Functional groups in the avian auditory system

A Complex Acoustical Environment During Development Enhances Auditory Perception and Coding Efficiency in the Zebra Finch

Sensory experience during development has lasting effects on perception and neural processing. Exposing juvenile animals to artificial stimuli influences the tuning and functional organization of the auditory cortex, but less is known about how the rich acoustical environments experienced by vocal communicators affect the processing of complex vocalizations. Here, we show that in zebra finches (Taeniopygia guttata), a colonial-breeding songbird species, exposure to a naturalistic social-acoustical environment during development has a profound impact on auditory perceptual behavior and on cortical-level auditory responses to conspecific song. Compared to birds raised by pairs in acoustic isolation, male and female birds raised in a breeding colony were better in an operant discrimination task at recognizing conspecific songs with and without masking colony noise. Neurons in colony-reared birds had higher average firing rates, selectivity, and discriminability, especially in the narrow-spiking, putatively inhibitory neurons of a higher-order auditory area, the caudomedial nidopallium (NCM). Neurons in colony-reared birds were also less correlated in their tuning, more efficient at encoding the spectrotemporal structure of conspecific song, and better at filtering out masking noise. These results suggest that the auditory cortex adapts to noisy, complex acoustical environments by strengthening inhibitory circuitry, functionally decoupling excitatory neurons while maintaining overall excitatory-inhibitory balance.

A complex acoustical environment during development enhances auditory perception and coding efficiency in the zebra finch

Sensory experience during development has lasting effects on perception and neural processing. Exposing juvenile animals to artificial stimuli influences the tuning and functional organization of the auditory cortex, but less is known about how the rich acoustical environments experienced by vocal communicators affect the processing of complex vocalizations. Here, we show that in zebra finches (Taeniopygia guttata), a colonial-breeding songbird species, exposure to a naturalistic social-acoustical environment during development has a profound impact on auditory perceptual behavior and on cortical-level auditory responses to conspecific song. Compared to birds raised by pairs in acoustic isolation, male and female birds raised in a breeding colony were better in an operant discrimination task at recognizing conspecific songs with and without masking colony noise. Neurons in colony-reared birds had higher average firing rates, selectivity, and discriminability, especially in the narrow-spiking, putatively inhibitory neurons of a higher-order auditory area, the caudomedial nidopallium (NCM). Neurons in colony-reared birds were also less correlated in their tuning and more efficient at encoding the spectrotemporal structure of conspecific song, and better at filtering out masking noise. These results suggest that the auditory cortex adapts to noisy, complex acoustical environments by strengthening inhibitory circuitry, functionally decoupling excitatory neurons while maintaining overall excitatory-inhibitory balance.

Estimating Mutual Information for Spike Trains: A Bird Song Example

Zebra finches are a model animal used in the study of audition. They are adept at recognizing zebra finch songs, and the neural pathway involved in song recognition is well studied. Here, this example is used to illustrate the estimation of mutual information between stimuli and responses using a Kozachenko-Leonenko estimator. The challenge in calculating mutual information for spike trains is that there are no obvious coordinates for the data. The Kozachenko-Leonenko estimator does not require coordinates; it relies only on the distance between data points. In the case of bird songs, estimating the mutual information demonstrates that the information content of spiking does not diminish as the song progresses.

Partial inactivation of songbird auditory cortex impairs both tempo and pitch discrimination

Neuronal tuning for spectral and temporal features has been studied extensively in the auditory system. In the auditory cortex, diverse combinations of spectral and temporal tuning have been found, but how specific feature tuning contributes to the perception of complex sounds remains unclear. Neurons in the avian auditory cortex are spatially organized in terms of spectral or temporal tuning widths, providing an opportunity for investigating the link between auditory tuning and perception. Here, using naturalistic conspecific vocalizations, we asked whether subregions of the auditory cortex that are tuned for broadband sounds are more important for discriminating tempo than pitch, due to the lower frequency selectivity. We found that bilateral inactivation of the broadband region impairs performance on both tempo and pitch discrimination. Our results do not support the hypothesis that the lateral, more broadband subregion of the songbird auditory cortex contributes more to processing temporal than spectral information.

Sparse ensemble neural code for a complete vocal repertoire

The categorization of animal vocalizations into distinct behaviorally relevant groups for communication is an essential operation that must be performed by the auditory system. This auditory object recognition is a difficult task that requires selectivity to the group identifying acoustic features and invariance to renditions within each group. We find that small ensembles of auditory neurons in the forebrain of a social songbird can code the bird's entire vocal repertoire (∼10 call types). Ensemble neural discrimination is not, however, correlated with single unit selectivity, but instead with how well the joint single unit tunings to characteristic spectro-temporal modulations span the acoustic subspace optimized for the discrimination of call types. Thus, akin to face recognition in the visual system, call type recognition in the auditory system is based on a sparse code representing a small number of high-level features and not on highly selective grandmother neurons.

Surround suppression in mouse auditory cortex underlies auditory edge detection

Surround suppression (SS) is a fundamental property of sensory processing throughout the brain. In the auditory system, the early processing stream encodes sounds using a one dimensional physical space-frequency. Previous studies in the auditory system have shown SS to manifest as bandwidth tuning around the preferred frequency. We asked whether bandwidth tuning can be found around frequencies away from the preferred frequency. We exploited the simplicity of spectral representation of sounds to study SS by manipulating both sound frequency and bandwidth. We recorded single unit spiking activity from the auditory cortex (ACx) of awake mice in response to an array of broadband stimuli with varying central frequencies and bandwidths. Our recordings revealed that a significant portion of neuronal response profiles had a preferred bandwidth that varied in a regular way with the sound's central frequency. To gain insight into the possible mechanism underlying these responses, we modelled neuronal activity using a variation of the "Mexican hat" function often used to model SS. The model accounted for response properties of single neurons with high accuracy. Our data and model show that these responses in ACx obey simple rules resulting from the presence of lateral inhibitory sidebands, mostly above the excitatory band of the neuron, that result in sensitivity to the location of top frequency edges, invariant to other spectral attributes. Our work offers a simple explanation for auditory edge detection and possibly other computations of spectral content in sounds.

Aromatase and nonaromatase neurons in the zebra finch secondary auditory forebrain are indistinct in their song-driven gene induction and intrinsic electrophysiological properties

Estrogens support major brain functions including cognition, reproduction, neuroprotection and sensory processing. Neuroestrogens are synthesized within some brain areas by the enzyme aromatase and can rapidly modulate local circuit functions, yet the cellular physiology and sensory-response profiles of aromatase neurons are essentially unknown. In songbirds, social and acoustic stimuli drive neuroestrogen elevations in the auditory forebrain caudomedial nidopallium (NCM). In both males and females, neuroestrogens rapidly enhance NCM auditory processing and auditory learning. Estrogen-producing neurons in NCM may therefore exhibit distinguishing profiles for sensory-activation and intrinsic electrophysiology. Here, we explored these questions using both immunocyctochemistry and electrophysiological recordings. Immunoreactivity for aromatase and the immediate early gene EGR1, a marker of activity and plasticity, were quantified in NCM of song-exposed animals versus silence-exposed controls. Using whole-cell patch clamp recordings from NCM slices, we also documented the intrinsic excitability profiles of aromatase-positive and aromatase-negative neurons. We observed that a subset of aromatase neurons were significantly activated during song playback, in both males and females, and in both hemispheres. A comparable population of non-aromatase-expressing neurons were also similarly driven by song stimulation. Membrane properties (i.e., resting membrane potential, rheobase, input resistance and multiple action potential parameters) were similarly indistinguishable between NCM aromatase and non-aromatase neurons. Together, these findings demonstrate that aromatase and non-aromatase neurons in NCM are indistinct in terms of their intrinsic electrophysiology and responses to song. Nevertheless, such similarities in response properties may belie more subtle differences in underlying conductances and/or computational roles that may be crucial to their function.

