How Movement Modulates Hearing.

Hearing is often viewed as a passive process: Sound enters the ear, triggers a cascade of activity through the auditory system, and culminates in an auditory percept. In contrast to a passive process, motor-related signals strongly modulate the auditory system from the eardrum to the cortex. The motor modulation of auditory activity is most well documented during speech and other vocalizations but also can be detected during a wide variety of other sound-generating behaviors. An influential idea is that these motor-related signals suppress neural responses to predictable movement-generated sounds, thereby enhancing sensitivity to environmental sounds during movement while helping to detect errors in learned acoustic behaviors, including speech and musicianship. Findings in humans, monkeys, songbirds, and mice provide new insights into the circuits that convey motor-related signals to the auditory system, while lending support to the idea that these signals function predictively to facilitate hearing and vocal learning.

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 in male mice 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 and cellular-resolution two-photon calcium imaging to visualize the activity of layer 2/3 excitatory neurons within each field. We measured each neuron's 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 nonlocomotor 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.

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 animal's behavior reveal enhanced neural responses to unexpected self-generated stimuli, indicating that populations of neurons in sensory cortex may reflect prediction errors (PEs), mismatches between expectation and experience. However, enhanced neural responses to self-generated stimuli could also arise through nonpredictive mechanisms, such as the movement-based facilitation of a neuron's 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 of either sex 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 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. Together, these findings reveal that cortical predictions about self-generated sounds have specificity in multiple simultaneous dimensions and that cortical prediction error neurons encode specific violations from expectation.SIGNIFICANCE STATEMENT Audette et. al record neural activity in the auditory cortex while mice perform a sound-generating forelimb movement and measure neural responses to sounds that violate an animal's expectation in different ways. They find that predictions about self-generated sounds are highly specific across multiple stimulus dimensions and that a population of typically nonsound-responsive neurons respond to sounds that violate an animal's expectation in a specific way. These results identify specific prediction error (PE) signals in the mouse auditory cortex and suggest that errors may be calculated early in sensory processing.

A cortical filter that learns to suppress the acoustic consequences of movement.

Sounds can arise from the environment and also predictably from many of our own movements, such as vocalizing, walking, or playing music. The capacity to anticipate these movement-related (reafferent) sounds and distinguish them from environmental sounds is essential for normal hearing1,2, but the neural circuits that learn to anticipate the often arbitrary and changeable sounds that result from our movements remain largely unknown. Here we developed an acoustic virtual reality (aVR) system in which a mouse learned to associate a novel sound with its locomotor movements, allowing us to identify the neural circuit mechanisms that learn to suppress reafferent sounds and to probe the behavioural consequences of this predictable sensorimotor experience. We found that aVR experience gradually and selectively suppressed auditory cortical responses to the reafferent frequency, in part by strengthening motor cortical activation of auditory cortical inhibitory neurons that respond to the reafferent tone. This plasticity is behaviourally adaptive, as aVR-experienced mice showed an enhanced ability to detect non-reafferent tones during movement. Together, these findings describe a dynamic sensory filter that involves motor cortical inputs to the auditory cortex that can be shaped by experience to selectively suppress the predictable acoustic consequences of movement.

Signatures of Microevolutionary Processes in Phylogenetic Patterns.

Phylogenetic trees are representations of evolutionary relationships among species and contain signatures of the processes responsible for the speciation events they display. Inferring processes from tree properties, however, is challenging. To address this problem, we analyzed a spatially-explicit model of speciation where genome size and mating range can be controlled. We simulated parapatric and sympatric (narrow and wide mating range, respectively) radiations and constructed their phylogenetic trees, computing structural properties such as tree balance and speed of diversification. We showed that parapatric and sympatric speciation are well separated by these structural tree properties. Balanced trees with constant rates of diversification only originate in sympatry and genome size affected both the balance and the speed of diversification of the simulated trees. Comparison with empirical data showed that most of the evolutionary radiations considered to have developed in parapatry or sympatry are in good agreement with model predictions. Even though additional forces other than spatial restriction of gene flow, genome size, and genetic incompatibilities, do play a role in the evolution of species formation, the microevolutionary processes modeled here capture signatures of the diversification pattern of evolutionary radiations, regarding the symmetry and speed of diversification of lineages.

