Sparse and background-invariant coding of vocalizations in auditory scenes

Spiking network optimized for word recognition in noise predicts auditory system hierarchy

The auditory neural code is resilient to acoustic variability and capable of recognizing sounds amongst competing sound sources, yet, the transformations enabling noise robust abilities are largely unknown. We report that a hierarchical spiking neural network (HSNN) optimized to maximize word recognition accuracy in noise and multiple talkers predicts organizational hierarchy of the ascending auditory pathway. Comparisons with data from auditory nerve, midbrain, thalamus and cortex reveals that the optimal HSNN predicts several transformations of the ascending auditory pathway including a sequential loss of temporal resolution and synchronization ability, increasing sparseness, and selectivity. The optimal organizational scheme enhances performance by selectively filtering out noise and fast temporal cues such as voicing periodicity, that are not directly relevant to the word recognition task. An identical network arranged to enable high information transfer fails to predict auditory pathway organization and has substantially poorer performance. Furthermore, conventional single-layer linear and nonlinear receptive field networks that capture the overall feature extraction of the HSNN fail to achieve similar performance. The findings suggest that the auditory pathway hierarchy and its sequential nonlinear feature extraction computations enhance relevant cues while removing non-informative sources of noise, thus enhancing the representation of sounds in noise impoverished conditions.

The brain’s ability to recognize sounds in the presence of competing sounds or background noise is essential for everyday hearing tasks. How the brain accomplishes noise resiliency, however, is poorly understood. Using neural recordings from the ascending auditory pathway and an auditory spiking network model trained for sound recognition in noise we explore the computational strategies that enable noise robustness. Our results suggest that the hierarchical feature organization of the ascending auditory pathway and the resulting computations are critical for sound recognition in the presence of noise.

Noise-Sensitive But More Precise Subcortical Representations Coexist with Robust Cortical Encoding of Natural Vocalizations

Humans and animals maintain accurate sound discrimination in the presence of loud sources of background noise. It is commonly assumed that this ability relies on the robustness of auditory cortex responses. However, only a few attempts have been made to characterize neural discrimination of communication sounds masked by noise at each stage of the auditory system and to quantify the noise effects on the neuronal discrimination in terms of alterations in amplitude modulations. Here, we measured neural discrimination between communication sounds masked by a vocalization-shaped stationary noise from multiunit responses recorded in the cochlear nucleus, inferior colliculus, auditory thalamus, and primary and secondary auditory cortex at several signal-to-noise ratios (SNRs) in anesthetized male or female guinea pigs. Masking noise decreased sound discrimination of neuronal populations in each auditory structure, but collicular and thalamic populations showed better performance than cortical populations at each SNR. In contrast, in each auditory structure, discrimination by neuronal populations was slightly decreased when tone-vocoded vocalizations were tested. These results shed new light on the specific contributions of subcortical structures to robust sound encoding, and suggest that the distortion of slow amplitude modulation cues conveyed by communication sounds is one of the factors constraining the neuronal discrimination in subcortical and cortical levels.SIGNIFICANCE STATEMENT Dissecting how auditory neurons discriminate communication sounds in noise is a major goal in auditory neuroscience. Robust sound coding in noise is often viewed as a specific property of cortical networks, although this remains to be demonstrated. Here, we tested the discrimination performance of neuronal populations at five levels of the auditory system in response to conspecific vocalizations masked by noise. In each acoustic condition, subcortical neurons better discriminated target vocalizations than cortical ones and in each structure, the reduction in discrimination performance was related to the reduction in slow amplitude modulation cues.

Statistical learning of transition patterns in the songbird auditory forebrain

Statistical learning of transition patterns between sounds—a striking capability of the auditory system—plays an essential role in animals’ survival (e.g., detect deviant sounds that signal danger). However, the neural mechanisms underlying this capability are still not fully understood. We recorded extracellular multi-unit and single-unit activity in the auditory forebrain of awake male zebra finches while presenting rare repetitions of a single sound in a long sequence of sounds (canary and zebra finch song syllables) patterned in either an alternating or random order at different inter-stimulus intervals (ISI). When preceding stimuli were regularly alternating (alternating condition), a repeated stimulus violated the preceding transition pattern and was a deviant. When preceding stimuli were in random order (control condition), a repeated stimulus did not violate any regularities and was not a deviant. At all ISIs tested (1 s, 3 s, or jittered at 0.8–1.2 s), deviant repetition enhanced neural responses in the alternating condition in a secondary auditory area (caudomedial nidopallium, NCM) but not in the primary auditory area (Field L2); in contrast, repetition suppressed responses in the control condition in both Field L2 and NCM. When stimuli were presented in the classical oddball paradigm at jittered ISI (0.8–1.2 s), neural responses in both NCM and Field L2 were stronger when a stimulus occurred as deviant with low probability than when the same stimulus occurred as standard with high probability. Together, these results demonstrate: (1) classical oddball effect exists even when ISI is jittered and the onset of a stimulus is not fully predictable; (2) neurons in NCM can learn transition patterns between sounds at multiple ISIs and detect violation of these transition patterns; (3) sensitivity to deviant sounds increases from Field L2 to NCM in the songbird auditory forebrain. Further studies using the current paradigms may help us understand the neural substrate of statistical learning and even speech comprehension.

Experience-Dependent Coding of Time-Dependent Frequency Trajectories by Off Responses in Secondary Auditory Cortex

Time-dependent frequency trajectories are an inherent feature of many behaviorally relevant sounds, such as species-specific vocalizations. Dynamic frequency trajectories, even in short sounds, often convey meaningful information, which may be used to differentiate sound categories. However, it is not clear what and where neural responses in the auditory cortical pathway are critical for conveying information about behaviorally relevant frequency trajectories, and how these responses change with experience. Here, we uncover tuning to subtle variations in frequency trajectories in auditory cortex of female mice. We found that auditory cortical responses could be modulated by variations in a pure tone trajectory as small as 1/24th of an octave, comparable to what has been reported in primates. In particular, late spiking after the end of a sound stimulus was more often sensitive to the sound's subtle frequency variation compared with spiking during the sound. Such “Off” responses in the adult A2, but not those in core auditory cortex, were plastic in a way that may enhance the representation of a newly acquired, behaviorally relevant sound category. We illustrate this with the maternal mouse paradigm for natural vocalization learning. By using an ethologically inspired paradigm to drive auditory responses in higher-order neurons, our results demonstrate that mouse auditory cortex can track fine frequency changes, which allows A2 Off responses in particular to better respond to pitch trajectories that distinguish behaviorally relevant, natural sound categories.

SIGNIFICANCE STATEMENT A whistle's pitch conveys meaning to its listener, as when dogs learn that distinct pitch trajectories whistled by their owner differentiate specific commands. Many species use pitch trajectories in their own vocalizations to distinguish sound categories, such as in human languages, such as Mandarin. How and where auditory neural activity encodes these pitch trajectories as their meaning is learned but not well understood, especially for short-duration sounds. We studied this in mice, where infants use ultrasonic whistles to communicate to adults. We found that late neural firing after a sound ends can be tuned to how the pitch changes in time, and that this response in a secondary auditory cortical field changes with experience to acquire a pitch change's meaning.

