Owl's behavior and neural representation predicted by Bayesian inference

Toward a computational role for locus coeruleus/norepinephrine arousal systems

Brain and behavior undergo measurable changes in their underlying state and neuromodulators are thought to contribute to these fluctuations. Why do we undergo such changes, and what function could the underlying neuromodulatory systems perform? Here we examine theoretical answers to these questions with respect to the locus coeruleus/norepinephrine system focusing on peripheral markers for arousal, such as pupil diameter, that are thought to provide a window into brain wide noradrenergic signaling. We explore a computational role for arousal systems in facilitating internal state transitions that facilitate credit assignment and promote accurate perceptions in non-stationary environments. We summarize recent work that supports this idea and highlight open questions as well as alternative views of how arousal affects cognition.

A unified framework for perceived magnitude and discriminability of sensory stimuli

The perception of sensory attributes is often quantified through measurements of sensitivity (the ability to detect small stimulus changes), as well as through direct judgments of appearance or intensity. Despite their ubiquity, the relationship between these two measurements remains controversial and unresolved. Here, we propose a framework in which they arise from different aspects of a common representation. Specifically, we assume that judgments of stimulus intensity (e.g., as measured through rating scales) reflect the mean value of an internal representation, and sensitivity reflects a combination of mean value and noise properties, as quantified by the statistical measure of Fisher information. Unique identification of these internal representation properties can be achieved by combining measurements of sensitivity and judgments of intensity. As a central example, we show that Weber's law of perceptual sensitivity can coexist with Stevens' power-law scaling of intensity ratings (for all exponents), when the noise amplitude increases in proportion to the representational mean. We then extend this result beyond the Weber's law range by incorporating a more general and physiology-inspired form of noise and show that the combination of noise properties and sensitivity measurements accurately predicts intensity ratings across a variety of sensory modalities and attributes. Our framework unifies two primary perceptual measurements-thresholds for sensitivity and rating scales for intensity-and provides a neural interpretation for the underlying representation.

Conductance-based dendrites perform Bayes-optimal cue integration

A fundamental function of cortical circuits is the integration of information from different sources to form a reliable basis for behavior. While animals behave as if they optimally integrate information according to Bayesian probability theory, the implementation of the required computations in the biological substrate remains unclear. We propose a novel, Bayesian view on the dynamics of conductance-based neurons and synapses which suggests that they are naturally equipped to optimally perform information integration. In our approach apical dendrites represent prior expectations over somatic potentials, while basal dendrites represent likelihoods of somatic potentials. These are parametrized by local quantities, the effective reversal potentials and membrane conductances. We formally demonstrate that under these assumptions the somatic compartment naturally computes the corresponding posterior. We derive a gradient-based plasticity rule, allowing neurons to learn desired target distributions and weight synaptic inputs by their relative reliabilities. Our theory explains various experimental findings on the system and single-cell level related to multi-sensory integration, which we illustrate with simulations. Furthermore, we make experimentally testable predictions on Bayesian dendritic integration and synaptic plasticity.

Single trial Bayesian inference by population vector readout in the barn owl's sound localization system

Bayesian models have proven effective in characterizing perception, behavior, and neural encoding across diverse species and systems. The neural implementation of Bayesian inference in the barn owl's sound localization system and behavior has been previously explained by a non-uniform population code model. This model specifies the neural population activity pattern required for a population vector readout to match the optimal Bayesian estimate. While prior analyses focused on trial-averaged comparisons of model predictions with behavior and single-neuron responses, it remains unknown whether this model can accurately approximate Bayesian inference on single trials under varying sensory reliability, a fundamental condition for natural perception and behavior. In this study, we utilized mathematical analysis and simulations to demonstrate that decoding a non-uniform population code via a population vector readout approximates the Bayesian estimate on single trials for varying sensory reliabilities. Our findings provide additional support for the non-uniform population code model as a viable explanation for the barn owl's sound localization pathway and behavior.

A unifying theory explains seemingly contradictory biases in perceptual estimation

Perceptual biases are widely regarded as offering a window into the neural computations underlying perception. To understand these biases, previous work has proposed a number of conceptually different, and even seemingly contradictory, explanations, including attraction to a Bayesian prior, repulsion from the prior due to efficient coding and central tendency effects on a bounded range. We present a unifying Bayesian theory of biases in perceptual estimation derived from first principles. We demonstrate theoretically an additive decomposition of perceptual biases into attraction to a prior, repulsion away from regions with high encoding precision and regression away from the boundary. The results reveal a simple and universal rule for predicting the direction of perceptual biases. Our theory accounts for, and yields, new insights regarding biases in the perception of a variety of stimulus attributes, including orientation, color and magnitude. These results provide important constraints on the neural implementations of Bayesian computations.

Barn owls specialized sound-driven behavior: Lessons in optimal processing and coding by the auditory system

The barn owl, a nocturnal raptor with remarkably efficient prey-capturing abilities, has been one of the initial animal models used for research of brain mechanisms underlying sound localization. Some seminal findings made from their specialized sound localizing auditory system include discoveries of a midbrain map of auditory space, mechanisms towards spatial cue detection underlying sound-driven orienting behavior, and circuit level changes supporting development and experience-dependent plasticity. These findings have explained properties of vital hearing functions and inspired theories in spatial hearing that extend across diverse animal species, thereby cementing the barn owl's legacy as a powerful experimental system for elucidating fundamental brain mechanisms. This concise review will provide an overview of the insights from which the barn owl model system has exemplified the strength of investigating diversity and similarity of brain mechanisms across species. First, we discuss some of the key findings in the specialized system of the barn owl that elucidated brain mechanisms toward detection of auditory cues for spatial hearing. Then we examine how the barn owl has validated mathematical computations and theories underlying optimal hearing across species. And lastly, we conclude with how the barn owl has advanced investigations toward developmental and experience dependent plasticity in sound localization, as well as avenues for future research investigations towards bridging commonalities across species. Analogous to the informative power of Astrophysics for understanding nature through diverse exploration of planets, stars, and galaxies across the universe, miscellaneous research across different animal species pursues broad understanding of natural brain mechanisms and behavior.

The continued importance of comparative auditory research to modern scientific discovery

A rich history of comparative research in the auditory field has afforded a synthetic view of sound information processing by ears and brains. Some organisms have proven to be powerful models for human hearing due to fundamental similarities (e.g., well-matched hearing ranges), while others feature intriguing differences (e.g., atympanic ears) that invite further study. Work across diverse "non-traditional" organisms, from small mammals to avians to amphibians and beyond, continues to propel auditory science forward, netting a variety of biomedical and technological advances along the way. In this brief review, limited primarily to tetrapod vertebrates, we discuss the continued importance of comparative studies in hearing research from the periphery to central nervous system with a focus on outstanding questions such as mechanisms for sound capture, peripheral and central processing of directional/spatial information, and non-canonical auditory processing, including efferent and hormonal effects.