Nonlinear effects of intrinsic dynamics on temporal encoding in a model of avian auditory cortex

Neurons exhibit diverse intrinsic dynamics, which govern how they integrate synaptic inputs to produce spikes. Intrinsic dynamics are often plastic during development and learning, but the effects of these changes on stimulus encoding properties are not well known. To examine this relationship, we simulated auditory responses to zebra finch song using a linear-dynamical cascade model, which combines a linear spectrotemporal receptive field with a dynamical, conductance-based neuron model, then used generalized linear models to estimate encoding properties from the resulting spike trains. We focused on the effects of a low-threshold potassium current (K_LT) that is present in a subset of cells in the zebra finch caudal mesopallium and is affected by early auditory experience. We found that K_LT affects both spike adaptation and the temporal filtering properties of the receptive field. The direction of the effects depended on the temporal modulation tuning of the linear (input) stage of the cascade model, indicating a strongly nonlinear relationship. These results suggest that small changes in intrinsic dynamics in tandem with differences in synaptic connectivity can have dramatic effects on the tuning of auditory neurons.

Experience-dependent developmental plasticity involves changes not only to synaptic connections, but to voltage-gated currents as well. Using biophysical models, it is straightforward to predict the effects of this intrinsic plasticity on the firing patterns of individual neurons, but it remains difficult to understand the consequences for sensory coding. We investigated this in the context of the zebra finch auditory cortex, where early exposure to a complex acoustic environment causes increased expression of a low-threshold potassium current. We simulated responses to song using a detailed biophysical model and then characterized encoding properties using generalized linear models. This analysis revealed that this potassium current has strong, nonlinear effects on how the model encodes the song’s temporal structure, and that the sign of these effects depend on the temporal tuning of the synaptic inputs. This nonlinearity gives intrinsic plasticity broad scope as a mechanism for developmental learning in the auditory system.

Auditory processing in the zebra finch midbrain: single unit responses and effect of rearing experience

In birds the auditory system plays a key role in providing the sensory input used to discriminate between conspecific and heterospecific vocal signals. In those species that are known to learn their vocalizations, for example, songbirds, it is generally considered that this ability arises and is manifest in the forebrain, although there is no a priori reason why brainstem components of the auditory system could not also play an important part. To test this assumption, we used groups of normal reared and cross-fostered zebra finches that had previously been shown in behavioural experiments to reduce their preference for conspecific songs subsequent to cross fostering experience with Bengalese finches, a related species with a distinctly different song. The question we asked, therefore, is whether this experiential change also changes the bias in favour of conspecific song displayed by auditory midbrain units of normally raised zebra finches. By recording the responses of single units in MLd to a variety of zebra finch and Bengalese finch songs in both normally reared and cross-fostered zebra finches, we provide a positive answer to this question. That is, the difference in response to conspecific and heterospecific songs seen in normal reared zebra finches is reduced following cross-fostering. In birds the virtual absence of mammalian-like cortical projections upon auditory brainstem nuclei argues against the interpretation that MLd units change, as observed in the present experiments, as a result of top-down influences on sensory processing. Instead, it appears that MLd units can be influenced significantly by sensory inputs arising directly from a change in auditory experience during development.

Effect of Stimulus-Dependent Spike Timing on Population Coding of Sound Location in the Owl's Auditory Midbrain

In the auditory system, the spectrotemporal structure of acoustic signals determines the temporal pattern of spikes. Here, we investigated this effect in neurons of the barn owl's auditory midbrain (Tyto furcata) that are selective for auditory space and whether it can influence the coding of sound direction. We found that in the nucleus where neurons first become selective to combinations of sound localization cues, reproducibility of spike trains across repeated trials of identical sounds, a metric of across-trial temporal fidelity of spiking patterns evoked by a stimulus, was maximal at the sound direction that elicited the highest firing rate. We then tested the hypothesis that this stimulus-dependent patterning resulted in rate co-modulation of cells with similar frequency and spatial selectivity, driving stimulus-dependent synchrony of population responses. Tetrodes were used to simultaneously record multiple nearby units in the optic tectum (OT), where auditory space is topographically represented. While spiking of neurons in OT showed lower reproducibility across trials compared with upstream nuclei, spike-time synchrony between nearby OT neurons was highest for sounds at their preferred direction. A model of the midbrain circuit explained the relationship between stimulus-dependent reproducibility and synchrony, and demonstrated that this effect can improve the decoding of sound location from the OT output. Thus, stimulus-dependent spiking patterns in the auditory midbrain can have an effect on spatial coding. This study reports a functional connection between spike patterning elicited by spectrotemporal features of a sound and the coding of its location.

Do songbirds hear songs syllable by syllable?

Songbirds as vocal learners have been one of the most popular model species to investigate the biological prerequisite to human language. Their songs consist of syllables, which appear as pulse trains in sound spectrograms. When describing the song sequence, researchers consider the syllable to be the unit of the song. Moreover, artificial grammar learning studies asking whether songbirds recognize structural regularities observed in human language often design stimuli using song syllables as components. However, whether syllables are perceptual units is yet to be determined. We found that Bengalese finches, a species of songbird, responded significantly less to one specific syllable when it was temporally placed close to the preceding syllable. The proximity, or silent interval was within the range of what is produced in the natural songs of both Bengalese and zebra finches, and what has been used in other artificial grammar learning studies using zebra finches. Our results suggest the need for a reinterpretation of the description of birdsong structure and of previous artificial grammar learning studies.

Invariant neural responses for sensory categories revealed by the time-varying information for communication calls

Although information theoretic approaches have been used extensively in the analysis of the neural code, they have yet to be used to describe how information is accumulated in time while sensory systems are categorizing dynamic sensory stimuli such as speech sounds or visual objects. Here, we present a novel method to estimate the cumulative information for stimuli or categories. We further define a time-varying categorical information index that, by comparing the information obtained for stimuli versus categories of these same stimuli, quantifies invariant neural representations. We use these methods to investigate the dynamic properties of avian cortical auditory neurons recorded in zebra finches that were listening to a large set of call stimuli sampled from the complete vocal repertoire of this species. We found that the time-varying rates carry 5 times more information than the mean firing rates even in the first 100 ms. We also found that cumulative information has slow time constants (100–600 ms) relative to the typical integration time of single neurons, reflecting the fact that the behaviorally informative features of auditory objects are time-varying sound patterns. When we correlated firing rates and information values, we found that average information correlates with average firing rate but that higher-rates found at the onset response yielded similar information values as the lower-rates found in the sustained response: the onset and sustained response of avian cortical auditory neurons provide similar levels of independent information about call identity and call-type. Finally, our information measures allowed us to rigorously define categorical neurons; these categorical neurons show a high degree of invariance for vocalizations within a call-type. Peak invariance is found around 150 ms after stimulus onset. Surprisingly, call-type invariant neurons were found in both primary and secondary avian auditory areas.