Neural Synchrony Gives Rise to Amplitude- and Duration-Invariant Encoding Consistent With Perception of Natural Communication Stimuli

When confronted with a highly variable environment, it remains poorly understood how neural populations encode and classify natural stimuli to give rise to appropriate and consistent behavioral responses. Here we investigated population coding of natural communication signals with different attributes (i.e., amplitude and duration) in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus. Our results show that, while single peripheral neurons encode the detailed timecourse of different stimulus waveforms, measures of population synchrony are effectively unchanged because of coordinated increases and decreases in activity. A phenomenological mathematical model reproduced this invariance and shows that this can be explained by considering homogeneous populations whose responses are solely determined by single neuron firing properties. Moreover, recordings from downstream central neurons reveal that synchronous afferent activity is actually decoded and thus most likely transmitted to higher brain areas. Finally, we demonstrate that the associated behavioral responses at the organism level are invariant. Our results provide a mechanism by which amplitude- and duration-invariant coding of behaviorally relevant sensory input emerges across successive brain areas thereby presumably giving rise to invariant behavioral responses. Such mechanisms are likely to be found in other systems that share anatomical and functional features with the electrosensory system (e.g., auditory, visual, vestibular).

Novel metrics to measure the effect of additive inputs on the activity of sensory system neurons

Sensory systems, such as the visual or auditory system, are highly non linear. It is therefore not easy to predict the effect of additive inputs on the spiking activity of related brain structures. Here, we propose two metrics to study the effect of additive covariates on the spiking activity of neurons. These metrics are directly obtained from a generalized linear model. We apply these metrics to the study of the effect of additive input audio noise on the spiking activity of neurons in the auditory system. To do so, we combine clean vocalisations with natural stationary or non-stationary noises and record activity in the auditory system while presenting the noisy vocalisations. We found that non-stationary noise has a greater effect on the neural activity than stationary noise. We observe that the results, obtained using the proposed metrics, is more consistent with current knowledge in auditory neuroscience than the results obtained when using a common metric from the literature, the extraction index.

Experience- and Sex-Dependent Intrinsic Plasticity in the Zebra Finch Auditory Cortex during Song Memorization

For vocal communicators like humans and songbirds, survival and reproduction depend on highly developed auditory processing systems that can detect and differentiate nuanced differences in vocalizations, even amid noisy environments. Early auditory experience is critical to the development of these systems. In zebra finches and other songbirds, there is a sensitive period when young birds memorize a song that will serve as a model for their own vocal production. In addition to learning a specific tutor's song, the auditory system may also undergo critical developmental processes that support auditory perception of vocalizations more generally. Here, we investigate changes in intrinsic spiking dynamics among neurons in the caudal mesopallium, a cortical-level auditory area implicated in discriminating and learning species-specific vocalizations. A subset of neurons in this area only fire transiently at the onset of current injections (i.e., phasic firing), a dynamical property that can enhance the reliability and selectivity of neural responses to complex acoustic stimuli. At the beginning of the sensitive period, just after zebra finches have fledged from the nest, there is an increase in the proportion of caudal mesopallium neurons with phasic excitability, and in the proportion of neurons expressing Kv1.1, a low-threshold channel that facilitates phasic firing. This plasticity requires exposure to a complex, noisy environment and is greater in males, the only sex that sings in this species. This shift to more phasic dynamics is therefore an experience-dependent adaptation that could facilitate auditory processing in noisy, acoustically complex conditions during a key stage of vocal development.SIGNIFICANCE STATEMENT Auditory experience early in life shapes how humans and songbirds perceive the vocal communication sounds produced by their species. However, the changes that occur in the brain as this learning takes place are poorly understood. In this study, we show that in young zebra finches that are just beginning to learn the structure of their species' song, neurons in a key cortical area adapt their intrinsic firing patterns in response to the acoustic environment. In the complex, cocktail-party-like environment of a colony, more neurons adopt transient firing dynamics, which can facilitate neural coding of songs amid such challenging conditions.

Age-Related Changes in Temporal Processing of Rapidly-Presented Sound Sequences in the Macaque Auditory Cortex

The mammalian auditory cortex is necessary to resolve temporal features in rapidly-changing sound streams. This capability is crucial for speech comprehension in humans and declines with normal aging. Nonhuman primate studies have revealed detrimental effects of normal aging on the auditory nervous system, and yet the underlying influence on temporal processing remains less well-defined. Therefore, we recorded from the core and lateral belt areas of auditory cortex when awake young and old monkeys listened to tone-pip and noise-burst sound sequences. Elevated spontaneous and stimulus-driven activity were the hallmark characteristics in old monkeys. These old neurons showed isomorphic-like discharge patterns to stimulus envelopes, though their phase-locking was less precise. Functional preference in temporal coding between the core and belt existed in the young monkeys but was mostly absent in the old monkeys, in which old belt neurons showed core-like response profiles. Finally, the analysis of population activity patterns indicated that the aged auditory cortex demonstrated a homogenous, distributed coding strategy, compared to the selective, sparse coding strategy observed in the young monkeys. Degraded temporal fidelity and highly-responsive, broadly-tuned cortical responses could underlie how aged humans have difficulties to resolve and track dynamic sounds leading to speech processing deficits.

Auditory Selectivity for Spectral Contrast in Cortical Neurons and Behavior

Vocal communication relies on the ability of listeners to identify, process, and respond to vocal sounds produced by others in complex environments. To accurately recognize these signals, animals' auditory systems must robustly represent acoustic features that distinguish vocal sounds from other environmental sounds. Vocalizations typically have spectral structure; power regularly fluctuates along the frequency axis, creating spectral contrast. Spectral contrast is closely related to harmonicity, which refers to spectral power peaks occurring at integer multiples of a fundamental frequency. Although both spectral contrast and harmonicity typify natural sounds, they may differ in salience for communication behavior and engage distinct neural mechanisms. Therefore, it is important to understand which of these properties of vocal sounds underlie the neural processing and perception of vocalizations.Here, we test the importance of vocalization-typical spectral features in behavioral recognition and neural processing of vocal sounds, using male zebra finches. We show that behavioral responses to natural and synthesized vocalizations rely on the presence of discrete frequency components, but not on harmonic ratios between frequencies. We identify a specific population of neurons in primary auditory cortex that are sensitive to the spectral resolution of vocal sounds. We find that behavioral and neural response selectivity is explained by sensitivity to spectral contrast rather than harmonicity. This selectivity emerges within the cortex; it is absent in the thalamorecipient region and present in the deep output region. Further, deep-region neurons that are contrast-sensitive show distinct temporal responses and selectivity for modulation density compared with unselective neurons.SIGNIFICANCE STATEMENT Auditory coding and perception are critical for vocal communication. Auditory neurons must encode acoustic features that distinguish vocalizations from other sounds in the environment and generate percepts that direct behavior. The acoustic features that drive neural and behavioral selectivity for vocal sounds are unknown, however. Here, we show that vocal response behavior scales with stimulus spectral contrast but not with harmonicity, in songbirds. We identify a distinct population of auditory cortex neurons in which response selectivity parallels behavioral selectivity. This neural response selectivity is explained by sensitivity to spectral contrast rather than to harmonicity. Our findings inform the understanding of how the auditory system encodes socially-relevant signals via detection of an acoustic feature that is ubiquitous in vocalizations.

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.

Invariance to background noise as a signature of non-primary auditory cortex

Despite well-established anatomical differences between primary and non-primary auditory cortex, the associated representational transformations have remained elusive. Here we show that primary and non-primary auditory cortex are differentiated by their invariance to real-world background noise. We measured fMRI responses to natural sounds presented in isolation and in real-world noise, quantifying invariance as the correlation between the two responses for individual voxels. Non-primary areas were substantially more noise-invariant than primary areas. This primary-nonprimary difference occurred both for speech and non-speech sounds and was unaffected by a concurrent demanding visual task, suggesting that the observed invariance is not specific to speech processing and is robust to inattention. The difference was most pronounced for real-world background noise-both primary and non-primary areas were relatively robust to simple types of synthetic noise. Our results suggest a general representational transformation between auditory cortical stages, illustrating a representational consequence of hierarchical organization in the auditory system.