Development of frequency tuning shaped by spatial cue reliability in the barn owl's auditory midbrain

Sensory systems preferentially strengthen responses to stimuli based on their reliability at conveying accurate information. While previous reports demonstrate that the brain reweighs cues based on dynamic changes in reliability, how the brain may learn and maintain neural responses to sensory statistics expected to be stable over time is unknown. The barn owl's midbrain features a map of auditory space where neurons compute horizontal sound location from the interaural time difference (ITD). Frequency tuning of midbrain map neurons correlates with the most reliable frequencies for the neurons' preferred ITD (Cazettes et al., 2014). Removal of the facial ruff led to a specific decrease in the reliability of high frequencies from frontal space. To directly test whether permanent changes in ITD reliability drive frequency tuning, midbrain map neurons were recorded from adult owls, with the facial ruff removed during development, and juvenile owls, before facial ruff development. In both groups, frontally tuned neurons were tuned to frequencies lower than in normal adult owls, consistent with the change in ITD reliability. In addition, juvenile owls exhibited more heterogeneous frequency tuning, suggesting normal developmental processes refine tuning to match ITD reliability. These results indicate causality of long-term statistics of spatial cues in the development of midbrain frequency tuning properties, implementing probabilistic coding for sound localization.

An artificial neural network explains how bats might use vision for navigation

Animals navigate using various sensory information to guide their movement. Miniature tracking devices now allow documenting animals' routes with high accuracy. Despite this detailed description of animal movement, how animals translate sensory information to movement is poorly understood. Recent machine learning advances now allow addressing this question with unprecedented statistical learning tools. We harnessed this power to address visual-based navigation in fruit bats. We used machine learning and trained a convolutional neural network to navigate along a bat's route using visual information that would have been available to the real bat, which we collected using a drone. We show that a simple feed-forward network can learn to guide the agent towards a goal based on sensory input, and can generalize its learning both in time and in space. Our analysis suggests how animals could potentially use visual input for navigation and which features might be useful for this purpose.

Azimuthal sound localization in the chicken

Sound localization is crucial for the survival and reproduction of animals, including non-auditory specialist animals such as the majority of avian species. The chicken (Gallus gallus) is a well-suited representative of a non-auditory specialist bird and several aspects of its auditory system have been well studied in the last decades. We conducted a behavioral experiment where 3 roosters performed a sound localization task with broad-band noise, using a 2-alternative forced choice paradigm. We determined the minimum audible angle (MAA) as measure for localization acuity. In general, our results compare to previous MAA measurements with hens in Go/NoGo tasks. The chicken has high localization acuity compared to other auditory generalist bird species tested so far. We found that chickens were better at localizing broadband noise with long duration (1 s; MAA = 16°) compared to brief duration (0.1 s; MAA = 26°). Moreover, the interaural difference in time of arrival and level (ITD and ILD, respectively) at these MAAs are comparable to what measured in other non-auditory specialist bird species, indicating that they might be sufficiently broad to be informative for azimuthal sound localization.

Two Types of Auditory Spatial Receptive Fields in Different Parts of the Chicken's Midbrain

The optic tectum (OT) is an avian midbrain structure involved in the integration of visual and auditory stimuli. Studies in the barn owl, an auditory specialist, have shown that spatial auditory information is topographically represented in the OT. Little is known about how auditory space is represented in the midbrain of birds with generalist hearing, i.e., most of avian species lacking peripheral adaptations such as facial ruffs or asymmetric ears. Thus, we conducted in vivo extracellular recordings of single neurons in the OT and in the external portion of the formatio reticularis lateralis (FRLx), a brain structure located between the inferior colliculus (IC) and the OT, in anaesthetized chickens of either sex. We found that most of the auditory spatial receptive fields (aSRFs) were spatially confined both in azimuth and elevation, divided into two main classes: round aSRFs, mainly present in the OT, and annular aSRFs, with a ring-like shape around the interaural axis, mainly present in the FRLx. Our data further indicate that interaural time difference (ITD) and interaural level difference (ILD) play a role in the formation of both aSRF classes. These results suggest that, unlike mammals and owls which have a congruent representation of visual and auditory space in the OT, generalist birds separate the computation of auditory space in two different midbrain structures. We hypothesize that the FRLx-annular aSRFs define the distance of a sound source from the axis of the lateral visual fovea, whereas the OT-round aSRFs are involved in multimodal integration of the stimulus around the lateral fovea.SIGNIFICANCE STATEMENT Previous studies implied that auditory spatial receptive fields (aSRFs) in the midbrain of generalist birds are only confined along azimuth. Interestingly, we found SRFs s in the chicken to be confined along both azimuth and elevation. Moreover, the auditory receptive fields are arranged in a concentric manner around the overlapping interaural and visual axes. These data suggest that in generalist birds, which mainly rely on vision, the auditory system mainly serves to align auditory stimuli with the visual axis, while auditory specialized birds like the barn owl compute sound sources more precisely and integrate sound positions in the multimodal space map of the optic tectum (OT).

Towards a cross-level understanding of Bayesian inference in the brain

Perception emerges from unconscious probabilistic inference, which guides behaviour in our ubiquitously uncertain environment. Bayesian decision theory is a prominent computational model that describes how people make rational decisions using noisy and ambiguous sensory observations. However, critical questions have been raised about the validity of the Bayesian framework in explaining the mental process of inference. Firstly, some natural behaviours deviate from Bayesian optimum. Secondly, the neural mechanisms that support Bayesian computations in the brain are yet to be understood. Taking Marr's cross level approach, we review the recent progress made in addressing these challenges. We first review studies that combined behavioural paradigms and modelling approaches to explain both optimal and suboptimal behaviours. Next, we evaluate the theoretical advances and the current evidence for ecologically feasible algorithms and neural implementations in the brain, which may enable probabilistic inference. We argue that this cross-level approach is necessary for the worthwhile pursuit to uncover mechanistic accounts of human behaviour.

