing the influence of disparate advisors. This suggests that junior scientists should have increased chances of success by training with and linking the ideas of mentors from different fields. We discuss the implications of these results for choosing mentors and determining the duration of postdoctoral training.

scientific communication and education↗