Rapid and long-lasting improvements in neural discrimination of acoustic signals with passive familiarization

Sensory representations in the adult brain must undergo dynamic changes to adapt to the complexity of the external world. This study investigated how passive exposure to novel sounds modifies neural representations to facilitate recognition and discrimination, using the zebra finch model organism. The neural responses in an auditory structure in the zebra finch brain, Caudal Medial Nidopallium (NCM), undergo a long-term form of adaptation with repeated stimulus presentation, providing an excellent substrate to probe the neural underpinnings of adaptive sensory representations. In Experiment 1, electrophysiological activity in NCM was recorded under passive listening conditions as novel natural vocalizations were familiarized through playback. Neural decoding of stimuli using the temporal profiles of both single-unit and multi-unit responses improved dramatically during the first few stimulus presentations. During subsequent encounters, these signals were recognized after hearing fewer initial acoustic features. Remarkably, the accuracy of neural decoding was higher when different stimuli were heard in separate blocks compared to when they were presented randomly in a shuffled sequence. NCM neurons with narrow spike waveforms generally yielded higher neural decoding accuracy than wide spike neurons, but the rate at which these accuracies improved with passive exposure was comparable between the two neuron types. Experiment 2 supported and extended these findings by showing that the rapid gains in neural decoding of novel vocalizations with passive familiarization were long-lasting, maintained for 20 hours after the initial encounter, in multi-unit responses. Taken together, these findings provide valuable insights into the mechanisms by which the nervous system dynamically modulates sensory representations to improve discrimination of novel complex signals over short and long timescales. Similar mechanisms may also be engaged during processing of human speech signals, and thus may have potential translational relevance for elucidating the neural basis of speech comprehension difficulties.

Adaptation of the human auditory cortex to changing background noise

Speech communication in real-world environments requires adaptation to changing acoustic conditions. How the human auditory cortex adapts as a new noise source appears in or disappears from the acoustic scene remain unclear. Here, we directly measured neural activity in the auditory cortex of six human subjects as they listened to speech with abruptly changing background noises. We report rapid and selective suppression of acoustic features of noise in the neural responses. This suppression results in enhanced representation and perception of speech acoustic features. The degree of adaptation to different background noises varies across neural sites and is predictable from the tuning properties and speech specificity of the sites. Moreover, adaptation to background noise is unaffected by the attentional focus of the listener. The convergence of these neural and perceptual effects reveals the intrinsic dynamic mechanisms that enable a listener to filter out irrelevant sound sources in a changing acoustic scene.

Neuronal Encoding in a High-Level Auditory Area: From Sequential Order of Elements to Grammatical Structure

Sensitivity to the sequential structure of communication sounds is fundamental not only for language comprehension in humans but also for song recognition in songbirds. By quantifying single-unit responses, we first assessed whether the sequential order of song elements, called syllables, in conspecific songs is encoded in a secondary auditory cortex-like region of the zebra finch brain. Based on a habituation/dishabituation paradigm, we show that, after multiple repetitions of the same conspecific song, rearranging syllable order reinstated strong responses. A large proportion of neurons showed sensitivity to song context in which syllables occurred providing support for the nonlinear processing of syllable sequences. Sensitivity to the temporal order of items within a sequence should enable learning its underlying structure, an ability considered a core mechanism of the human language faculty. We show that repetitions of songs that were ordered according to a specific grammatical structure (i.e., ABAB or AABB structures; A and B denoting song syllables) led to different responses in both anesthetized and awake birds. Once responses were decreased due to song repetitions, the transition from one structure to the other could affect the firing rates and/or the spike patterns. Our results suggest that detection was based on local differences rather than encoding of the global song structure as a whole. Our study demonstrates that a high-level auditory region provides neuronal mechanisms to help discriminate stimuli that differ in their sequential structure.SIGNIFICANCE STATEMENT Sequence processing has been proposed as a potential precursor of language syntax. As a sequencing operation, the encoding of the temporal order of items within a sequence may help in recognition of relationships between adjacent items and in learning the underlying structure. Taking advantage of the stimulus-specific adaptation phenomenon observed in a high-level auditory region of the zebra finch brain, we addressed this question at the neuronal level. Reordering elements within conspecific songs reinstated robust responses. Neurons also detected changes in the structure of artificial songs, and this detection depended on local transitions between adjacent or nonadjacent syllables. These findings establish the songbird as a model system for deciphering the mechanisms underlying sequence processing at the single-cell level.

Acoustic Pattern Recognition and Courtship Songs: Insights from Insects

Across the animal kingdom, social interactions rely on sound production and perception. From simple cricket chirps to more elaborate bird songs, animals go to great lengths to communicate information critical for reproduction and survival via acoustic signals. Insects produce a wide array of songs to attract a mate, and the intended receivers must differentiate these calls from competing sounds, analyze the quality of the sender from spectrotemporal signal properties, and then determine how to react. Insects use numerically simple nervous systems to analyze and respond to courtship songs, making them ideal model systems for uncovering the neural mechanisms underlying acoustic pattern recognition. We highlight here how the combination of behavioral studies and neural recordings in three groups of insects-crickets, grasshoppers, and fruit flies-reveals common strategies for extracting ethologically relevant information from acoustic patterns and how these findings might translate to other systems.

A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex

The auditory pathway consists of multiple stages, from the cochlear nucleus to the auditory cortex. Neurons acting at different stages have different functions and exhibit different response properties. It is unclear whether these stages share a common encoding mechanism. We trained an unsupervised deep learning model consisting of alternating sparse coding and max pooling layers on cochleogram-filtered human speech. Evaluation of the response properties revealed that computing units in lower layers exhibited spectro-temporal receptive fields (STRFs) similar to those of inferior colliculus neurons measured in physiological experiments, including properties such as sound onset and termination, checkerboard pattern, and spectral motion. Units in upper layers tended to be tuned to phonetic features such as plosivity and nasality, resembling the results of field recording in human auditory cortex. Variation of the sparseness level of the units in each higher layer revealed a positive correlation between the sparseness level and the strength of phonetic feature encoding. The activities of the units in the top layer, but not other layers, correlated with the dynamics of the first two formants (F1, F2) of all phonemes, indicating the encoding of phoneme dynamics in these units. These results suggest that the principles of sparse coding and max pooling may be universal in the human auditory pathway.

When speech enters the ear, it is subjected to a series of processing stages prior to arriving at the auditory cortex. Neurons acting at different processing stages have different response properties. For example, at the auditory midbrain, a neuron may specifically detect the onsets of a frequency component in the speech, whereas in the auditory cortex, a neuron may specifically detect phonetic features. The encoding mechanisms underlying these neuronal functions remain unclear. To address this issue, we designed a hierarchical sparse coding model, inspired by the sparse activity of neurons in the sensory system, to learn features in speech signals. We found that the computing units in different layers exhibited hierarchical extraction of speech sound features, similar to those of neurons in the auditory midbrain and auditory cortex, although the computational principles in these layers were the same. The results suggest that sparse coding and max pooling represent universal computational principles throughout the auditory pathway.

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.