Emergence of probabilistic representation in the neural network of primary visual cortex

During the early development of the mammalian visual system, the distribution of neuronal preferred orientations in the primary visual cortex (V1) gradually shifts to match major orientation features of the environment, achieving its optimal representation. By combining computational modeling and electrophysiological recording, we provide a circuit plasticity mechanism that underlies the developmental emergence of such matched representation in the visual cortical network. Specifically, in a canonical circuit of densely-interconnected pyramidal cells and inhibitory parvalbumin-expressing (PV+) fast-spiking interneurons in V1 layer 2/3, our model successfully simulates the experimental observations and further reveals that the nonuniform inhibition plays a key role in shaping the network representation through spike timing-dependent plasticity. The experimental results suggest that PV + interneurons in V1 are capable of providing nonuniform inhibition shortly after vision onset. Our study elucidates a circuit mechanism for acquisition of prior knowledge of environment for optimal inference in sensory neural systems

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Computational and experimental methods are combined to representation in mice V1

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Nonuniform inhibition plays a key role in shaping the network representation

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PV + interneurons provide nonuniform inhibition shortly after vision onset

Deep neural network models of sound localization reveal how perception is adapted to real-world environments

Mammals localize sounds using information from their two ears. Localization in real-world conditions is challenging, as echoes provide erroneous information, and noises mask parts of target sounds. To better understand real-world localization we equipped a deep neural network with human ears and trained it to localize sounds in a virtual environment. The resulting model localized accurately in realistic conditions with noise and reverberation. In simulated experiments, the model exhibited many features of human spatial hearing: sensitivity to monaural spectral cues and interaural time and level differences, integration across frequency, biases for sound onsets, and limits on localization of concurrent sources. But when trained in unnatural environments without either reverberation, noise, or natural sounds, these performance characteristics deviated from those of humans. The results show how biological hearing is adapted to the challenges of real-world environments and illustrate how artificial neural networks can reveal the real-world constraints that shape perception.

A neural surveyor to map touch on the body

Perhaps the most recognizable “sensory map” in neuroscience is the somatosensory homunculus. Although the homunculus suggests a direct link between cortical territory and body part, the relationship is actually ambiguous without a decoder that knows this mapping. How the somatosensory system derives a spatial code from an activation in the homunculus is a longstanding mystery we aimed to solve. We propose that touch location is disambiguated using multilateration, a computation used by surveying and global positioning systems to localize objects. We develop a Bayesian formulation of multilateration, which we implement in a neural network to identify its computational signature. We then detect this signature in psychophysical experiments. Our results suggest that multilateration provides the homunculus-to-body mapping necessary for localizing touch.

Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, but how this is computed by neural networks is unknown. We propose that the somatosensory system implements multilateration, a common computation used by surveying and global positioning systems to localize objects. Specifically, to decode touch location on the body, multilateration estimates the relative distance between the afferent input and the boundaries of a body part (e.g., the joints of a limb). We show that a simple feedforward neural network, which captures several fundamental receptive field properties of cortical somatosensory neurons, can implement a Bayes-optimal multilateral computation. Simulations demonstrated that this decoder produced a pattern of localization variability between two boundaries that was unique to multilateration. Finally, we identify this computational signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization.

Barn Owl's Auditory Space Map Activity Matching Conditions for a Population Vector Readout to Drive Adaptive Sound-Localizing Behavior

Space-specific neurons in the owl's midbrain form a neural map of auditory space, which supports sound-orienting behavior. Previous work proposed that a population vector (PV) readout of this map, implementing statistical inference, predicts the owl's sound localization behavior. This model also predicts the frontal localization bias normally observed and how sound-localizing behavior changes when the signal-to-noise ratio varies, based on the spread of activity across the map. However, the actual distribution of population activity and whether this pattern is consistent with premises of the PV readout model on a trial-by-trial basis remains unknown. To answer these questions, we investigated whether the population response profile across the midbrain map in the optic tectum of the barn owl matches these predictions using in vivo multielectrode array recordings. We found that response profiles of recorded subpopulations are sufficient for estimating the stimulus interaural time difference using responses from single trials. Furthermore, this decoder matches the expected differences in trial-by-trial variability and frontal bias between stimulus conditions of low and high signal-to-noise ratio. These results support the hypothesis that a PV readout of the midbrain map can mediate statistical inference in sound-localizing behavior of barn owls.SIGNIFICANCE STATEMENT While the tuning of single neurons in the owl's midbrain map of auditory space has been considered predictive of the highly specialized sound-localizing behavior of this species, response properties across the population remain largely unknown. For the first time, this study analyzed the spread of population responses across the map using multielectrode recordings and how it changes with signal-to-noise ratio. The observed responses support the hypothesis concerning the ability of a population vector readout to predict biases in orienting behaviors and mediate uncertainty-dependent behavioral commands. The results are of significance for understanding potential mechanisms for the implementation of optimal behavioral commands across species.

Neural implementations of Bayesian inference

Bayesian inference has emerged as a general framework that captures how organisms make decisions under uncertainty. Recent experimental findings reveal disparate mechanisms for how the brain generates behaviors predicted by normative Bayesian theories. Here, we identify two broad classes of neural implementations for Bayesian inference: a modular class, where each probabilistic component of Bayesian computation is independently encoded and a transform class, where uncertain measurements are converted to Bayesian estimates through latent processes. Many recent experimental neuroscience findings studying probabilistic inference broadly fall into these classes. We identify potential avenues for synthesis across these two classes and the disparities that, at present, cannot be reconciled. We conclude that to distinguish among implementation hypotheses for Bayesian inference, we require greater engagement among theoretical and experimental neuroscientists in an effort that spans different scales of analysis, circuits, tasks, and species.

Investigating the representation of uncertainty in neuronal circuits

Skilled behavior often displays signatures of Bayesian inference. In order for the brain to implement the required computations, neuronal activity must carry accurate information about the uncertainty of sensory inputs. Two major approaches have been proposed to study neuronal representations of uncertainty. The first one, the Bayesian decoding approach, aims primarily at decoding the posterior probability distribution of the stimulus from population activity using Bayes’ rule, and indirectly yields uncertainty estimates as a by-product. The second one, which we call the correlational approach, searches for specific features of neuronal activity (such as tuning-curve width and maximum firing-rate) which correlate with uncertainty. To compare these two approaches, we derived a new normative model of sound source localization by Interaural Time Difference (ITD), that reproduces a wealth of behavioral and neural observations. We found that several features of neuronal activity correlated with uncertainty on average, but none provided an accurate estimate of uncertainty on a trial-by-trial basis, indicating that the correlational approach may not reliably identify which aspects of neuronal responses represent uncertainty. In contrast, the Bayesian decoding approach reveals that the activity pattern of the entire population was required to reconstruct the trial-to-trial posterior distribution with Bayes’ rule. These results suggest that uncertainty is unlikely to be represented in a single feature of neuronal activity, and highlight the importance of using a Bayesian decoding approach when exploring the neural basis of uncertainty.

In order to optimize their behavior, animals must continuously represent the uncertainty associated with their beliefs. Understanding the neural code for this uncertainty is a pressing and critical issue in neuroscience. Following a long tradition, some studies have investigated this code by measuring how average statistics of neural responses (like the tuning curves) correlate with uncertainty as stimulus characteristics are varied. We show that this approach can be very misleading. An alternative consists in decoding the neuronal responses to recover the posterior distribution over the encoded sensory variables and using the variance of this distribution as the measure of uncertainty. We demonstrate that this decoding approach can indeed avoid the pitfalls of the traditional approach, while leading to more accurate estimates of uncertainty.