A synaptic and circuit basis for corollary discharge in the auditory cortex.

Sensory regions of the brain integrate environmental cues with copies of motor-related signals important for imminent and ongoing movements. In mammals, signals propagating from the motor cortex to the auditory cortex are thought to have a critical role in normal hearing and behaviour, yet the synaptic and circuit mechanisms by which these motor-related signals influence auditory cortical activity remain poorly understood. Using in vivo intracellular recordings in behaving mice, we find that excitatory neurons in the auditory cortex are suppressed before and during movement, owing in part to increased activity of local parvalbumin-positive interneurons. Electrophysiology and optogenetic gain- and loss-of-function experiments reveal that motor-related changes in auditory cortical dynamics are driven by a subset of neurons in the secondary motor cortex that innervate the auditory cortex and are active during movement. These findings provide a synaptic and circuit basis for the motor-related corollary discharge hypothesized to facilitate hearing and auditory-guided behaviours.

The mutation-drift balance in spatially structured populations.

In finite populations the action of neutral mutations is balanced by genetic drift, leading to a stationary distribution of alleles that displays a transition between two different behaviors. For small mutation rates most individuals will carry the same allele at equilibrium, whereas for high mutation rates of the alleles will be randomly distributed with frequencies close to one half for a biallelic gene. For well-mixed haploid populations the mutation threshold is µc=1/2N, where N is the population size. In this paper we study how spatial structure affects this mutation threshold. Specifically, we study the stationary allele distribution for populations placed on regular networks where connected nodes represent potential mating partners. We show that the mutation threshold is sensitive to spatial structure only if the number of potential mates is very small. In this limit, the mutation threshold decreases substantially, increasing the diversity of the population at considerably low mutation rates. Defining kc as the degree of the network for which the mutation threshold drops to half of its value in well-mixed populations we show that kc grows slowly as a function of the population size, following a power law. Our calculations and simulations are based on the Moran model and on a mapping between the Moran model with mutations and the voter model with opinion makers.

Diploid versus haploid models of neutral speciation.

Neutral models of speciation based on isolation by distance and assortative mating, termed topopatric, have shown to be successful in describing abundance distributions and species-area relationships. Previous works have considered this type of process in the context of haploid genomes. Here we discuss the implementation of two schemes of dominance to analyze the effects of diploidy: a complete dominance model in which one allele dominates over the other and a perfect codominant model in which heterozygous genotypes give rise to a third phenotype. In the case of complete dominance, we observe that speciation requires stronger spatial inbreeding in comparison to the haploid model. For perfect codominance, instead, speciation demands stronger genetic assortativeness. Nevertheless, once speciation is established, the three models predict the same abundance distributions even at the quantitative level, revealing the robustness of the original mechanism to describe biodiversity features.

Error catastrophe in populations under similarity-essential recombination.

Organisms are often more likely to exchange genetic information with others that are similar to themselves. One of the most widely accepted mechanisms of RNA virus recombination requires substantial sequence similarity between the parental RNAs and is termed similarity-essential recombination. This mechanism may be considered analogous to assortative mating, an important form of non-random mating that can be found in animals and plants. Here we study the dynamics of haplotype frequencies in populations evolving under similarity-essential recombination. Haplotypes are represented by a genome of B biallelic loci and the Hamming distance between individuals is used as a criterion for recombination. We derive the evolution equations for the haplotype frequencies assuming that recombination does not occur if the genetic distance is larger than a critical value G and that mutation occurs at a rate µ per locus. Additionally, uniform crossover is considered. Although no fitness is directly associated to the haplotypes, we show that frequency-dependent selection emerges dynamically and governs the haplotype distribution. A critical mutation rate µc can be identified as the error threshold transition, beyond which this selective information cannot be stored. For µ<µc the distribution consists of a dominant sequence surrounded by a cloud of closely related sequences, characterizing a quasispecies. For µ>µc the distribution becomes uniform, with all haplotypes having the same frequency. In the case of extreme assortativeness, where individuals only recombine with others identical to themselves (G=0), the error threshold results µc=1/4, independently of the genome size. For weak assortativity (G=B-1)µc=2(-(B+1)) and for the case of no assortativity (G=B) µc=0. We compute the mutation threshold for 0