Response properties of single neurons in higher level auditory cortex of adult songbirds

The caudomedial nidopallium (NCM) is a higher level region of auditory cortex in songbirds that has been implicated in encoding learned vocalizations and mediating perception of complex sounds. We made cell-attached recordings in awake adult male zebra finches ( Taeniopygia guttata) to characterize responses of single NCM neurons to playback of tones and songs. Neurons fell into two broad classes: narrow fast-spiking cells and broad sparsely firing cells. Virtually all narrow-spiking cells responded to playback of pure tones, compared with approximately half of broad-spiking cells. In addition, narrow-spiking cells tended to have lower thresholds and faster, less variable spike onset latencies than did broad-spiking cells, as well as higher firing rates. Tonal responses of narrow-spiking cells also showed broader ranges for both frequency and amplitude compared with broad-spiking neurons and were more apt to have V-shaped tuning curves compared with broad-spiking neurons, which tended to have complex (discontinuous), columnar, or O-shaped frequency response areas. In response to playback of conspecific songs, narrow-spiking neurons showed high firing rates and low levels of selectivity whereas broad-spiking neurons responded sparsely and selectively. Broad-spiking neurons in which tones failed to evoke a response showed greater song selectivity compared with those with a clear tuning curve. These results are consistent with the idea that narrow-spiking neurons represent putative fast-spiking interneurons, which may provide a source of intrinsic inhibition that contributes to the more selective tuning in broad-spiking cells. NEW & NOTEWORTHY The response properties of neurons in higher level regions of auditory cortex in songbirds are of fundamental interest because processing in such regions is essential for vocal learning and plasticity and for auditory perception of complex sounds. Within a region of secondary auditory cortex, neurons with narrow spikes exhibited high firing rates to playback of both tones and multiple conspecific songs, whereas broad-spiking neurons responded sparsely and selectively to both tones and songs.

Neural Correlate of Transition Violation and Deviance Detection in the Songbird Auditory Forebrain

Deviants are stimuli that violate one's prediction about the incoming stimuli. Studying deviance detection helps us understand how nervous system learns temporal patterns between stimuli and forms prediction about the future. Detecting deviant stimuli is also critical for animals' survival in the natural environment filled with complex sounds and patterns. Using natural songbird vocalizations as stimuli, we recorded multi-unit and single-unit activity from the zebra finch auditory forebrain while presenting rare repeated stimuli after regular alternating stimuli (alternating oddball experiment) or rare deviant among multiple different common stimuli (context oddball experiment). The alternating oddball experiment showed that neurons were sensitive to rare repetitions in regular alternations. In the absence of expectation, repetition suppresses neural responses to the 2nd stimulus in the repetition. When repetition violates expectation, neural responses to the 2nd stimulus in the repetition were stronger than expected. The context oddball experiment showed that a stimulus elicits stronger neural responses when it is presented infrequently as a deviant among multiple common stimuli. As the acoustic differences between deviant and common stimuli increase, the response enhancement also increases. These results together showed that neural encoding of a stimulus depends not only on the acoustic features of the stimulus but also on the preceding stimuli and the transition patterns between them. These results also imply that the classical oddball effect may result from a combination of repetition suppression and deviance enhancement. Classification analyses showed that the difficulties in decoding the stimulus responsible for the neural responses differed for deviants in different experimental conditions. These findings suggest that learning transition patterns and detecting deviants in natural sequences may depend on a hierarchy of neural mechanisms, which may be involved in more complex forms of auditory processing that depend on the transition patterns between stimuli, such as speech processing.

Modeling Neural Adaptation in Auditory Cortex

Neural responses recorded from auditory cortex exhibit adaptation, a stimulus-specific decrease that occurs when the same sound is presented repeatedly. Stimulus-specific adaptation is thought to facilitate perception in noisy environments. Although adaptation is assumed to arise independently from cortex, this has been difficult to validate directly in vivo. In this study, we used a neural network model of auditory cortex with multicompartmental cell modeling to investigate cortical adaptation. We found that repetitive, non-adapted inputs to layer IV neurons in the model elicited frequency-specific decreases in simulated single neuron, population-level and local field potential (LFP) activity, consistent with stimulus-specific cortical adaptation. Simulated recordings of LFPs, generated solely by excitatory post-synaptic inputs and recorded from layers II/III in the model, showed similar waveform morphologies and stimulus probability effects as auditory evoked responses recorded from human cortex. We tested two proposed mechanisms of cortical adaptation, neural fatigue and neural sharpening, by varying the strength and type of inter- and intra-layer synaptic connections (excitatory, inhibitory). Model simulations showed that synaptic depression modeled in excitatory (AMPA) synapses was sufficient to elicit a reduction in neural firing rate, consistent with neural fatigue. However, introduction of lateral inhibition from local layer II/III interneurons resulted in a reduction in the number of responding neurons, but not their firing rates, consistent with neural sharpening. These modeling results demonstrate that adaptation can arise from multiple neural mechanisms in auditory cortex.

Zebra finches are sensitive to combinations of temporally distributed features in a model of word recognition

Discrimination between spoken words composed of overlapping elements, such as "captain" and "captive," relies on sensitivity to unique combinations of prefix and suffix elements that span a "uniqueness point" where the word candidates diverge. To model such combinatorial processing, adult female zebra finches were trained to discriminate between target and distractor syllable sequences that shared overlapping "contextual" prefixes and differed only in their "informative" suffixes. The transition from contextual to informative syllables thus created a uniqueness point analogous to that present between overlapping word candidates, where targets and distractors diverged. It was found that target recognition depended not only on informative syllables, but also on contextual syllables that were shared with distractors. Moreover, the influence of each syllable depended on proximity to the uniqueness point. Birds were then trained birds with targets and distractors that shared both prefix and suffix sequences and could only be discriminated by recognizing unique combinations of those sequences. Birds learned to robustly discriminate target and distractor combinations and maintained significant discrimination when the local transitions from prefix to suffix were disrupted. These findings indicate that birds, like humans, combine information across temporally distributed features, spanning contextual and informative elements, in recognizing and discriminating word-like stimuli.

Contextual modulation of sound processing in the auditory cortex

In everyday acoustic environments, we navigate through a maze of sounds that possess a complex spectrotemporal structure, spanning many frequencies and exhibiting temporal modulations that differ within frequency bands. Our auditory system needs to efficiently encode the same sounds in a variety of different contexts, while preserving the ability to separate complex sounds within an acoustic scene. Recent work in auditory neuroscience has made substantial progress in studying how sounds are represented in the auditory system under different contexts, demonstrating that auditory processing of seemingly simple acoustic features, such as frequency and time, is highly dependent on co-occurring acoustic and behavioral stimuli. Through a combination of electrophysiological recordings, computational analysis and behavioral techniques, recent research identified the interactions between external spectral and temporal context of stimuli, as well as the internal behavioral state.

A Membrane G-Protein-Coupled Estrogen Receptor Is Necessary but Not Sufficient for Sex Differences in Zebra Finch Auditory Coding

Estradiol acts as a neuromodulator in brain regions important for cognition and sensory processing. Estradiol also shapes brain sex differences but rarely have these concepts been considered simultaneously. In male and female songbirds, estradiol rapidly increases within the auditory forebrain during song exposure and enhances local auditory processing. We tested whether G-protein-coupled estrogen receptor 1 (GPER1), a membrane-bound estrogen receptor, is necessary and sufficient for neuroestrogen regulation of forebrain auditory processing in male and female zebra finches (Taeniopygia guttata). At baseline, we observed that females had elevated single-neuron responses to songs vs males. In males, narrow-spiking (NS) neurons were more responsive to conspecific songs than broad-spiking (BS) neurons, yet cell types were similarly auditory responsive in females. Following acute inactivation of GPER1, auditory responsiveness and coding were suppressed in male NS yet unchanged in female NS and in BS of both sexes. By contrast, GPER1 activation did not mimic previously established estradiol actions in either sex. Lastly, the expression of GPER1 and its coexpression with an inhibitory neuron marker were similarly abundant in males and females, confirming anatomical similarity in the auditory forebrain. In this study, we found: (1) a role for GPER1 in regulating sensory processing and (2) a sex difference in auditory processing of complex vocalizations in a cell type-specific manner. These results reveal sex specificity of a rapid estrogen signaling mechanism in which neuromodulation accounts and/or compensates for brain sex differences, dependent on cell type, in brain regions that are anatomically similar in both sexes.