Population codes of prior knowledge learned through environmental regularities

How the brain makes correct inferences about its environment based on noisy and ambiguous observations is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment plays an important role in this process. Humans are able to incorporate such prior knowledge in an efficient, Bayes optimal, way in many situations, but it remains an open question how the brain acquires and represents this prior knowledge. The long time spans over which prior knowledge is acquired make it a challenging question to investigate experimentally. In order to guide future experiments with clear empirical predictions, we used a neural network model to learn two commonly used tasks in the experimental literature (i.e. orientation classification and orientation estimation) where the prior probability of observing a certain stimulus is manipulated. We show that a population of neurons learns to correctly represent and incorporate prior knowledge, by only receiving feedback about the accuracy of their inference from trial-to-trial and without any probabilistic feedback. We identify different factors that can influence the neural responses to unexpected or expected stimuli, and find a novel mechanism that changes the activation threshold of neurons, depending on the prior probability of the encoded stimulus. In a task where estimating the exact stimulus value is important, more likely stimuli also led to denser tuning curve distributions and narrower tuning curves, allocating computational resources such that information processing is enhanced for more likely stimuli. These results can explain several different experimental findings, clarify why some contradicting observations concerning the neural responses to expected versus unexpected stimuli have been reported and pose some clear and testable predictions about the neural representation of prior knowledge that can guide future experiments.

Natural ITD statistics predict human auditory spatial perception

A neural code adapted to the statistical structure of sensory cues may optimize perception. We investigated whether interaural time difference (ITD) statistics inherent in natural acoustic scenes are parameters determining spatial discriminability. The natural ITD rate of change across azimuth (ITDrc) and ITD variability over time (ITDv) were combined in a Fisher information statistic to assess the amount of azimuthal information conveyed by this sensory cue. We hypothesized that natural ITD statistics underlie the neural code for ITD and thus influence spatial perception. To test this hypothesis, sounds with invariant statistics were presented to measure human spatial discriminability and spatial novelty detection. Human auditory spatial perception showed correlation with natural ITD statistics, supporting our hypothesis. Further analysis showed that these results are consistent with classic models of ITD coding and can explain the ITD tuning distribution observed in the mammalian brainstem.

Asymmetric visual representation of sex from human body shape

We efficiently infer others' states and traits from their appearance, and these inferences powerfully shape our social behaviour. One key trait is sex, which is strongly cued by the appearance of the body. What are the visual representations that link body shape to sex? Previous studies of visual sex judgment tasks find observers have a bias to report "male", particularly for ambiguous stimuli. This finding implies a representational asymmetry - that for the processes that generate a sex percept, the default output is "male", and "female" is determined by the presence of additional perceptual evidence. That is, female body shapes are positively coded by reference to a male default shape. This perspective makes a novel prediction in line with Treisman's studies of visual search asymmetries: female body targets should be more readily detected amongst male distractors than vice versa. Across 10 experiments (N = 32 each) we confirmed this prediction and ruled out alternative low-level explanations. The asymmetry was found with profile and frontal body silhouettes, frontal photographs, and schematised icons. Low-level confounds were controlled by balancing silhouette images for size and homogeneity, and by matching physical properties of photographs. The female advantage was nulled for inverted icons, but intact for inverted photographs, suggesting reliance on distinct cues to sex for different body depictions. Together, these findings demonstrate a principle of the perceptual coding that links bodily appearance with a significant social trait: the female body shape is coded as an extension of a male default. We conclude by offering a visual experience account of how these asymmetric representations arise in the first place.

Sound localization in barn owls studied with manipulated head-related transfer functions: beyond broadband interaural time and level differences

Interaural time and level differences are important cues for sound localization. We wondered whether the broadband information contained in these two cues could fully explain the behavior of barn owls and responses of midbrain neurons in these birds. To tackle this problem, we developed a novel approach based on head-related transfer functions. These filters contain the complete information present at the eardrum. We selected positions in space characterized by equal broadband interaural time and level differences. Stimulation from such positions provides reduced information to the owl. We show that barn owls are able to discriminate between such positions. In many cases, but not all, the owls may have used spectral components of interaural level differences that exceeded the known behavioral resolution and variability for discrimination. Alternatively, the birds may have used template matching. Likewise, neurons in the optic tectum of the barn owl, a nucleus involved in sensorimotor integration, contained more information than is available in the broadband interaural time and level differences. Thus, these data show that more information is available and used by barn owls for sound localization than carried by broadband interaural time and level differences.

Synthesis of Hemispheric ITD Tuning from the Readout of a Neural Map: Commonalities of Proposed Coding Schemes in Birds and Mammals

A major cue to infer sound direction is the difference in arrival time of the sound at the left and right ears, called interaural time difference (ITD). The neural coding of ITD and its similarity across species have been strongly debated. In the barn owl, an auditory specialist relying on sound localization to capture prey, ITDs within the physiological range determined by the head width are topographically represented at each frequency. The topographic representation suggests that sound direction may be inferred from the location of maximal neural activity within the map. Such topographical representation of ITD, however, is not evident in mammals. Instead, the preferred ITD of neurons in the mammalian brainstem often lies outside the physiological range and depends on the neuron's best frequency. Because of these disparities, it has been assumed that how spatial hearing is achieved in birds and mammals is fundamentally different. However, recent studies reveal ITD responses in the owl's forebrain and midbrain premotor area that are consistent with coding schemes proposed in mammals. Particularly, sound location in owls could be decoded from the relative firing rates of two broadly and inversely ITD-tuned channels. This evidence suggests that, at downstream stages, the code for ITD may not be qualitatively different across species. Thus, while experimental evidence continues to support the notion of differences in ITD representation across species and brain regions, the latest results indicate notable commonalities, suggesting that codes driving orienting behavior in mammals and birds may be comparable.

Decoding Hidden Cognitive States From Behavior and Physiology Using a Bayesian Approach

Cognitive processes, such as learning and cognitive flexibility, are both difficult to measure and to sample continuously using objective tools because cognitive processes arise from distributed, high-dimensional neural activity. For both research and clinical applications, that dimensionality must be reduced. To reduce dimensionality and measure underlying cognitive processes, we propose a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable—called a cognitive state, which links high-dimensional neural recordings and multidimensional behavioral readouts. This framework allows us to decompose the hard problem of modeling the relationship between neural and behavioral data into separable encoding-decoding approaches. We first use a state-space modeling framework, the behavioral decoder, to articulate the relationship between an objective behavioral readout (e.g., response times) and cognitive state. The second step, the neural encoder, involves using a generalized linear model (GLM) to identify the relationship between the cognitive state and neural signals, such as local field potential (LFP). We then use the neural encoder model and a Bayesian filter to estimate cognitive state using neural data (LFP power) to generate the neural decoder. We provide goodness-of-fit analysis and model selection criteria in support of the encoding-decoding result. We apply this framework to estimate an underlying cognitive state from neural data in human participants ([Formula: see text]) performing a cognitive conflict task. We successfully estimated the cognitive state within the 95% confidence intervals of that estimated using behavior readout for an average of 90% of task trials across participants. In contrast to previous encoder-decoder models, our proposed modeling framework incorporates LFP spectral power to encode and decode a cognitive state. The framework allowed us to capture the temporal evolution of the underlying cognitive processes, which could be key to the development of closed-loop experiments and treatments.