A circuit for motor cortical modulation of auditory cortical activity.

Normal hearing depends on the ability to distinguish self-generated sounds from other sounds, and this ability is thought to involve neural circuits that convey copies of motor command signals to various levels of the auditory system. Although such interactions at the cortical level are believed to facilitate auditory comprehension during movements and drive auditory hallucinations in pathological states, the synaptic organization and function of circuitry linking the motor and auditory cortices remain unclear. Here we describe experiments in the mouse that characterize circuitry well suited to transmit motor-related signals to the auditory cortex. Using retrograde viral tracing, we established that neurons in superficial and deep layers of the medial agranular motor cortex (M2) project directly to the auditory cortex and that the axons of some of these deep-layer cells also target brainstem motor regions. Using in vitro whole-cell physiology, optogenetics, and pharmacology, we determined that M2 axons make excitatory synapses in the auditory cortex but exert a primarily suppressive effect on auditory cortical neuron activity mediated in part by feedforward inhibition involving parvalbumin-positive interneurons. Using in vivo intracellular physiology, optogenetics, and sound playback, we also found that directly activating M2 axon terminals in the auditory cortex suppresses spontaneous and stimulus-evoked synaptic activity in auditory cortical neurons and that this effect depends on the relative timing of motor cortical activity and auditory stimulation. These experiments delineate the structural and functional properties of a corticocortical circuit that could enable movement-related suppression of auditory cortical activity.

Functional circuits and anatomical distribution of response properties in the primate amygdala.

Recent electrophysiological studies on the primate amygdala have advanced our understanding of how individual neurons encode information relevant to emotional processes, but it remains unclear how these neurons are functionally and anatomically organized. To address this, we analyzed cross-correlograms of amygdala spike trains recorded during a task in which monkeys learned to associate novel images with rewarding and aversive outcomes. Using this task, we have recently described two populations of amygdala neurons: one that responds more strongly to images predicting reward (positive value-coding), and another that responds more strongly to images predicting an aversive stimulus (negative value-coding). Here, we report that these neural populations are organized into distinct, but anatomically intermingled, appetitive and aversive functional circuits, which are dynamically modulated as animals used the images to predict outcomes. Furthermore, we report that responses to sensory stimuli are prevalent in the lateral amygdala, and are also prevalent in the medial amygdala for sensory stimuli that are emotionally significant. The circuits identified here could potentially mediate valence-specific emotional behaviors thought to involve the amygdala.

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.

Auditory neuroscience: Sounds make the face move.

Animals including humans often react to sounds by involuntarily moving their face and body. A new study shows that facial movements provide a simple and reliable readout of a mouse's hearing ability that is more sensitive than traditional measurements.

Sensation and expectation are embedded in mouse motor cortical activity.

During behavior, the motor cortex sends copies of motor-related signals to sensory cortices. 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 reciprocal 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 embedding of sensory and expectation signals in motor cortical activity.

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.

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.

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.

Fish who act on impulse must learn the consequences.

Animals learn internal models that link specific behaviors to their anticipated sensory outcomes. In this issue of Neuron, Wallach and Sawtell1 discover that freely moving fish learn how the sensory outcome of a single behavior changes with local context.

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.

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 animal's 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 neuron's 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.