Building a state space for song learning

The songbird system has shed light on how the brain produces precisely timed behavioral sequences, and how the brain implements reinforcement learning (RL). RL is a powerful strategy for learning what action to produce in each state, but requires a unique representation of the states involved in the task. Songbird RL circuitry is thought to operate using a representation of each moment within song syllables, consistent with the sparse sequential bursting of neurons in premotor cortical nucleus HVC. However, such sparse sequences are not present in very young birds, which sing highly variable syllables of random lengths. Here, we review and expand upon a model for how the songbird brain could construct latent sequences to support RL, in light of new data elucidating connections between HVC and auditory cortical areas. We hypothesize that learning occurs via four distinct plasticity processes: 1) formation of 'tutor memory' sequences in auditory areas; 2) formation of appropriately-timed latent HVC sequences, seeded by inputs from auditory areas spontaneously replaying the tutor song; 3) strengthening, during spontaneous replay, of connections from HVC to auditory neurons of corresponding timing in the 'tutor memory' sequence, aligning auditory and motor representations for subsequent song evaluation; and 4) strengthening of connections from premotor neurons to motor output neurons that produce the desired sounds, via well-described song RL circuitry.

Sensory Coding and Sensitivity to Local Estrogens Shift during Critical Period Milestones in the Auditory Cortex of Male Songbirds

Vocal learning occurs during an experience-dependent, age-limited critical period early in development. In songbirds, vocal learning begins when presinging birds acquire an auditory memory of their tutor's song (sensory phase) followed by the onset of vocal production and refinement (sensorimotor phase). Hearing is necessary throughout the vocal learning critical period. One key brain area for songbird auditory processing is the caudomedial nidopallium (NCM), a telencephalic region analogous to mammalian auditory cortex. Despite NCM's established role in auditory processing, it is unclear how the response properties of NCM neurons may shift across development. Moreover, communication processing in NCM is rapidly enhanced by local 17β-estradiol (E2) administration in adult songbirds; however, the function of dynamically fluctuating E₂ in NCM during development is unknown. We collected bilateral extracellular recordings in NCM coupled with reverse microdialysis delivery in juvenile male zebra finches (Taeniopygia guttata) across the vocal learning critical period. We found that auditory-evoked activity and coding accuracy were substantially higher in the NCM of sensory-aged animals compared to sensorimotor-aged animals. Further, we observed both age-dependent and lateralized effects of local E₂ administration on sensory processing. In sensory-aged subjects, E₂ decreased auditory responsiveness across both hemispheres; however, a similar trend was observed in age-matched control subjects. In sensorimotor-aged subjects, E₂ dampened auditory responsiveness in left NCM but enhanced auditory responsiveness in right NCM. Our results reveal an age-dependent physiological shift in auditory processing and lateralized E₂ sensitivity that each precisely track a key neural "switch point" from purely sensory (pre-singing) to sensorimotor (singing) in developing songbirds.

Clustered organization and region-specific identities of estrogen-producing neurons in the forebrain of Zebra Finches (Taeniopygia guttata)

A fast, neuromodulatory role for estrogen signaling has been reported in many regions of the vertebrate brain. Regional differences in the cellular distribution of aromatase (estrogen synthase) in several species suggest that mechanisms for neuroestrogen signaling differ between and even within brain regions. A more comprehensive understanding of neuroestrogen signaling depends on characterizing the cellular identities of neurons that express aromatase. Calcium-binding proteins such as parvalbumin and calbindin are molecular markers for interneuron subtypes, and are co-expressed with aromatase in human temporal cortex. Songbirds like the zebra finch have become important models to understand the brain synthesis of steroids like estrogens and the implications for neurobiology and behavior. Here, we investigated the regional differences in cytoarchitecture and cellular identities of aromatase-expressing neurons in the auditory and sensorimotor forebrain of zebra finches. Aromatase was co-expressed with parvalbumin in the caudomedial nidopallium (NCM) and HVC shelf (proper name) but not in the caudolateral nidopallium (NCL) or hippocampus. By contrast, calbindin was not co-expressed with aromatase in any region investigated. Notably, aromatase-expressing neurons were found in dense somato-somatic clusters, suggesting a coordinated release of local neuroestrogens from clustered neurons. Aromatase clusters were also more abundant and tightly packed in the NCM of males as compared to females. Overall, this study provides new insights into neuroestrogen regulation at the network level, and extends previous findings from human cortex by identifying a subset of aromatase neurons as putative inhibitory interneurons.

Norepinephrine enhances song responsiveness and encoding in the auditory forebrain of male zebra finches

Norepinephrine (NE) can dynamically modulate excitability and functional connectivity of neural circuits in response to changes in external and internal states. Regulation by NE has been demonstrated extensively in mammalian sensory cortices, but whether NE-dependent modulation in sensory cortex alters response properties in downstream sensorimotor regions is less clear. Here we examine this question in male zebra finches, a songbird species with complex vocalizations and a well-defined neural network for auditory processing of those vocalizations. We test the hypothesis that NE modulates auditory processing and encoding, using paired extracellular electrophysiology recordings and pattern classifier analyses. We report that a NE infusion into the auditory cortical region NCM (caudomedial nidopallium; analogous to mammalian secondary auditory cortex) enhances the auditory responses, burst firing, and coding properties of single NCM neurons. Furthermore, we report that NE-dependent changes in NCM coding properties, but not auditory response strength, are transmitted downstream to the sensorimotor nucleus HVC. Finally, NE modulation in the NCM of males is qualitatively similar to that observed in females: in both sexes, NE increases auditory response strengths. However, we observed a sex difference in the mechanism of enhancement: whereas NE increases response strength in females by decreasing baseline firing rates, NE increases response strength in males by increasing auditory-evoked activity. Therefore, NE signaling exhibits a compensatory sex difference to achieve a similar, state-dependent enhancement in signal-to-noise ratio and coding accuracy in males and females. In summary, our results provide further evidence for adrenergic regulation of sensory processing and modulation of auditory/sensorimotor functional connectivity. NEW & NOTEWORTHY This study documents that the catecholamine norepinephrine (also known as noradrenaline) acts in the auditory cortex to shape local processing of complex sound stimuli. Moreover, it also enhances the coding accuracy of neurons in the auditory cortex as well as in the downstream sensorimotor cortex. Finally, this study shows that while the sensory-enhancing effects of norepinephrine are similar in males and females, there are sex differences in the mode of action.