Just as the recognition of faces requires neural representations that are invariant to scale and rotation, the recognition of behaviorally relevant auditory objects, such as spoken words, requires neural representations that are invariant to the speaker uttering the word and to his or her location. Here, we used information theory to investigate the time course of the neural representation of bird communication calls and of behaviorally relevant categories of these same calls: the call-types of the bird’s repertoire. We found that neurons in both the primary and secondary avian auditory cortex exhibit invariant responses to call renditions within a call-type, suggestive of a potential role for extracting the meaning of these communication calls. We also found that time plays an important role: first, neural responses carry significantly more information when represented by temporal patterns calculated at the small time scale of 10 ms than when measured as average rates and, second, this information accumulates in a non-redundant fashion up to long integration times of 600 ms. This rich temporal neural representation is matched to the temporal richness found in the communication calls of this species.

A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex

Birdsong is a complex vocal communication signal, and like humans, birds need to discriminate between similar sequences of sound with different meanings. The caudal mesopallium (CM) is a cortical-level auditory area implicated in song discrimination. CM neurons respond sparsely to conspecific song and are tolerant of production variability. Intracellular recordings in CM have identified a diversity of intrinsic membrane dynamics, which could contribute to the emergence of these higher-order functional properties. We investigated this hypothesis using a novel linear-dynamical cascade model that incorporated detailed biophysical dynamics to simulate auditory responses to birdsong. Neuron models that included a low-threshold potassium current present in a subset of CM neurons showed increased selectivity and coding efficiency relative to models without this current. These results demonstrate the impact of intrinsic dynamics on sensory coding and the importance of including the biophysical characteristics of neural populations in simulation studies.

Maintaining a stable mental representation of an object is an important task for sensory systems, requiring both recognizing the features required for identification and ignoring incidental changes in its presentation. The prevailing explanation for these processes emphasizes precise sets of connections between neurons that capture only the essential features of an object. However, the intrinsic dynamics of the neurons themselves, which determine how these inputs are transformed into spiking outputs, may also contribute to the neural computations underlying object recognition. To understand how intrinsic dynamics contribute to sensory coding, we constructed a computational model capable of simulating a neural response to an auditory stimulus using a detailed description of different intrinsic dynamics in a higher-order avian auditory area. The results of our simulation showed that intrinsic dynamics can have a profound effect on processes underlying object recognition. These findings challenge the view that patterns of connectivity alone account for the emergence of stable object representations and encourage greater consideration of the functional implications of the diversity of neurons in the brain.

STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds

We investigate how the neural processing in auditory cortex is shaped by the statistics of natural sounds. Hypothesising that auditory cortex (A1) represents the structural primitives out of which sounds are composed, we employ a statistical model to extract such components. The input to the model are cochleagrams which approximate the non-linear transformations a sound undergoes from the outer ear, through the cochlea to the auditory nerve. Cochleagram components do not superimpose linearly, but rather according to a rule which can be approximated using the max function. This is a consequence of the compression inherent in the cochleagram and the sparsity of natural sounds. Furthermore, cochleagrams do not have negative values. Cochleagrams are therefore not matched well by the assumptions of standard linear approaches such as sparse coding or ICA. We therefore consider a new encoding approach for natural sounds, which combines a model of early auditory processing with maximal causes analysis (MCA), a sparse coding model which captures both the non-linear combination rule and non-negativity of the data. An efficient truncated EM algorithm is used to fit the MCA model to cochleagram data. We characterize the generative fields (GFs) inferred by MCA with respect to in vivo neural responses in A1 by applying reverse correlation to estimate spectro-temporal receptive fields (STRFs) implied by the learned GFs. Despite the GFs being non-negative, the STRF estimates are found to contain both positive and negative subfields, where the negative subfields can be attributed to explaining away effects as captured by the applied inference method. A direct comparison with ferret A1 shows many similar forms, and the spectral and temporal modulation tuning of both ferret and model STRFs show similar ranges over the population. In summary, our model represents an alternative to linear approaches for biological auditory encoding while it captures salient data properties and links inhibitory subfields to explaining away effects.

The information carried by natural sounds enters the cortex of mammals in a specific format: the cochleagram. Instead of representing the original pressure waveforms, the inner ear represents how the energy in a sound is distributed across frequency bands and how the energy distribution evolves over time. The generation of cochleagrams is highly non-linear resulting in the dominance of one sound source per time-frequency bin under natural conditions (masking). Auditory cortex is believed to decompose cochleagrams into structural primitives, i.e., reappearing regular spectro-temporal subpatterns that make up cochleagram patterns (similar to edges in images). However, such a decomposition has so far only been modeled without considering masking and non-negativity. Here we apply a novel non-linear sparse coding model that can capture masking non-linearities and non-negativities. When trained on cochleagrams of natural sounds, the model gives rise to an encoding primarily based-on spectro-temporally localized components. If stimulated by a sound, the encoding units compete to explain its contents. The competition is a direct consequence of the statistical sound model, and it results in neural responses being best described by spectro-temporal receptive fields (STRFs) with positive and negative subfields. The emerging STRFs show a higher similarity to experimentally measured STRFs than a model without masking, which provides evidence for cortical encoding being consistent with the masking based sound statistics of cochleagrams. Furthermore, and more generally, our study suggests for the first time that negative subfields of STRFs may be direct evidence for explaining away effects resulting from performing inference in an underlying statistical model.

Neural code: Another breach in the wall?

Brette presents arguments that query the existence of the neural code. However, he has neglected certain evidence that could be viewed as proof that a neural code operates in the brain. Albeit these proofs show a link between neural activity and cognition, we discuss why they fail to demonstrate the existence of an invariant neural code.