The barn owls' Minimum Audible Angle

Interaural time differences (ITD) and interaural level differences (ILD) are physical cues that enable the auditory system to pinpoint the position of a sound source in space. This ability is crucial for animal communication and predator-prey interactions. The barn owl has evolved an exceptional sense of hearing and shows abilities of sound localisation that outperform most other species. So far, behavioural studies in the barn owl often used reflexive responses to investigate aspects of sound localisation. Furthermore, they predominately probed the higher frequencies of the owl's hearing range (> 3 kHz). In the present study we used a Go/NoGo paradigm to measure the barn owl's behavioural sound localisation acuity (expressed as the Minimum Audible Angle, MAA) as a function of stimulus type (narrow-band noise centred at 500, 1000, 2000, 4000 and 8000 Hz, and broad-band noise) and sound source position. We found significant effects of both stimulus type and sound source position on the barn owls' MAA. The MAA improved with increasing stimulus frequency, from 14° at 500 Hz to 6° at 8000 Hz. The smallest MAA of 4° was found for broadband noise stimuli. Comparing different sound source positions revealed smaller MAAs for frontal compared to lateral stimulus presentation, irrespective of stimulus type. These results are consistent with both the known variations in physical ITDs and variation in the width of neural ITD tuning curves with azimuth and frequency. Physical and neural characteristics combine to result in better spatial acuity for frontal compared to lateral sounds and reduced localisation acuity at lower frequencies.

No evidence for abnormal priors in early vision in schizophrenia

The predictive coding account of psychosis postulates the abnormal formation of prior beliefs in schizophrenia, resulting in psychotic symptoms. One domain in which priors play a crucial role is visual perception. For instance, our perception of brightness, line length, and motion direction are not merely based on a veridical extraction of sensory input but are also determined by expectation (or prior) of the stimulus. Formation of such priors is thought to be governed by the statistical regularities within natural scenes. Recently, the use of such priors has been attributed to a specific set of well-documented visual illusions, supporting the idea that perception is biased toward what is statistically more probable within the environment. The Predictive Coding account of psychosis proposes that patients form abnormal representations of statistical regularities in natural scenes, leading to altered perceptual experiences. Here we use classical vision experiments involving a specific set of visual illusions to directly test this hypothesis. We find that perceptual judgments for both patients and control participants are biased in accordance with reported probability distributions of natural scenes. Thus, despite there being a suggested link between visual abnormalities and psychotic symptoms in schizophrenia, our results provide no support for the notion that altered formation of priors is a general feature of the disorder. These data call for a refinement in the predictions of quantitative models of psychosis.

Spectral Weighting Underlies Perceived Sound Elevation

The brain estimates the two-dimensional direction of sounds from the pressure-induced displacements of the eardrums. Accurate localization along the horizontal plane (azimuth angle) is enabled by binaural difference cues in timing and intensity. Localization along the vertical plane (elevation angle), including frontal and rear directions, relies on spectral cues made possible by the elevation dependent filtering in the idiosyncratic pinna cavities. However, the problem of extracting elevation from the sensory input is ill-posed, since the spectrum results from a convolution between source spectrum and the particular head-related transfer function (HRTF) associated with the source elevation, which are both unknown to the system. It is not clear how the auditory system deals with this problem, or which implicit assumptions it makes about source spectra. By varying the spectral contrast of broadband sounds around the 6–9 kHz band, which falls within the human pinna’s most prominent elevation-related spectral notch, we here suggest that the auditory system performs a weighted spectral analysis across different frequency bands to estimate source elevation. We explain our results by a model, in which the auditory system weighs the different spectral bands, and compares the convolved weighted sensory spectrum with stored information about its own HRTFs, and spatial prior assumptions.

Explaining the Effect of Likelihood Manipulation and Prior Through a Neural Network of the Audiovisual Perception of Space

Results in the recent literature suggest that multisensory integration in the brain follows the rules of Bayesian inference. However, how neural circuits can realize such inference and how it can be learned from experience is still the subject of active research. The aim of this work is to use a recent neurocomputational model to investigate how the likelihood and prior can be encoded in synapses, and how they affect audio-visual perception, in a variety of conditions characterized by different experience, different cue reliabilities and temporal asynchrony. The model considers two unisensory networks (auditory and visual) with plastic receptive fields and plastic crossmodal synapses, trained during a learning period. During training visual and auditory stimuli are more frequent and more tuned close to the fovea. Model simulations after training have been performed in crossmodal conditions to assess the auditory and visual perception bias: visual stimuli were positioned at different azimuth (±10° from the fovea) coupled with an auditory stimulus at various audio-visual distances (±20°). The cue reliability has been altered by using visual stimuli with two different contrast levels. Model predictions are compared with behavioral data. Results show that model predictions agree with behavioral data, in a variety of conditions characterized by a different role of prior and likelihood. Finally, the effect of a different unimodal or crossmodal prior, re-learning, temporal correlation among input stimuli, and visual damage (hemianopia) are tested, to reveal the possible use of the model in the clarification of important multisensory problems.

Accuracy-Precision Trade-off in Human Sound Localisation

Sensory representations are typically endowed with intrinsic noise, leading to variability and inaccuracies in perceptual responses. The Bayesian framework accounts for an optimal strategy to deal with sensory-motor uncertainty, by combining the noisy sensory input with prior information regarding the distribution of stimulus properties. The maximum-a-posteriori (MAP) estimate selects the perceptual response from the peak (mode) of the resulting posterior distribution that ensure optimal accuracy-precision trade-off when the underlying distributions are Gaussians (minimal mean-squared error, with minimum response variability). We tested this model on human eye- movement responses toward broadband sounds, masked by various levels of background noise, and for head movements to sounds with poor spectral content. We report that the response gain (accuracy) and variability (precision) of the elevation response components changed systematically with the signal-to-noise ratio of the target sound: gains were high for high SNRs and decreased for low SNRs. In contrast, the azimuth response components maintained high gains for all conditions, as predicted by maximum-likelihood estimation. However, we found that the elevation data did not follow the MAP prediction. Instead, results were better described by an alternative decision strategy, in which the response results from taking a random sample from the posterior in each trial. We discuss two potential implementations of a simple posterior sampling scheme in the auditory system that account for the results and argue that although the observed response strategies for azimuth and elevation are sub-optimal with respect to their variability, it allows the auditory system to actively explore the environment in the absence of adequate sensory evidence.