Contribution of spiking activity in the primary auditory cortex to detection in noise

A fundamental problem in hearing is detecting a "target" stimulus (e.g., a friend's voice) that is presented with a noisy background (e.g., the din of a crowded restaurant). Despite its importance to hearing, a relationship between spiking activity and behavioral performance during such a "detection-in-noise" task has yet to be fully elucidated. In this study, we recorded spiking activity in primary auditory cortex (A1) while rhesus monkeys detected a target stimulus that was presented with a noise background. Although some neurons were modulated, the response of the typical A1 neuron was not modulated by the stimulus- and task-related parameters of our task. In contrast, we found more robust representations of these parameters in population-level activity: small populations of neurons matched the monkeys' behavioral sensitivity. Overall, these findings are consistent with the hypothesis that the sensory evidence, which is needed to solve such detection-in-noise tasks, is represented in population-level A1 activity and may be available to be read out by downstream neurons that are involved in mediating this task.NEW & NOTEWORTHY This study examines the contribution of A1 to detecting a sound that is presented with a noisy background. We found that population-level A1 activity, but not single neurons, could provide the evidence needed to make this perceptual decision.

Background noise exerts diverse effects on the cortical encoding of foreground sounds

In natural listening conditions, many sounds must be detected and identified in the context of competing sound sources, which function as background noise. Traditionally, noise is thought to degrade the cortical representation of sounds by suppressing responses and increasing response variability. However, recent studies of neural network models and brain slices have shown that background synaptic noise can improve the detection of signals. Because acoustic noise affects the synaptic background activity of cortical networks, it may improve the cortical responses to signals. We used spike train decoding techniques to determine the functional effects of a continuous white noise background on the responses of clusters of neurons in auditory cortex to foreground signals, specifically frequency-modulated sweeps (FMs) of different velocities, directions, and amplitudes. Whereas the addition of noise progressively suppressed the FM responses of some cortical sites in the core fields with decreasing signal-to-noise ratios (SNRs), the stimulus representation remained robust or was even significantly enhanced at specific SNRs in many others. Even though the background noise level was typically not explicitly encoded in cortical responses, significant information about noise context could be decoded from cortical responses on the basis of how the neural representation of the foreground sweeps was affected. These findings demonstrate significant diversity in signal in noise processing even within the core auditory fields that could support noise-robust hearing across a wide range of listening conditions.NEW & NOTEWORTHY The ability to detect and discriminate sounds in background noise is critical for our ability to communicate. The neural basis of robust perceptual performance in noise is not well understood. We identified neuronal populations in core auditory cortex of squirrel monkeys that differ in how they process foreground signals in background noise and that may contribute to robust signal representation and discrimination in acoustic environments with prominent background noise.

Single Neurons in the Avian Auditory Cortex Encode Individual Identity and Propagation Distance in Naturally Degraded Communication Calls

One of the most complex tasks performed by sensory systems is "scene analysis": the interpretation of complex signals as behaviorally relevant objects. The study of this problem, universal to species and sensory modalities, is particularly challenging in audition, where sounds from various sources and localizations, degraded by propagation through the environment, sum to form a single acoustical signal. Here we investigated in a songbird model, the zebra finch, the neural substrate for ranging and identifying a single source. We relied on ecologically and behaviorally relevant stimuli, contact calls, to investigate the neural discrimination of individual vocal signature as well as sound source distance when calls have been degraded through propagation in a natural environment. Performing electrophysiological recordings in anesthetized birds, we found neurons in the auditory forebrain that discriminate individual vocal signatures despite long-range degradation, as well as neurons discriminating propagation distance, with varying degrees of multiplexing between both information types. Moreover, the neural discrimination performance of individual identity was not affected by propagation-induced degradation beyond what was induced by the decreased intensity. For the first time, neurons with distance-invariant identity discrimination properties as well as distance-discriminant neurons are revealed in the avian auditory cortex. Because these neurons were recorded in animals that had prior experience neither with the vocalizers of the stimuli nor with long-range propagation of calls, we suggest that this neural population is part of a general-purpose system for vocalizer discrimination and ranging.SIGNIFICANCE STATEMENT Understanding how the brain makes sense of the multitude of stimuli that it continually receives in natural conditions is a challenge for scientists. Here we provide a new understanding of how the auditory system extracts behaviorally relevant information, the vocalizer identity and its distance to the listener, from acoustic signals that have been degraded by long-range propagation in natural conditions. We show, for the first time, that single neurons, in the auditory cortex of zebra finches, are capable of discriminating the individual identity and sound source distance in conspecific communication calls. The discrimination of identity in propagated calls relies on a neural coding that is robust to intensity changes, signals' quality, and decreases in the signal-to-noise ratio.

Temporal modulations in speech and music

Speech and music have structured rhythms. Here we discuss a major acoustic correlate of spoken and musical rhythms, the slow (0.25-32Hz) temporal modulations in sound intensity and compare the modulation properties of speech and music. We analyze these modulations using over 25h of speech and over 39h of recordings of Western music. We show that the speech modulation spectrum is highly consistent across 9 languages (including languages with typologically different rhythmic characteristics). A different, but similarly consistent modulation spectrum is observed for music, including classical music played by single instruments of different types, symphonic, jazz, and rock. The temporal modulations of speech and music show broad but well-separated peaks around 5 and 2Hz, respectively. These acoustically dominant time scales may be intrinsic features of speech and music, a possibility which should be investigated using more culturally diverse samples in each domain. Distinct modulation timescales for speech and music could facilitate their perceptual analysis and its neural processing.

Stimulus Phase Locking of Cortical Oscillations for Rhythmic Tone Sequences in Rats

Humans can rapidly detect regular patterns (i.e., within few cycles) without any special attention to the acoustic environment. This suggests that human sensory systems are equipped with a powerful mechanism for automatically predicting forthcoming stimuli to detect regularity. It has recently been hypothesized that the neural basis of sensory predictions exists for not only what happens (predictive coding) but also when a particular stimulus occurs (predictive timing). Here, we hypothesize that the phases of neural oscillations are critical in predictive timing, and these oscillations are modulated in a band-specific manner when acoustic patterns become predictable, i.e., regular. A high-density microelectrode array (10 × 10 within 4 × 4 mm²) was used to characterize spatial patterns of band-specific oscillations when a random-tone sequence was switched to a regular-tone sequence. Increasing the regularity of the tone sequence enhanced phase locking in a band-specific manner, notwithstanding the type of the regular sound pattern. Gamma-band phase locking increased immediately after the transition from random to regular sequences, while beta-band phase locking gradually evolved with time after the transition. The amplitude of the tone-evoked response, in contrast, increased with frequency separation with respect to the prior tone, suggesting that the evoked-response amplitude encodes sequence information on a local scale, i.e., the local order of tones. The phase locking modulation spread widely over the auditory cortex, while the amplitude modulation was confined around the activation foci. Thus, our data suggest that oscillatory phase plays a more important role than amplitude in the neuronal detection of tone sequence regularity, which is closely related to predictive timing. Furthermore, band-specific contributions may support recent theories that gamma oscillations encode bottom-up prediction errors, whereas beta oscillations are involved in top-down prediction.

Vocal sequences suppress spiking in the bat auditory cortex while evoking concomitant steady-state local field potentials

The mechanisms by which the mammalian brain copes with information from natural vocalization streams remain poorly understood. This article shows that in highly vocal animals, such as the bat species Carollia perspicillata, the spike activity of auditory cortex neurons does not track the temporal information flow enclosed in fast time-varying vocalization streams emitted by conspecifics. For example, leading syllables of so-called distress sequences (produced by bats subjected to duress) suppress cortical spiking to lagging syllables. Local fields potentials (LFPs) recorded simultaneously to cortical spiking evoked by distress sequences carry multiplexed information, with response suppression occurring in low frequency LFPs (i.e. 2–15 Hz) and steady-state LFPs occurring at frequencies that match the rate of energy fluctuations in the incoming sound streams (i.e. >50 Hz). Such steady-state LFPs could reflect underlying synaptic activity that does not necessarily lead to cortical spiking in response to natural fast time-varying vocal sequences.