Binaural responses in the auditory midbrain of chicken (Gallus gallus)

The auditory midbrain is the location in which neurons represent binaural acoustic information necessary for sound localization. The external nucleus of the midbrain inferior colliculus (IC) of the barn owl is a classic example of an auditory space map, but it is unknown to what extent the principles underlying its formation generalize to other, less specialized animals. We characterized the spiking responses of 139 auditory neurons in the IC of the chicken (Gallus gallus) in vivo, focusing on their sensitivities to the binaural localization cues of interaural time (ITD) and level (ILD) differences. Most units were frequency-selective, with best frequencies distributed unevenly into low-frequency and high-frequency (> 2 kHz) clusters. Many units showed sensitivity to either ITD (65%) or ILD (66%) and nearly half to both (47%). ITD selectivity was disproportionately more common among low-frequency units, while ILD-only selective units were predominantly tuned to high frequencies. ILD sensitivities were diverse, and we thus developed a decision tree defining five types. One rare type with a bell-like ILD tuning was also selective for ITD but typically not frequency-selective, and thus matched the characteristics of neurons in the auditory space map of the barn owl. Our results suggest that generalist birds such as the chicken show a prominent representation of ITD and ILD cues in the IC, providing complementary information for sound localization, according to the duplex theory. A broadband response type narrowly selective for both ITD and ILD may form the basis for a representation of auditory space.

Phasic and tonic cell types in the zebra finch auditory caudal mesopallium

The caudal mesopallium (CM) is a cortical-level area in the songbird auditory pathway where selective, invariant responses to familiar songs emerge. To characterize the cell types that perform this computation, we made whole cell recordings from brain slices in juvenile zebra finches ( Taeniopygia guttata) of both sexes. We found three groups of putatively excitatory neurons with distinct firing patterns. Tonic cells produced sustained responses to depolarizing step currents, phasic cells produced only a few spikes at the onset, and an intermediate group was also phasic but responded for up to a few hundred milliseconds. Phasic cells had smaller dendritic fields, higher resting potentials, and strong low-threshold outward rectification. Pharmacological treatment with voltage-gated potassium channel antagonists 4-aminopyridine and α-dendrotoxin converted phasic to tonic firing. When stimulated with broadband currents, phasic cells fired coherently with frequencies up to 20-30 Hz, whereas tonic neurons were more responsive to frequencies around 0-10 Hz. The distribution of peak coherence frequencies was similar to the distribution of temporal modulation rates in zebra finch song. We reproduced these observations in a single-compartment biophysical model by varying cell size and the magnitude of a slowly inactivating, low-threshold potassium current ( I_LT). These data suggest that intrinsic dynamics in CM are matched to the temporal statistics of conspecific song. NEW & NOTEWORTHY In songbirds, the caudal mesopallium is a key brain area involved in recognizing the songs of other individuals. This study identifies three cell types in this area with distinct firing patterns (tonic, phasic, and intermediate) that reflect differences in cell size and a low-threshold potassium current. The phasic-firing neurons, which do not have a counterpart in mammalian auditory cortex, are better able to follow rapid modulations at the frequencies found in song.

Encoding and Decoding Models in Cognitive Electrophysiology

Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of "Encoding" models, in which stimulus features are used to model brain activity, and "Decoding" models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses.

The Hierarchical Cortical Organization of Human Speech Processing

Speech comprehension requires that the brain extract semantic meaning from the spectral features represented at the cochlea. To investigate this process, we performed an fMRI experiment in which five men and two women passively listened to several hours of natural narrative speech. We then used voxelwise modeling to predict BOLD responses based on three different feature spaces that represent the spectral, articulatory, and semantic properties of speech. The amount of variance explained by each feature space was then assessed using a separate validation dataset. Because some responses might be explained equally well by more than one feature space, we used a variance partitioning analysis to determine the fraction of the variance that was uniquely explained by each feature space. Consistent with previous studies, we found that speech comprehension involves hierarchical representations starting in primary auditory areas and moving laterally on the temporal lobe: spectral features are found in the core of A1, mixtures of spectral and articulatory in STG, mixtures of articulatory and semantic in STS, and semantic in STS and beyond. Our data also show that both hemispheres are equally and actively involved in speech perception and interpretation. Further, responses as early in the auditory hierarchy as in STS are more correlated with semantic than spectral representations. These results illustrate the importance of using natural speech in neurolinguistic research. Our methodology also provides an efficient way to simultaneously test multiple specific hypotheses about the representations of speech without using block designs and segmented or synthetic speech.SIGNIFICANCE STATEMENT To investigate the processing steps performed by the human brain to transform natural speech sound into meaningful language, we used models based on a hierarchical set of speech features to predict BOLD responses of individual voxels recorded in an fMRI experiment while subjects listened to natural speech. Both cerebral hemispheres were actively involved in speech processing in large and equal amounts. Also, the transformation from spectral features to semantic elements occurs early in the cortical speech-processing stream. Our experimental and analytical approaches are important alternatives and complements to standard approaches that use segmented speech and block designs, which report more laterality in speech processing and associated semantic processing to higher levels of cortex than reported here.

Rapid tuning shifts in human auditory cortex enhance speech intelligibility

Experience shapes our perception of the world on a moment-to-moment basis. This robust perceptual effect of experience parallels a change in the neural representation of stimulus features, though the nature of this representation and its plasticity are not well-understood. Spectrotemporal receptive field (STRF) mapping describes the neural response to acoustic features, and has been used to study contextual effects on auditory receptive fields in animal models. We performed a STRF plasticity analysis on electrophysiological data from recordings obtained directly from the human auditory cortex. Here, we report rapid, automatic plasticity of the spectrotemporal response of recorded neural ensembles, driven by previous experience with acoustic and linguistic information, and with a neurophysiological effect in the sub-second range. This plasticity reflects increased sensitivity to spectrotemporal features, enhancing the extraction of more speech-like features from a degraded stimulus and providing the physiological basis for the observed ‘perceptual enhancement' in understanding speech.

Experience constantly shapes perception, but the neural mechanisms of this rapid plasticity are unclear. Here, Holdgraf et al. record neural activity in the human auditory cortex and show that listening to normal speech elicits rapid plasticity that increases the neural gain for features of sound that are key for speech intelligibility.

Direct Viewing of Dyslexics' Compensatory Strategies in Speech in Noise Using Auditory Classification Images

A vast majority of dyslexic children exhibit a phonological deficit, particularly noticeable in phonemic identification or discrimination tasks. The gap in performance between dyslexic and normotypical listeners appears to decrease into adulthood, suggesting that some individuals with dyslexia develop compensatory strategies. Some dyslexic adults however remain impaired in more challenging listening situations such as in the presence of background noise. This paper addresses the question of the compensatory strategies employed, using the recently developed Auditory Classification Image (ACI) methodology. The results of 18 dyslexics taking part in a phoneme categorization task in noise were compared with those of 18 normotypical age-matched controls. By fitting a penalized Generalized Linear Model on the data of each participant, we obtained his/her ACI, a map of the time-frequency regions he/she relied on to perform the task. Even though dyslexics performed significantly less well than controls, we were unable to detect a robust difference between the mean ACIs of the two groups. This is partly due to the considerable heterogeneity in listening strategies among a subgroup of 7 low-performing dyslexics, as confirmed by a complementary analysis. When excluding these participants to restrict our comparison to the 11 dyslexics performing as well as their average-reading peers, we found a significant difference in the F3 onset of the first syllable, and a tendency of difference on the F4 onset, suggesting that these listeners can compensate for their deficit by relying upon additional allophonic cues.