How Do Expectations Shape Perception?

Perception and perceptual decision-making are strongly facilitated by prior knowledge about the probabilistic structure of the world. While the computational benefits of using prior expectation in perception are clear, there are myriad ways in which this computation can be realized. We review here recent advances in our understanding of the neural sources and targets of expectations in perception. Furthermore, we discuss Bayesian theories of perception that prescribe how an agent should integrate prior knowledge and sensory information, and investigate how current and future empirical data can inform and constrain computational frameworks that implement such probabilistic integration in perception.

Chance, long tails, and inference in a non-Gaussian, Bayesian theory of vocal learning in songbirds

Traditional theories of sensorimotor learning posit that animals use sensory error signals to find the optimal motor command in the face of Gaussian sensory and motor noise. However, most such theories cannot explain common behavioral observations, for example, that smaller sensory errors are more readily corrected than larger errors and large abrupt (but not gradually introduced) errors lead to weak learning. Here, we propose a theory of sensorimotor learning that explains these observations. The theory posits that the animal controls an entire probability distribution of motor commands rather than trying to produce a single optimal command and that learning arises via Bayesian inference when new sensory information becomes available. We test this theory using data from a songbird, the Bengalese finch, that is adapting the pitch (fundamental frequency) of its song following perturbations of auditory feedback using miniature headphones. We observe the distribution of the sung pitches to have long, non-Gaussian tails, which, within our theory, explains the observed dynamics of learning. Further, the theory makes surprising predictions about the dynamics of the shape of the pitch distribution, which we confirm experimentally.

Emergence of an Adaptive Command for Orienting Behavior in Premotor Brainstem Neurons of Barn Owls

The midbrain map of auditory space commands sound-orienting responses in barn owls. Owls precisely localize sounds in frontal space but underestimate the direction of peripheral sound sources. This bias for central locations was proposed to be adaptive to the decreased reliability in the periphery of sensory cues used for sound localization by the owl. Understanding the neural pathway supporting this biased behavior provides a means to address how adaptive motor commands are implemented by neurons. Here we find that the sensory input for sound direction is weighted by its reliability in premotor neurons of the midbrain tegmentum of owls (male and female), such that the mean population firing rate approximates the head-orienting behavior. We provide evidence that this coding may emerge through convergence of upstream projections from the midbrain map of auditory space. We further show that manipulating the sensory input yields changes predicted by the convergent network in both premotor neural responses and behavior. This work demonstrates how a topographic sensory representation can be linearly read out to adjust behavioral responses by the reliability of the sensory input.SIGNIFICANCE STATEMENT This research shows how statistics of the sensory input can be integrated into a behavioral command by readout of a sensory representation. The firing rate of midbrain premotor neurons receiving sensory information from a topographic representation of auditory space is weighted by the reliability of sensory cues. We show that these premotor responses are consistent with a weighted convergence from the topographic sensory representation. This convergence was also tested behaviorally, where manipulation of stimulus properties led to bidirectional changes in sound localization errors. Thus a topographic representation of auditory space is translated into a premotor command for sound localization that is modulated by sensory reliability.

Adaptive coding for dynamic sensory inference

Behavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we discuss general principles that underlie the tradeoff between encoding cost and inference error. We then derive adaptive encoding schemes that dynamically navigate this tradeoff. These optimal encodings tend to increase the fidelity of the neural representation following a change in the stimulus distribution, and reduce fidelity for stimuli that originate from a known distribution. We predict dynamical signatures of such encoding schemes and demonstrate how known phenomena, such as burst coding and firing rate adaptation, can be understood as hallmarks of optimal coding for accurate inference.

Combination of Interaural Level and Time Difference in Azimuthal Sound Localization in Owls

A function of the auditory system is to accurately determine the location of a sound source. The main cues for sound location are interaural time (ITD) and level (ILD) differences. Humans use both ITD and ILD to determine the azimuth. Thus far, the conception of sound localization in barn owls was that their facial ruff and asymmetrical ears generate a two-dimensional grid of ITD for azimuth and ILD for elevation. We show that barn owls also use ILD for azimuthal sound localization when ITDs are ambiguous. For high-frequency narrowband sounds, midbrain neurons can signal multiple locations, leading to the perception of an auditory illusion called a phantom source. Owls respond to such an illusory percept by orienting toward it instead of the true source. Acoustical measurements close to the eardrum reveal a small ILD component that changes with azimuth, suggesting that ITD and ILD information could be combined to eliminate the illusion. Our behavioral data confirm that perception was robust against ambiguities if ITD and ILD information was combined. Electrophysiological recordings of ILD sensitivity in the owl’s midbrain support the behavioral findings indicating that rival brain hemispheres drive the decision to orient to either true or phantom sources. Thus, the basis for disambiguation, and reliable detection of sound source azimuth, relies on similar cues across species as similar response to combinations of ILD and narrowband ITD has been observed in humans.

Development of ear asymmetry in the American barn owl (Tyto furcata pratincola)

Owls are known for their nocturnal hunting capability. Many owl species are able to localize prey in complete darkness just by hearing. Sound localization of strictly nocturnal owls is improved by asymmetrically arranged outer ears. According to Norberg (1977), who worked with adult owls, asymmetrical ears evolved at least four times independently among owls. What is unknown so far is how the ear asymmetry develops in the embryo. Here we examine the embryonic development of ear asymmetry in the American barn owl (Tyto furcata pratincola) in the frame of the 42 stages suggested by Köppl et al. (2005). In this species, the left ear opening in the skin is located higher than its counterpart on the right. Micro-CT scans show that in an anatomically defined coordinate system, the ear openings initially appear symmetrically as does the skull as well as the eyes, the nasal openings, the stapes and the squamosum. The ear openings are initially located ventrally in the skull. Soon after their appearance, the ear openings start to move dorso-occipitally. At the developmental stages 36-39, the left ear opening moves faster than the right one. In this way, an ear asymmetry develops within a few developmental stages. The skull and the other anatomical markers remain symmetrical. Thus, these data show that the soft tissue asymmetry in the barn owl develops already before hatching. It will be interesting to compare the time course described here with the time course of development in owl species with bony ear asymmetries.