Transformation of temporal sequences in the zebra finch auditory system

This study examines how temporally patterned stimuli are transformed as they propagate from primary to secondary zones in the thalamorecipient auditory pallium in zebra finches. Using a new class of synthetic click stimuli, we find a robust mapping from temporal sequences in the primary zone to distinct population vectors in secondary auditory areas. We tested whether songbirds could discriminate synthetic click sequences in an operant setup and found that a robust behavioral discrimination is present for click sequences composed of intervals ranging from 11 ms to 40 ms, but breaks down for stimuli composed of longer inter-click intervals. This work suggests that the analog of the songbird auditory cortex transforms temporal patterns to sequence-selective population responses or 'spatial codes', and that these distinct population responses contribute to behavioral discrimination of temporally complex sounds.

Contextual effects of noise on vocalization encoding in primary auditory cortex

Robust auditory perception plays a pivotal function for processing behaviorally relevant sounds, particularly with distractions from the environment. The neuronal coding enabling this ability, however, is still not well understood. In this study, we recorded single-unit activity from the primary auditory cortex (A1) of awake marmoset monkeys (Callithrix jacchus) while delivering conspecific vocalizations degraded by two different background noises: broadband white noise and vocalization babble. Noise effects on neural representation of target vocalizations were quantified by measuring the responses' similarity to those elicited by natural vocalizations as a function of signal-to-noise ratio. A clustering approach was used to describe the range of response profiles by reducing the population responses to a summary of four response classes (robust, balanced, insensitive, and brittle) under both noise conditions. This clustering approach revealed that, on average, approximately two-thirds of the neurons change their response class when encountering different noises. Therefore, the distortion induced by one particular masking background in single-unit responses is not necessarily predictable from that induced by another, suggesting the low likelihood of a unique group of noise-invariant neurons across different background conditions in A1. Regarding noise influence on neural activities, the brittle response group showed addition of spiking activity both within and between phrases of vocalizations relative to clean vocalizations, whereas the other groups generally showed spiking activity suppression within phrases, and the alteration between phrases was noise dependent. Overall, the variable single-unit responses, yet consistent response types, imply that primate A1 performs scene analysis through the collective activity of multiple neurons.

NEW & NOTEWORTHY

The understanding of where and how auditory scene analysis is accomplished is of broad interest to neuroscientists. In this paper, we systematically investigated neuronal coding of multiple vocalizations degraded by two distinct noises at various signal-to-noise ratios in nonhuman primates. In the process, we uncovered heterogeneity of single-unit representations for different auditory scenes yet homogeneity of responses across the population.

Effects of Signal-to-Noise Ratio on Auditory Cortical Frequency Processing

The neural mechanisms that support the robust processing of acoustic signals in the presence of background noise in the auditory system remain largely unresolved. Psychophysical experiments have shown that signal detection is influenced by the signal-to-noise ratio (SNR) and the overall stimulus level, but this relationship has not been fully characterized. We evaluated the neural representation of frequency in rat primary auditory cortex by constructing tonal frequency response areas (FRAs) in primary auditory cortex for different SNRs, tone levels, and noise levels. We show that response strength and selectivity for frequency and sound level depend on interactions between SNRs and tone levels. At low SNRs, jointly increasing the tone and noise levels reduced firing rates and narrowed FRA bandwidths; at higher SNRs, however, increasing the tone and noise levels increased firing rates and expanded bandwidths, as is usually seen for FRAs obtained without background noise. These changes in frequency and intensity tuning decreased tone level and tone frequency discriminability at low SNRs. By contrast, neither response onset latencies nor noise-driven steady-state firing rates meaningfully interacted with SNRs or overall sound levels. Speech detection performance in humans was also shown to depend on the interaction between overall sound level and SNR. Together, these results indicate that signal processing difficulties imposed by high noise levels are quite general and suggest that the neurophysiological changes we see for simple sounds generalize to more complex stimuli.

SIGNIFICANCE STATEMENT

Effective processing of sounds in background noise is an important feature of the mammalian auditory system and a necessary feature for successful hearing in many listening conditions. Even mild hearing loss strongly affects this ability in humans, seriously degrading the ability to communicate. The mechanisms involved in achieving high performance in background noise are not well understood. We investigated the effects of SNR and overall stimulus level on the frequency tuning of neurons in rat primary auditory cortex. We found that the effects of noise on frequency selectivity are not determined solely by the SNR but depend also on the levels of the foreground tones and background noise. These observations can lead to improvement in therapeutic approaches for hearing-impaired patients.

Auditory experience-dependent cortical circuit shaping for memory formation in bird song learning

As in human speech acquisition, songbird vocal learning depends on early auditory experience. During development, juvenile songbirds listen to and form auditory memories of adult tutor songs, which they use to shape their own vocalizations in later sensorimotor learning. The higher-level auditory cortex, called the caudomedial nidopallium (NCM), is a potential storage site for tutor song memory, but no direct electrophysiological evidence of tutor song memory has been found. Here, we identify the neuronal substrate for tutor song memory by recording single-neuron activity in the NCM of behaving juvenile zebra finches. After tutor song experience, a small subset of NCM neurons exhibit highly selective auditory responses to the tutor song. Moreover, blockade of GABAergic inhibition, and sleep decrease their selectivity. Taken together, these results suggest that experience-dependent recruitment of GABA-mediated inhibition shapes auditory cortical circuits, leading to sparse representation of tutor song memory in auditory cortical neurons.

Hierarchical emergence of sequence sensitivity in the songbird auditory forebrain

Bengalese finches (Lonchura striata var. domestica) generate more complex sequences in their songs than zebra finches. Because of this, we chose this species to explore the signal processing of sound sequence in the primary auditory forebrain area, field L, and in a secondary area, the caudomedial nidopallium (NCM). We simultaneously recorded activity from multiple single units in urethane-anesthetized birds. We successfully replicated the results of a previous study in awake zebra finches examining stimulus-specific habituation of NCM neurons to conspecific songs. Then, we used an oddball paradigm and compared the neural response to deviant sounds that were presented infrequently, with the response to standard sounds, which were presented frequently. In a single sound oddball task, two different song elements were assigned for the deviant and standard sounds. The response bias to deviant elements was larger in NCM than in field L. In a triplet sequence oddball task, two triplet sequences containing elements ABC and ACB were assigned as the deviant and standard. Only neurons in NCM that displayed broad-shaped spike waveforms had sensitivity to the difference in element order. Our results suggest the hierarchical processing of complex sound sequences in the songbird auditory forebrain.

Central auditory neurons have composite receptive fields

High-level neurons processing complex, behaviorally relevant signals are sensitive to conjunctions of features. Characterizing the receptive fields of such neurons is difficult with standard statistical tools, however, and the principles governing their organization remain poorly understood. Here, we demonstrate multiple distinct receptive-field features in individual high-level auditory neurons in a songbird, European starling, in response to natural vocal signals (songs). We then show that receptive fields with similar characteristics can be reproduced by an unsupervised neural network trained to represent starling songs with a single learning rule that enforces sparseness and divisive normalization. We conclude that central auditory neurons have composite receptive fields that can arise through a combination of sparseness and normalization in neural circuits. Our results, along with descriptions of random, discontinuous receptive fields in the central olfactory neurons in mammals and insects, suggest general principles of neural computation across sensory systems and animal classes.