The vocal repertoire of the domesticated zebra finch: a data-driven approach to decipher the information-bearing acoustic features of communication signals

Although a universal code for the acoustic features of animal vocal communication calls may not exist, the thorough analysis of the distinctive acoustical features of vocalization categories is important not only to decipher the acoustical code for a specific species but also to understand the evolution of communication signals and the mechanisms used to produce and understand them. Here, we recorded more than 8000 examples of almost all the vocalizations of the domesticated zebra finch, Taeniopygia guttata: vocalizations produced to establish contact, to form and maintain pair bonds, to sound an alarm, to communicate distress or to advertise hunger or aggressive intents. We characterized each vocalization type using complete representations that avoided any a priori assumptions on the acoustic code, as well as classical bioacoustics measures that could provide more intuitive interpretations. We then used these acoustical features to rigorously determine the potential information-bearing acoustical features for each vocalization type using both a novel regularized classifier and an unsupervised clustering algorithm. Vocalization categories are discriminated by the shape of their frequency spectrum and by their pitch saliency (noisy to tonal vocalizations) but not particularly by their fundamental frequency. Notably, the spectral shape of zebra finch vocalizations contains peaks or formants that vary systematically across categories and that would be generated by active control of both the vocal organ (source) and the upper vocal tract (filter).

A Framework for Speech Activity Detection Using Adaptive Auditory Receptive Fields

One of the hallmarks of sound processing in the brain is the ability of the nervous system to adapt to changing behavioral demands and surrounding soundscapes. It can dynamically shift sensory and cognitive resources to focus on relevant sounds. Neurophysiological studies indicate that this ability is supported by adaptively retuning the shapes of cortical spectro-temporal receptive fields (STRFs) to enhance features of target sounds while suppressing those of task-irrelevant distractors. Because an important component of human communication is the ability of a listener to dynamically track speech in noisy environments, the solution obtained by auditory neurophysiology implies a useful adaptation strategy for speech activity detection (SAD). SAD is an important first step in a number of automated speech processing systems, and performance is often reduced in highly noisy environments. In this paper, we describe how task-driven adaptation is induced in an ensemble of neurophysiological STRFs, and show how speech-adapted STRFs reorient themselves to enhance spectro-temporal modulations of speech while suppressing those associated with a variety of nonspeech sounds. We then show how an adapted ensemble of STRFs can better detect speech in unseen noisy environments compared to an unadapted ensemble and a noise-robust baseline. Finally, we use a stimulus reconstruction task to demonstrate how the adapted STRF ensemble better captures the spectrotemporal modulations of attended speech in clean and noisy conditions. Our results suggest that a biologically plausible adaptation framework can be applied to speech processing systems to dynamically adapt feature representations for improving noise robustness.

A single microphone noise reduction algorithm based on the detection and reconstruction of spectro-temporal features

Animals throughout the animal kingdom excel at extracting individual sounds from competing background sounds, yet current state-of-the-art signal processing algorithms struggle to process speech in the presence of even modest background noise. Recent psychophysical experiments in humans and electrophysiological recordings in animal models suggest that the brain is adapted to process sounds within the restricted domain of spectro-temporal modulations found in natural sounds. Here, we describe a novel single microphone noise reduction algorithm called spectro-temporal detection–reconstruction (STDR) that relies on an artificial neural network trained to detect, extract and reconstruct the spectro-temporal features found in speech. STDR can significantly reduce the level of the background noise while preserving the foreground speech quality and improving estimates of speech intelligibility. In addition, by leveraging the strong temporal correlations present in speech, the STDR algorithm can also operate on predictions of upcoming speech features, retaining similar performance levels while minimizing inherent throughput delays. STDR performs better than a competing state-of-the-art algorithm for a wide range of signal-to-noise ratios and has the potential for real-time applications such as hearing aids and automatic speech recognition.

Exposure to a novel stimulus environment alters patterns of lateralization in avian auditory cortex

Perceptual filters formed early in development provide an initial means of parsing the incoming auditory stream. However, these filters may not remain fixed, and may be updated by subsequent auditory input, such that, even in an adult organism, the auditory system undergoes plastic changes to achieve a more efficient representation of the recent auditory environment. Songbirds are an excellent model system for experimental studies of auditory phenomena due to many parallels between song learning in birds and language acquisition in humans. In the present study, we explored the effects of passive immersion in a novel heterospecific auditory environment on neural responses in caudo-medial neostriatum (NCM), a songbird auditory area similar to the secondary auditory cortex in mammals. In zebra finches, a well-studied species of songbirds, NCM responds selectively to conspecific songs and contains a neuronal memory for tutor and other familiar conspecific songs. Adult male zebra finches were randomly assigned to either a conspecific or heterospecific auditory environment. After 2, 4 or 9 days of exposure, subjects were presented with heterospecific and conspecific songs during awake electrophysiological recording. The neural response strength and rate of adaptation to the testing stimuli were recorded bilaterally. Controls exposed to conspecific environment sounds exhibited the normal pattern of hemispheric lateralization with higher absolute response strength and faster adaptation in the right hemisphere. The pattern of lateralization was fully reversed in birds exposed to heterospecific environment for 4 or 9 days and partially reversed in birds exposed to heterospecific environment for 2 days. Our results show that brief passive exposure to a novel category of sounds was sufficient to induce a gradual reorganization of the left and right secondary auditory cortices. These changes may reflect modification of perceptual filters to form a more efficient representation of auditory space.

Recent evidence for rapid synthesis and action of oestrogens during auditory processing in a songbird

It is now clear that oestrogens are not only circulating reproductive hormones, but that they also have neurotransmitter-like properties in a wide range of brain circuits. The view of oestrogens as intrinsic neuromodulators that shape behaviour has been bolstered by a series of recent developments from multiple vertebrate model systems. Here, we review several recent findings from studies of songbirds showing how the identified neural circuits that govern auditory processing and sensorimotor integration are modulated by the local and acute production of oestrogens. First, studies using in vivo microdialysis demonstrate that oestrogens fluctuate in the auditory cortex (30-min time bin resolution) when songbirds are hearing song and interacting with conspecifics. Second, oestrogens rapidly boost the auditory-evoked activity of neurones in the same auditory cortical region, enhancing auditory processing. Third, local pharmacological blockade of oestrogen signalling in this region impairs auditory neuronal responsiveness, as well as behavioural song preferences. Fourth, the rapid actions of oestrogens that occur within the auditory cortex can propagate downstream (trans-synaptically) to sensorimotor circuits to enhance the neural representation of song. Lastly, we present new evidence showing that the receptor for the rapid actions of oestradiol is likely in neuronal membranes, and that traditional nuclear oestrogen receptor agonists do not mimic these rapid actions. Broadly speaking, many of these findings are observed in both males and females, emphasising the fundamental importance of oestrogens in neural circuit function. Together, these and other emergent studies provide support for rapid, brain-derived oestrogen signalling in regulating sensorimotor integration, learning and perception.