Development of a Bayesian Estimator for Audio-Visual Integration: A Neurocomputational Study

The brain integrates information from different sensory modalities to generate a coherent and accurate percept of external events. Several experimental studies suggest that this integration follows the principle of Bayesian estimate. However, the neural mechanisms responsible for this behavior, and its development in a multisensory environment, are still insufficiently understood. We recently presented a neural network model of audio-visual integration (Neural Computation, 2017) to investigate how a Bayesian estimator can spontaneously develop from the statistics of external stimuli. Model assumes the presence of two unimodal areas (auditory and visual) topologically organized. Neurons in each area receive an input from the external environment, computed as the inner product of the sensory-specific stimulus and the receptive field synapses, and a cross-modal input from neurons of the other modality. Based on sensory experience, synapses were trained via Hebbian potentiation and a decay term. Aim of this work is to improve the previous model, including a more realistic distribution of visual stimuli: visual stimuli have a higher spatial accuracy at the central azimuthal coordinate and a lower accuracy at the periphery. Moreover, their prior probability is higher at the center, and decreases toward the periphery. Simulations show that, after training, the receptive fields of visual and auditory neurons shrink to reproduce the accuracy of the input (both at the center and at the periphery in the visual case), thus realizing the likelihood estimate of unimodal spatial position. Moreover, the preferred positions of visual neurons contract toward the center, thus encoding the prior probability of the visual input. Finally, a prior probability of the co-occurrence of audio-visual stimuli is encoded in the cross-modal synapses. The model is able to simulate the main properties of a Bayesian estimator and to reproduce behavioral data in all conditions examined. In particular, in unisensory conditions the visual estimates exhibit a bias toward the fovea, which increases with the level of noise. In cross modal conditions, the SD of the estimates decreases when using congruent audio-visual stimuli, and a ventriloquism effect becomes evident in case of spatially disparate stimuli. Moreover, the ventriloquism decreases with the eccentricity.

Population Vectors Can Provide Near Optimal Integration of Information

Much attention has been paid to the question of how Bayesian integration of information could be implemented by a simple neural mechanism. We show that population vectors based on point-process inputs combine evidence in a form that closely resembles Bayesian inference, with each input spike carrying information about the tuning of the input neuron. We also show that population vectors can combine information relatively accurately in the presence of noisy synaptic encoding of tuning curves.

Sure enough: efficient Bayesian learning and choice

Probabilistic decision-making is a general phenomenon in animal behavior, and has often been interpreted to reflect the relative certainty of animals' beliefs. Extensive neurological and behavioral results increasingly suggest that animal beliefs may be represented as probability distributions, with explicit accounting of uncertainty. Accordingly, we develop a model that describes decision-making in a manner consistent with this understanding of neuronal function in learning and conditioning. This first-order Markov, recursive Bayesian algorithm is as parsimonious as its minimalist point-estimate, Rescorla-Wagner analogue. We show that the Bayesian algorithm can reproduce naturalistic patterns of probabilistic foraging, in simulations of an experiment in bumblebees. We go on to show that the Bayesian algorithm can efficiently describe the behavior of several heuristic models of decision-making, and is consistent with the ubiquitous variation in choice that we observe within and between individuals in implementing heuristic decision-making. By describing learning and decision-making in a single Bayesian framework, we believe we can realistically unify descriptions of behavior across contexts and organisms. A unified cognitive model of this kind may facilitate descriptions of behavioral evolution.

Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics

In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli that caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the predictions of an internal model. The internal model of the Bayes filter is based on the true stimulus dynamics, and in this note, we present a method for training a theoretical neural circuit to approximately implement a Bayes filter when the stimulus dynamics are unknown. To do this we use the inferential properties of linear probabilistic population codes to compute Bayes' rule and train a neural network to compute approximate predictions by the method of maximum likelihood. In particular, we perform stochastic gradient descent on the negative log-likelihood of the neural network parameters with a novel approximation of the gradient. We demonstrate our methods on a finite-state, a linear, and a nonlinear filtering problem and show how the hidden layer of the neural network develops tuning curves consistent with findings in experimental neuroscience.

Arousal-related adjustments of perceptual biases optimize perception in dynamic environments

Prior expectations can be used to improve perceptual judgments about ambiguous stimuli. However, little is known about if and how these improvements are maintained in dynamic environments in which the quality of appropriate priors changes from one stimulus to the next. Using a sound-localization task, we show that changes in stimulus predictability lead to arousal-mediated adjustments in the magnitude of prior-driven biases that optimize perceptual judgments about each stimulus. These adjustments depend on task-dependent changes in the relevance and reliability of prior expectations, which subjects update using both normative and idiosyncratic principles. The resulting variations in biases across task conditions and individuals are reflected in modulations of pupil diameter, such that larger stimulus-evoked pupil responses correspond to smaller biases. These results suggest a critical role for the arousal system in adjusting the strength of perceptual biases with respect to inferred environmental dynamics to optimize perceptual judgements.

Correlates of Perceptual Orientation Biases in Human Primary Visual Cortex

Vision can be considered as a process of probabilistic inference. In a Bayesian framework, perceptual estimates from sensory information are combined with prior knowledge, with a stronger influence of the prior when the sensory evidence is less certain. Here, we explored the behavioral and neural consequences of manipulating stimulus certainty in the context of orientation processing. First, we asked participants to judge whether a stimulus was oriented closer to vertical or the clockwise primary oblique (45°) for two stimulus types (spatially filtered noise textures and sinusoidal gratings) and three manipulations of certainty (orientation bandwidth, contrast, and duration). We found that participants consistently had a bias toward reporting orientation as closer to 45° during conditions of high certainty and that this bias was reduced when sensory evidence was less certain. Second, we measured event-related fMRI BOLD responses in human primary visual cortex (V1) and manipulated certainty via stimulus contrast (100% vs 3%). We then trained a multivariate classifier on the pattern of responses in V1 to cardinal and primary oblique orientations. We found that the classifier showed a bias toward classifying orientation as oblique at high contrast but categorized a wider range of orientations as cardinal for low-contrast stimuli. Orientation classification based on data from V1 thus paralleled the perceptual biases revealed through the behavioral experiments. This pattern of bias cannot be explained simply by a prior for cardinal orientations.SIGNIFICANCE STATEMENT Our perception of the world around us is biased through prior expectations rather than necessarily reflecting the true state of our environment. Here, we investigate biases in the visual processing of spatial orientation to understand how prior expectations and current sensory information interact to generate a percept. By degrading visual input in various ways, we are able to quantify the extent to which prior experience affects both perceptual judgments and neural responses in the human visual system. We observe systematic biases in the perception of orientation that correlate with the pattern of activity in the primary visual cortex of the human brain. These results indicate that prior expectations influence neural processing right from the earliest stage of the cortical hierarchy.

Multisensory Bayesian Inference Depends on Synapse Maturation during Training: Theoretical Analysis and Neural Modeling Implementation

Recent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding—the idea that a population of neurons can encode probability functions to perform Bayesian inference. The model consists of two chains of unisensory neurons (auditory and visual) topologically organized. They receive the corresponding input through a plastic receptive field and reciprocally exchange plastic cross-modal synapses, which encode the spatial co-occurrence of visual-auditory inputs. A third chain of multisensory neurons performs a simple sum of auditory and visual excitations.