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.

Subthreshold membrane responses underlying sparse spiking to natural vocal signals in auditory cortex

Natural acoustic communication signals, such as speech, are typically high-dimensional with a wide range of co-varying spectro-temporal features at multiple timescales. The synaptic and network mechanisms for encoding these complex signals are largely unknown. We are investigating these mechanisms in high-level sensory regions of the songbird auditory forebrain, where single neurons show sparse, object-selective spiking responses to conspecific songs. Using whole-cell in vivo patch clamp techniques in the caudal mesopallium and the caudal nidopallium of starlings, we examine song-driven subthreshold and spiking activity. We find that both the subthreshold and the spiking activity are reliable (i.e. the same song drives a similar response each time it is presented) and specific (i.e. responses to different songs are distinct). Surprisingly, however, the reliability and specificity of the subthreshold response was uniformly high regardless of when the cell spiked, even for song stimuli that drove no spikes. We conclude that despite a selective and sparse spiking response, high-level auditory cortical neurons are under continuous, non-selective, stimulus-specific synaptic control. To investigate the role of local network inhibition in this synaptic control, we then recorded extracellularly while pharmacologically blocking local GABAergic transmission. This manipulation modulated the strength and the reliability of stimulus-driven spiking, consistent with a role for local inhibition in regulating the reliability of network activity and the stimulus specificity of the subthreshold response in single cells. We discuss these results in the context of underlying computations that could generate sparse, stimulus-selective spiking responses, and models for hierarchical pooling.

Norepinephrine Modulates Coding of Complex Vocalizations in the Songbird Auditory Cortex Independent of Local Neuroestrogen Synthesis

The catecholamine norepinephrine plays a significant role in auditory processing. Most studies to date have examined the effects of norepinephrine on the neuronal response to relatively simple stimuli, such as tones and calls. It is less clear how norepinephrine shapes the detection of complex syntactical sounds, as well as the coding properties of sensory neurons. Songbirds provide an opportunity to understand how auditory neurons encode complex, learned vocalizations, and the potential role of norepinephrine in modulating the neuronal computations for acoustic communication. Here, we infused norepinephrine into the zebra finch auditory cortex and performed extracellular recordings to study the modulation of song representations in single neurons. Consistent with its proposed role in enhancing signal detection, norepinephrine decreased spontaneous activity and firing during stimuli, yet it significantly enhanced the auditory signal-to-noise ratio. These effects were all mimicked by clonidine, an α-2 receptor agonist. Moreover, a pattern classifier analysis indicated that norepinephrine enhanced the ability of single neurons to accurately encode complex auditory stimuli. Because neuroestrogens are also known to enhance auditory processing in the songbird brain, we tested the hypothesis that norepinephrine actions depend on local estrogen synthesis. Neither norepinephrine nor adrenergic receptor antagonist infusion into the auditory cortex had detectable effects on local estradiol levels. Moreover, pretreatment with fadrozole, a specific aromatase inhibitor, did not block norepinephrine's neuromodulatory effects. Together, these findings indicate that norepinephrine enhances signal detection and information encoding for complex auditory stimuli by suppressing spontaneous "noise" activity and that these actions are independent of local neuroestrogen synthesis.

Hearing the light: neural and perceptual encoding of optogenetic stimulation in the central auditory pathway

Optogenetics provides a means to dissect the organization and function of neural circuits. Optogenetics also offers the translational promise of restoring sensation, enabling movement or supplanting abnormal activity patterns in pathological brain circuits. However, the inherent sluggishness of evoked photocurrents in conventional channelrhodopsins has hampered the development of optoprostheses that adequately mimic the rate and timing of natural spike patterning. Here, we explore the feasibility and limitations of a central auditory optoprosthesis by photoactivating mouse auditory midbrain neurons that either express channelrhodopsin-2 (ChR2) or Chronos, a channelrhodopsin with ultra-fast channel kinetics. Chronos-mediated spike fidelity surpassed ChR2 and natural acoustic stimulation to support a superior code for the detection and discrimination of rapid pulse trains. Interestingly, this midbrain coding advantage did not translate to a perceptual advantage, as behavioral detection of midbrain activation was equivalent with both opsins. Auditory cortex recordings revealed that the precisely synchronized midbrain responses had been converted to a simplified rate code that was indistinguishable between opsins and less robust overall than acoustic stimulation. These findings demonstrate the temporal coding benefits that can be realized with next-generation channelrhodopsins, but also highlight the challenge of inducing variegated patterns of forebrain spiking activity that support adaptive perception and behavior.

Behavioral relevance helps untangle natural vocal categories in a specific subset of core auditory cortical pyramidal neurons

Sound categorization is essential for auditory behaviors like acoustic communication, but its genesis within the auditory pathway is not well understood-especially for learned natural categories like vocalizations, which often share overlapping acoustic features that must be distinguished (e.g., speech). We use electrophysiological mapping and single-unit recordings in mice to investigate how representations of natural vocal categories within core auditory cortex are modulated when one category acquires enhanced behavioral relevance. Taking advantage of a maternal mouse model of acoustic communication, we found no long-term auditory cortical map expansion to represent a behaviorally relevant pup vocalization category-contrary to expectations from the cortical plasticity literature on conditioning with pure tones. Instead, we observed plasticity that improved the separation between acoustically similar pup and adult vocalization categories among a physiologically defined subset of late-onset, putative pyramidal neurons, but not among putative interneurons. Additionally, a larger proportion of these putative pyramidal neurons in maternal animals compared with nonmaternal animals responded to the individual pup call exemplars having combinations of acoustic features most typical of that category. Together, these data suggest that higher-order representations of acoustic categories arise from a subset of core auditory cortical pyramidal neurons that become biased toward the combination of acoustic features statistically predictive of membership to a behaviorally relevant sound category.

Temporal pattern processing in songbirds

Understanding how the brain perceives, organizes and uses patterned information is directly related to the neurobiology of language. Given the present limitations, such knowledge at the scale of neurons, neural circuits and neural populations can only come from non-human models, focusing on shared capacities that are relevant to language processing. Here we review recent advances in the behavioral and neural basis of temporal pattern processing of natural auditory communication signals in songbirds, focusing on European starlings. We suggest a general inhibitory circuit for contextual modulation that can act to control sensory representations based on patterning rules.

Hearing in noisy environments: noise invariance and contrast gain control

Contrast gain control has recently been identified as a fundamental property of the auditory system. Electrophysiological recordings in ferrets have shown that neurons continuously adjust their gain (their sensitivity to change in sound level) in response to the contrast of sounds that are heard. At the level of the auditory cortex, these gain changes partly compensate for changes in sound contrast. This means that sounds which are structurally similar, but have different contrasts, have similar neuronal representations in the auditory cortex. As a result, the cortical representation is relatively invariant to stimulus contrast and robust to the presence of noise in the stimulus. In the inferior colliculus (an important subcortical auditory structure), gain changes are less reliably compensatory, suggesting that contrast- and noise-invariant representations are constructed gradually as one ascends the auditory pathway. In addition to noise invariance, contrast gain control provides a variety of computational advantages over static neuronal representations; it makes efficient use of neuronal dynamic range, may contribute to redundancy-reducing, sparse codes for sound and allows for simpler decoding of population responses. The circuits underlying auditory contrast gain control are still under investigation. As in the visual system, these circuits may be modulated by factors other than stimulus contrast, forming a potential neural substrate for mediating the effects of attention as well as interactions between the senses.