Conserved mechanisms of vocalization coding in mammalian and songbird auditory midbrain

The ubiquity of social vocalizations among animals provides the opportunity to identify conserved mechanisms of auditory processing that subserve communication. Identifying auditory coding properties that are shared across vocal communicators will provide insight into how human auditory processing leads to speech perception. Here, we compare auditory response properties and neural coding of social vocalizations in auditory midbrain neurons of mammalian and avian vocal communicators. The auditory midbrain is a nexus of auditory processing because it receives and integrates information from multiple parallel pathways and provides the ascending auditory input to the thalamus. The auditory midbrain is also the first region in the ascending auditory system where neurons show complex tuning properties that are correlated with the acoustics of social vocalizations. Single unit studies in mice, bats and zebra finches reveal shared principles of auditory coding including tonotopy, excitatory and inhibitory interactions that shape responses to vocal signals, nonlinear response properties that are important for auditory coding of social vocalizations and modulation tuning. Additionally, single neuron responses in the mouse and songbird midbrain are reliable, selective for specific syllables, and rely on spike timing for neural discrimination of distinct vocalizations. We propose that future research on auditory coding of vocalizations in mouse and songbird midbrain neurons adopt similar experimental and analytical approaches so that conserved principles of vocalization coding may be distinguished from those that are specialized for each species. This article is part of a Special Issue entitled "Communication Sounds and the Brain: New Directions and Perspectives".

Frequency response areas in the inferior colliculus: nonlinearity and binaural interaction

The tuning, binaural properties, and encoding characteristics of neurons in the central nucleus of the inferior colliculus (CNIC) were investigated to shed light on nonlinearities in the responses of these neurons. Results were analyzed for three types of neurons (I, O, and V) in the CNIC of decerebrate cats. Rate responses to binaural stimuli were characterized using a 1st- plus 2nd-order spectral integration model. Parameters of the model were derived using broadband stimuli with random spectral shapes (RSS). This method revealed four characteristics of CNIC neurons: (1) Tuning curves derived from broadband stimuli have fixed (i. e., level tolerant) bandwidths across a 50-60 dB range of sound levels; (2) 1st-order contralateral weights (particularly for type I and O neurons) were usually larger in magnitude than corresponding ipsilateral weights; (3) contralateral weights were more important than ipsilateral weights when using the model to predict responses to untrained noise stimuli; and (4) 2nd-order weight functions demonstrate frequency selectivity different from that of 1st-order weight functions. Furthermore, while the inclusion of 2nd-order terms in the model usually improved response predictions related to untrained RSS stimuli, they had limited impact on predictions related to other forms of filtered broadband noise [e. g., virtual-space stimuli (VS)]. The accuracy of the predictions varied considerably by response type. Predictions were most accurate for I neurons, and less accurate for O and V neurons, except at the lowest stimulus levels. These differences in prediction performance support the idea that type I, O, and V neurons encode different aspects of the stimulus: while type I neurons are most capable of producing linear representations of spectral shape, type O and V neurons may encode spectral features or temporal stimulus properties in a manner not easily explained with the low-order model. Supported by NIH grant DC00115.

Selective and efficient neural coding of communication signals depends on early acoustic and social environment

Previous research has shown that postnatal exposure to simple, synthetic sounds can affect the sound representation in the auditory cortex as reflected by changes in the tonotopic map or other relatively simple tuning properties, such as AM tuning. However, their functional implications for neural processing in the generation of ethologically-based perception remain unexplored. Here we examined the effects of noise-rearing and social isolation on the neural processing of communication sounds such as species-specific song, in the primary auditory cortex analog of adult zebra finches. Our electrophysiological recordings reveal that neural tuning to simple frequency-based synthetic sounds is initially established in all the laminae independent of patterned acoustic experience; however, we provide the first evidence that early exposure to patterned sound statistics, such as those found in native sounds, is required for the subsequent emergence of neural selectivity for complex vocalizations and for shaping neural spiking precision in superficial and deep cortical laminae, and for creating efficient neural representations of song and a less redundant ensemble code in all the laminae. Our study also provides the first causal evidence for ‘sparse coding’, such that when the statistics of the stimuli were changed during rearing, as in noise-rearing, that the sparse or optimal representation for species-specific vocalizations disappeared. Taken together, these results imply that a layer-specific differential development of the auditory cortex requires patterned acoustic input, and a specialized and robust sensory representation of complex communication sounds in the auditory cortex requires a rich acoustic and social environment.

Sustained firing of model central auditory neurons yields a discriminative spectro-temporal representation for natural sounds

The processing characteristics of neurons in the central auditory system are directly shaped by and reflect the statistics of natural acoustic environments, but the principles that govern the relationship between natural sound ensembles and observed responses in neurophysiological studies remain unclear. In particular, accumulating evidence suggests the presence of a code based on sustained neural firing rates, where central auditory neurons exhibit strong, persistent responses to their preferred stimuli. Such a strategy can indicate the presence of ongoing sounds, is involved in parsing complex auditory scenes, and may play a role in matching neural dynamics to varying time scales in acoustic signals. In this paper, we describe a computational framework for exploring the influence of a code based on sustained firing rates on the shape of the spectro-temporal receptive field (STRF), a linear kernel that maps a spectro-temporal acoustic stimulus to the instantaneous firing rate of a central auditory neuron. We demonstrate the emergence of richly structured STRFs that capture the structure of natural sounds over a wide range of timescales, and show how the emergent ensembles resemble those commonly reported in physiological studies. Furthermore, we compare ensembles that optimize a sustained firing code with one that optimizes a sparse code, another widely considered coding strategy, and suggest how the resulting population responses are not mutually exclusive. Finally, we demonstrate how the emergent ensembles contour the high-energy spectro-temporal modulations of natural sounds, forming a discriminative representation that captures the full range of modulation statistics that characterize natural sound ensembles. These findings have direct implications for our understanding of how sensory systems encode the informative components of natural stimuli and potentially facilitate multi-sensory integration.

Noise-invariant neurons in the avian auditory cortex: hearing the song in noise

Given the extraordinary ability of humans and animals to recognize communication signals over a background of noise, describing noise invariant neural responses is critical not only to pinpoint the brain regions that are mediating our robust perceptions but also to understand the neural computations that are performing these tasks and the underlying circuitry. Although invariant neural responses, such as rotation-invariant face cells, are well described in the visual system, high-level auditory neurons that can represent the same behaviorally relevant signal in a range of listening conditions have yet to be discovered. Here we found neurons in a secondary area of the avian auditory cortex that exhibit noise-invariant responses in the sense that they responded with similar spike patterns to song stimuli presented in silence and over a background of naturalistic noise. By characterizing the neurons' tuning in terms of their responses to modulations in the temporal and spectral envelope of the sound, we then show that noise invariance is partly achieved by selectively responding to long sounds with sharp spectral structure. Finally, to demonstrate that such computations could explain noise invariance, we designed a biologically inspired noise-filtering algorithm that can be used to separate song or speech from noise. This novel noise-filtering method performs as well as other state-of-the-art de-noising algorithms and could be used in clinical or consumer oriented applications. Our biologically inspired model also shows how high-level noise-invariant responses could be created from neural responses typically found in primary auditory cortex.