The work includes a theoretical part and a computer simulation study. We show how a simple rule for synapse learning (consisting of Hebbian reinforcement and a decay term) can be used during training to shrink the receptive fields and encode the unisensory likelihood functions. Hence, after training, each unisensory area realizes a maximum likelihood estimate of stimulus position (auditory or visual). In cross-modal conditions, the same learning rule can encode information on prior probability into the cross-modal synapses. Computer simulations confirm the theoretical results and show that the proposed network can realize a maximum likelihood estimate of auditory (or visual) positions in unimodal conditions and a Bayesian estimate, with moderate deviations from optimality, in cross-modal conditions. Furthermore, the model explains the ventriloquism illusion and, looking at the activity in the multimodal neurons, explains the automatic reweighting of auditory and visual inputs on a trial-by-trial basis, according to the reliability of the individual cues.

Optimal nonlinear cue integration for sound localization

Integration of multiple sensory cues can improve performance in detection and estimation tasks. There is an open theoretical question of the conditions under which linear or nonlinear cue combination is Bayes-optimal. We demonstrate that a neural population decoded by a population vector requires nonlinear cue combination to approximate Bayesian inference. Specifically, if cues are conditionally independent, multiplicative cue combination is optimal for the population vector. The model was tested on neural and behavioral responses in the barn owl's sound localization system where space-specific neurons owe their selectivity to multiplicative tuning to sound localization cues interaural phase (IPD) and level (ILD) differences. We found that IPD and ILD cues are approximately conditionally independent. As a result, the multiplicative combination selectivity to IPD and ILD of midbrain space-specific neurons permits a population vector to perform Bayesian cue combination. We further show that this model describes the owl's localization behavior in azimuth and elevation. This work provides theoretical justification and experimental evidence supporting the optimality of nonlinear cue combination.

Integer Ratio Priors on Musical Rhythm Revealed Cross-culturally by Iterated Reproduction

Probability distributions over external states (priors) are essential to the interpretation of sensory signals. Priors for cultural artifacts such as music and language remain largely uncharacterized, but likely constrain cultural transmission, because only those signals with high probability under the prior can be reliably reproduced and communicated. We developed a method to estimate priors for simple rhythms via iterated reproduction of random temporal sequences. Listeners were asked to reproduce random "seed" rhythms; their reproductions were fed back as the stimulus and over time became dominated by internal biases, such that the prior could be estimated by applying the procedure multiple times. We validated that the measured prior was consistent across the modality of reproduction and that it correctly predicted perceptual discrimination. We then measured listeners' priors over the entire space of two- and three-interval rhythms. Priors in US participants showed peaks at rhythms with simple integer ratios and were similar for musicians and non-musicians. An analogous procedure produced qualitatively different results for spoken phrases, indicating some specificity to music. Priors measured in members of a native Amazonian society were distinct from those in US participants but also featured integer ratio peaks. The results do not preclude biological constraints favoring integer ratios, but they suggest that priors on musical rhythm are substantially modulated by experience and may simply reflect the empirical distribution of rhythm that listeners encounter. The proposed method can efficiently map out a high-resolution view of biases that shape transmission and stability of simple reproducible patterns within a culture.

Stimulus-specific adaptation to visual but not auditory motion direction in the barn owl's optic tectum

Whether the auditory and visual systems use a similar coding strategy to represent motion direction is an open question. We investigated this question in the barn owl's optic tectum (OT) testing stimulus-specific adaptation (SSA) to the direction of motion. SSA, the reduction of the response to a repetitive stimulus that does not generalize to other stimuli, has been well established in OT neurons. SSA suggests a separate representation of the adapted stimulus in upstream pathways. So far, only SSA to static stimuli has been studied in the OT. Here, we examined adaptation to moving auditory and visual stimuli. SSA to motion direction was examined using repeated presentations of moving stimuli, occasionally switching motion to the opposite direction. Acoustic motion was either mimicked by varying binaural spatial cues or implemented in free field using a speaker array. While OT neurons displayed SSA to motion direction in visual space, neither stimulation paradigms elicited significant SSA to auditory motion direction. These findings show a qualitative difference in how auditory and visual motion is processed in the OT and support the existence of dedicated circuitry for representing motion direction in the early stages of visual but not the auditory system.

Statistics of natural reverberation enable perceptual separation of sound and space

In everyday listening, sound reaches our ears directly from a source as well as indirectly via reflections known as reverberation. Reverberation profoundly distorts the sound from a source, yet humans can both identify sound sources and distinguish environments from the resulting sound, via mechanisms that remain unclear. The core computational challenge is that the acoustic signatures of the source and environment are combined in a single signal received by the ear. Here we ask whether our recognition of sound sources and spaces reflects an ability to separate their effects and whether any such separation is enabled by statistical regularities of real-world reverberation. To first determine whether such statistical regularities exist, we measured impulse responses (IRs) of 271 spaces sampled from the distribution encountered by humans during daily life. The sampled spaces were diverse, but their IRs were tightly constrained, exhibiting exponential decay at frequency-dependent rates: Mid frequencies reverberated longest whereas higher and lower frequencies decayed more rapidly, presumably due to absorptive properties of materials and air. To test whether humans leverage these regularities, we manipulated IR decay characteristics in simulated reverberant audio. Listeners could discriminate sound sources and environments from these signals, but their abilities degraded when reverberation characteristics deviated from those of real-world environments. Subjectively, atypical IRs were mistaken for sound sources. The results suggest the brain separates sound into contributions from the source and the environment, constrained by a prior on natural reverberation. This separation process may contribute to robust recognition while providing information about spaces around us.

A novel relay nucleus between the inferior colliculus and the optic tectum in the chicken (Gallus gallus)

Processing multimodal sensory information is vital for behaving animals in many contexts. The barn owl, an auditory specialist, is a classic model for studying multisensory integration. In the barn owl, spatial auditory information is conveyed to the optic tectum (TeO) by a direct projection from the external nucleus of the inferior colliculus (ICX). In contrast, evidence of an integration of visual and auditory information in auditory generalist avian species is completely lacking. In particular, it is not known whether in auditory generalist species the ICX projects to the TeO at all. Here we use various retrograde and anterograde tracing techniques both in vivo and in vitro, intracellular fillings of neurons in vitro, and whole-cell patch recordings to characterize the connectivity between ICX and TeO in the chicken. We found that there is a direct projection from ICX to the TeO in the chicken, although this is small and only to the deeper layers (layers 13-15) of the TeO. However, we found a relay area interposed among the IC, the TeO, and the isthmic complex that receives strong synaptic input from the ICX and projects broadly upon the intermediate and deep layers of the TeO. This area is an external portion of the formatio reticularis lateralis (FRLx). In addition to the projection to the TeO, cells in FRLx send, via collaterals, descending projections through tectopontine-tectoreticular pathways. This newly described connection from the inferior colliculus to the TeO provides a solid basis for visual-auditory integration in an auditory generalist bird. J. Comp. Neurol. 525:513-534, 2017. © 2016 Wiley Periodicals, Inc.