Birds and humans excel at the task of detecting important sounds, such as song and speech, in difficult listening environments such as in a large bird colony or in a crowded bar. How our brains achieve such a feat remains a mystery to both neuroscientists and audio engineers. In our research, we found a population of neurons in the brain of songbirds that are able to extract a song signal from a background of noise. We explain how the neurons are able to perform this task and show how a biologically inspired algorithm could outperform the best noise-reduction methods proposed by engineers.

A simple algorithm for averaging spike trains

Although spike trains are the principal channel of communication between neurons, a single stimulus will elicit different spike trains from trial to trial. This variability, in both spike timings and spike number can obscure the temporal structure of spike trains and often means that computations need to be run on numerous spike trains in order to extract features common across all the responses to a particular stimulus. This can increase the computational burden and obscure analytical results. As a consequence, it is useful to consider how to calculate a central spike train that summarizes a set of trials. Indeed, averaging responses over trials is routine for other signal types. Here, a simple method for finding a central spike train is described. The spike trains are first mapped to functions, these functions are averaged, and a greedy algorithm is then used to map the average function back to a spike train. The central spike trains are tested for a large data set. Their performance on a classification-based test is considerably better than the performance of the medoid spike trains.

Emergence of selectivity and tolerance in the avian auditory cortex

The ability to recognize auditory objects like words and bird songs is thought to depend on neural responses that are selective between categories of the objects and tolerant of variation within those categories. To determine whether a hierarchy of increasing selectivity and tolerance exists in the avian auditory system, we trained European starlings (Sturnus vulgaris) to differentially recognize sets of songs, then measured extracellular single unit responses under urethane anesthesia in six areas of the auditory cortex. Responses were analyzed with a novel, generalized linear mixed model that provides robust estimates of the variance in responses to different stimuli. There were significant differences between areas in selectivity, tolerance, and the effects of training. The L2b and L1 subdivisions of field L had the least selectivity and tolerance. The caudal nidopallium (NCM) and subdivision L3 of field L were more selective than other areas, whereas the medial and lateral caudal mesopallium were more tolerant than NCM or L2b. L3 had a multimodal distribution of tolerance. Sensitivity to songs that were familiar and those that were not also distinguished the responses of caudomedial mesopallium and NCM. There were significant differences across areas between neurons with wide and narrow spikes. Collectively these results do not fit the traditional hierarchical view of the avian auditory forebrain, but are consistent with emerging concepts homologizing avian cortical and neocortical circuitry. The results suggest a functional divergence within the cortex into processing streams that respond to complementary aspects of the variability in communicative sounds.

Acoustic structure of the five perceptual dimensions of timbre in orchestral instrument tones

Attempts to relate the perceptual dimensions of timbre to quantitative acoustical dimensions have been tenuous, leading to claims that timbre is an emergent property, if measurable at all. Here, a three-pronged analysis shows that the timbre space of sustained instrument tones occupies 5 dimensions and that a specific combination of acoustic properties uniquely determines gestalt perception of timbre. Firstly, multidimensional scaling (MDS) of dissimilarity judgments generated a perceptual timbre space in which 5 dimensions were cross-validated and selected by traditional model comparisons. Secondly, subjects rated tones on semantic scales. A discriminant function analysis (DFA) accounting for variance of these semantic ratings across instruments and between subjects also yielded 5 significant dimensions with similar stimulus ordination. The dimensions of timbre space were then interpreted semantically by rotational and reflectional projection of the MDS solution into two DFA dimensions. Thirdly, to relate this final space to acoustical structure, the perceptual MDS coordinates of each sound were regressed with its joint spectrotemporal modulation power spectrum. Sound structures correlated significantly with distances in perceptual timbre space. Contrary to previous studies, most perceptual timbre dimensions are not the result of purely temporal or spectral features but instead depend on signature spectrotemporal patterns.

Early experience shapes vocal neural coding and perception in songbirds

Songbirds, like humans, are highly accomplished vocal learners. The many parallels between speech and birdsong and conserved features of mammalian and avian auditory systems have led to the emergence of the songbird as a model system for studying the perceptual mechanisms of vocal communication. Laboratory research on songbirds allows the careful control of early life experience and high-resolution analysis of brain function during vocal learning, production, and perception. Here, I review what songbird studies have revealed about the role of early experience in the development of vocal behavior, auditory perception, and the processing of learned vocalizations by auditory neurons. The findings of these studies suggest general principles for how exposure to vocalizations during development and into adulthood influences the perception of learned vocal signals.

Spectrotemporal processing in spectral tuning modules of cat primary auditory cortex

Spectral integration properties show topographical order in cat primary auditory cortex (AI). Along the iso-frequency domain, regions with predominantly narrowly tuned (NT) neurons are segregated from regions with more broadly tuned (BT) neurons, forming distinct processing modules. Despite their prominent spatial segregation, spectrotemporal processing has not been compared for these regions. We identified these NT and BT regions with broad-band ripple stimuli and characterized processing differences between them using both spectrotemporal receptive fields (STRFs) and nonlinear stimulus/firing rate transformations. The durations of STRF excitatory and inhibitory subfields were shorter and the best temporal modulation frequencies were higher for BT neurons than for NT neurons. For NT neurons, the bandwidth of excitatory and inhibitory subfields was matched, whereas for BT neurons it was not. Phase locking and feature selectivity were higher for NT neurons. Properties of the nonlinearities showed only slight differences across the bandwidth modules. These results indicate fundamental differences in spectrotemporal preferences - and thus distinct physiological functions - for neurons in BT and NT spectral integration modules. However, some global processing aspects, such as spectrotemporal interactions and nonlinear input/output behavior, appear to be similar for both neuronal subgroups. The findings suggest that spectral integration modules in AI differ in what specific stimulus aspects are processed, but they are similar in the manner in which stimulus information is processed.

The sender-receiver matching hypothesis: support from the peripheral coding of acoustic features in songbirds

The sender-receiver matching hypothesis predicts that species-specific features of vocalizations will be reflected in species-specific auditory processing. This hypothesis has most often been invoked to explain correlations between vocal frequency ranges and the frequency range of auditory sensitivity; however it could apply to other structure features, such as the rise time of stimuli. We explored this hypothesis in five songbird species that vary in the rise times and frequency range of their vocalizations. We recorded auditory evoked potentials (AEPs) to onset and sustained portions of stimuli that varied in both frequency and rise time. AEPs are gross potentials generated in the auditory nerve and brainstem and measured from the scalp. We found that species with shorter rise times in their vocalizations had greater amplitude and shorter latency onset AEPs than species with longer rise times. We also found that species with lower frequency and/or more tonal vocalizations had stronger sustained AEPs that follow the sound pressure changes in the stimulus (i.e. frequency following responses) than species with higher frequency and/or less tonal vocalizations. This is the first study in songbirds to show that acoustic features such as rise time and tonality are reflected in peripheral auditory processing.

Linear and nonlinear auditory response properties of interneurons in a high-order avian vocal motor nucleus during wakefulness

Motor-related forebrain areas in higher vertebrates also show responses to passively presented sensory stimuli. However, sensory tuning properties in these areas, especially during wakefulness, and their relation to perception, are poorly understood. In the avian song system, HVC (proper name) is a vocal-motor structure with auditory responses well defined under anesthesia but poorly characterized during wakefulness. We used a large set of stimuli including the bird's own song (BOS) and many conspecific songs (CON) to characterize auditory tuning properties in putative interneurons (HVC(IN)) during wakefulness. Our findings suggest that HVC contains a diversity of responses that vary in overall excitability to auditory stimuli, as well as bias in spike rate increases to BOS over CON. We used statistical tests to classify cells in order to further probe auditory responses, yielding one-third of neurons that were either unresponsive or suppressed and two-thirds with excitatory responses to one or more stimuli. A subset of excitatory neurons were tuned exclusively to BOS and showed very low linearity as measured by spectrotemporal receptive field analysis (STRF). The remaining excitatory neurons responded well to CON stimuli, although many cells still expressed a bias toward BOS. These findings suggest the concurrent presence of a nonlinear and a linear component to responses in HVC, even within the same neuron. These characteristics are consistent with perceptual deficits in distinguishing BOS from CON stimuli following lesions of HVC and other song nuclei and suggest mirror neuronlike qualities in which "self" (here BOS) is used as a referent to judge "other" (here CON).

A behavioral framework to guide research on central auditory development and plasticity

The auditory CNS is influenced profoundly by sounds heard during development. Auditory deprivation and augmented sound exposure can each perturb the maturation of neural computations as well as their underlying synaptic properties. However, we have learned little about the emergence of perceptual skills in these same model systems, and especially how perception is influenced by early acoustic experience. Here, we argue that developmental studies must take greater advantage of behavioral benchmarks. We discuss quantitative measures of perceptual development and suggest how they can play a much larger role in guiding experimental design. Most importantly, including behavioral measures will allow us to establish empirical connections among environment, neural development, and perception.

Extra-classical tuning predicts stimulus-dependent receptive fields in auditory neurons

The receptive fields of many sensory neurons are sensitive to statistical differences among classes of complex stimuli. For example, excitatory spectral bandwidths of midbrain auditory neurons and the spatial extent of cortical visual neurons differ during the processing of natural stimuli compared to the processing of artificial stimuli. Experimentally characterizing neuronal nonlinearities that contribute to stimulus-dependent receptive fields is important for understanding how neurons respond to different stimulus classes in multiple sensory modalities. Here we show that in the zebra finch, many auditory midbrain neurons have extra-classical receptive fields, consisting of sideband excitation and sideband inhibition. We also show that the presence, degree, and asymmetry of stimulus-dependent receptive fields during the processing of complex sounds are predicted by the presence, valence, and asymmetry of extra-classical tuning. Neurons for which excitatory bandwidth expands during the processing of song have extra-classical excitation. Neurons for which frequency tuning is static and for which excitatory bandwidth contracts during the processing of song have extra-classical inhibition. Simulation experiments further demonstrate that stimulus-dependent receptive fields can arise from extra-classical tuning with a static spike threshold nonlinearity. These findings demonstrate that a common neuronal nonlinearity can account for the stimulus dependence of receptive fields estimated from the responses of auditory neurons to stimuli with natural and non-natural statistics.

An efficient coding hypothesis links sparsity and selectivity of neural responses

To what extent are sensory responses in the brain compatible with first-order principles? The efficient coding hypothesis projects that neurons use as few spikes as possible to faithfully represent natural stimuli. However, many sparsely firing neurons in higher brain areas seem to violate this hypothesis in that they respond more to familiar stimuli than to nonfamiliar stimuli. We reconcile this discrepancy by showing that efficient sensory responses give rise to stimulus selectivity that depends on the stimulus-independent firing threshold and the balance between excitatory and inhibitory inputs. We construct a cost function that enforces minimal firing rates in model neurons by linearly punishing suprathreshold synaptic currents. By contrast, subthreshold currents are punished quadratically, which allows us to optimally reconstruct sensory inputs from elicited responses. We train synaptic currents on many renditions of a particular bird's own song (BOS) and few renditions of conspecific birds' songs (CONs). During training, model neurons develop a response selectivity with complex dependence on the firing threshold. At low thresholds, they fire densely and prefer CON and the reverse BOS (REV) over BOS. However, at high thresholds or when hyperpolarized, they fire sparsely and prefer BOS over REV and over CON. Based on this selectivity reversal, our model suggests that preference for a highly familiar stimulus corresponds to a high-threshold or strong-inhibition regime of an efficient coding strategy. Our findings apply to songbird mirror neurons, and in general, they suggest that the brain may be endowed with simple mechanisms to rapidly change selectivity of neural responses to focus sensory processing on either familiar or nonfamiliar stimuli. In summary, we find support for the efficient coding hypothesis and provide new insights into the interplay between the sparsity and selectivity of neural responses.

Estradiol-dependent modulation of auditory processing and selectivity in songbirds

The steroid hormone estradiol plays an important role in reproductive development and behavior and modulates a wide array of physiological and cognitive processes. Recently, reports from several research groups have converged to show that estradiol also powerfully modulates sensory processing, specifically, the physiology of central auditory circuits in songbirds. These investigators have discovered that (1) behaviorally-relevant auditory experience rapidly increases estradiol levels in the auditory forebrain; (2) estradiol instantaneously enhances the responsiveness and coding efficiency of auditory neurons; (3) these changes are mediated by a non-genomic effect of brain-generated estradiol on the strength of inhibitory neurotransmission; and (4) estradiol regulates biochemical cascades that induce the expression of genes involved in synaptic plasticity. Together, these findings have established estradiol as a central regulator of auditory function and intensified the need to consider brain-based mechanisms, in addition to peripheral organ dysfunction, in hearing pathologies associated with estrogen deficiency.

Anesthetic state modulates excitability but not spectral tuning or neural discrimination in single auditory midbrain neurons

The majority of sensory physiology experiments have used anesthesia to facilitate the recording of neural activity. Current techniques allow researchers to study sensory function in the context of varying behavioral states. To reconcile results across multiple behavioral and anesthetic states, it is important to consider how and to what extent anesthesia plays a role in shaping neural response properties. The role of anesthesia has been the subject of much debate, but the extent to which sensory coding properties are altered by anesthesia has yet to be fully defined. In this study we asked how urethane, an anesthetic commonly used for avian and mammalian sensory physiology, affects the coding of complex communication vocalizations (songs) and simple artificial stimuli in the songbird auditory midbrain. We measured spontaneous and song-driven spike rates, spectrotemporal receptive fields, and neural discriminability from responses to songs in single auditory midbrain neurons. In the same neurons, we recorded responses to pure tone stimuli ranging in frequency and intensity. Finally, we assessed the effect of urethane on population-level representations of birdsong. Results showed that intrinsic neural excitability is significantly depressed by urethane but that spectral tuning, single neuron discriminability, and population representations of song do not differ significantly between unanesthetized and anesthetized animals.