Rapid neural adaptation to sound level statistics

Speech Recognition and Noise Adaptation in Realistic Noises

The recognition of isolated words in noise improves as words are delayed from the noise onset. This phenomenon, known as adaptation to noise, has been mostly investigated using synthetic noises. The aim here was to investigate whether adaptation occurs for realistic noises and to what extent it depends on the spectrum and level fluctuations of the noise. Forty-nine different realistic and synthetic noises were analyzed and classified according to how much they fluctuated in level over time and how much their spectra differed from the speech spectrum. Six representative noises were chosen that covered the observed range of level fluctuations and spectral differences but could still mask speech. For the six noises, speech reception thresholds (SRTs) were measured for natural and tone-vocoded words delayed 50 (early condition) and 800 ms (late condition) from the noise onset. Adaptation was calculated as the SRT improvement in the late relative to the early condition. Twenty-two adults with normal hearing participated in the experiments. For natural words, adaptation was small overall (mean = 0.5 dB) and similar across the six noises. For vocoded words, significant adaptation occurred for all six noises (mean = 1.3 dB) and was not statistically different across noises. For the tested noises, the amount of adaptation was independent of the spectrum and level fluctuations of the noise. The results suggest that adaptation in speech recognition can occur in realistic noisy environments.

Decoding contextual influences on auditory perception from primary auditory cortex

Perception can be highly dependent on stimulus context, but whether and how sensory areas encode the context remains uncertain. We used an ambiguous auditory stimulus - a tritone pair - to investigate the neural activity associated with a preceding contextual stimulus that strongly influenced the tritone pair's perception: either as an ascending or a descending step in pitch. We recorded single-unit responses from a population of auditory cortical cells in awake ferrets listening to the tritone pairs preceded by the contextual stimulus. We find that the responses adapt locally to the contextual stimulus, consistent with human MEG recordings from the auditory cortex under the same conditions. Decoding the population responses demonstrates that cells responding to pitch-changes are able to predict well the context-sensitive percept of the tritone pairs. Conversely, decoding the individual pitch representations and taking their distance in the circular Shepard tone space predicts the opposite of the percept. The various percepts can be readily captured and explained by a neural model of cortical activity based on populations of adapting, pitch and pitch-direction cells, aligned with the neurophysiological responses. Together, these decoding and model results suggest that contextual influences on perception may well be already encoded at the level of the primary sensory cortices, reflecting basic neural response properties commonly found in these areas.

Cortical encoding of hierarchical linguistic information when syllabic rhythms are obscured by echoes

In speech perception, low-frequency cortical activity tracks hierarchical linguistic units (e.g., syllables, phrases, and sentences) on top of acoustic features (e.g., speech envelope). Since the fluctuation of speech envelope typically corresponds to the syllabic boundaries, one common interpretation is that the acoustic envelope underlies the extraction of discrete syllables from continuous speech for subsequent linguistic processing. However, it remains unclear whether and how cortical activity encodes linguistic information when the speech envelope does not provide acoustic correlates of syllables. To address the issue, we introduced a frequency-tagging speech stream where the syllabic rhythm was obscured by echoic envelopes and investigated neural encoding of hierarchical linguistic information using electroencephalography (EEG). When listeners attended to the echoic speech, cortical activity showed reliable tracking of syllable, phrase, and sentence levels, among which the higher-level linguistic units elicited more robust neural responses. When attention was diverted from the echoic speech, reliable neural tracking of the syllable level was also observed in contrast to deteriorated neural tracking of the phrase and sentence levels. Further analyses revealed that the envelope aligned with the syllabic rhythm could be recovered from the echoic speech through a neural adaptation model, and the reconstructed envelope yielded higher predictive power for the neural tracking responses than either the original echoic envelope or anechoic envelope. Taken together, these results suggest that neural adaptation and attentional modulation jointly contribute to neural encoding of linguistic information in distorted speech where the syllabic rhythm is obscured by echoes.

A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses

Sounds are temporal stimuli decomposed into numerous elementary components by the auditory nervous system. For instance, a temporal to spectro-temporal transformation modelling the frequency decomposition performed by the cochlea is a widely adopted first processing step in today's computational models of auditory neural responses. Similarly, increments and decrements in sound intensity (i.e., of the raw waveform itself or of its spectral bands) constitute critical features of the neural code, with high behavioural significance. However, despite the growing attention of the scientific community on auditory OFF responses, their relationship with transient ON, sustained responses and adaptation remains unclear. In this context, we propose a new general model, based on a pair of linear filters, named AdapTrans, that captures both sustained and transient ON and OFF responses into a unifying and easy to expand framework. We demonstrate that filtering audio cochleagrams with AdapTrans permits to accurately render known properties of neural responses measured in different mammal species such as the dependence of OFF responses on the stimulus fall time and on the preceding sound duration. Furthermore, by integrating our framework into gold standard and state-of-the-art machine learning models that predict neural responses from audio stimuli, following a supervised training on a large compilation of electrophysiology datasets (ready-to-deploy PyTorch models and pre-processed datasets shared publicly), we show that AdapTrans systematically improves the prediction accuracy of estimated responses within different cortical areas of the rat and ferret auditory brain. Together, these results motivate the use of our framework for computational and systems neuroscientists willing to increase the plausibility and performances of their models of audition.

Decoding contextual influences on auditory perception from primary auditory cortex

Perception can be highly dependent on stimulus context, but whether and how sensory areas encode the context remains uncertain. We used an ambiguous auditory stimulus - a tritone pair - to investigate the neural activity associated with a preceding contextual stimulus that strongly influenced the tritone pair's perception: either as an ascending or a descending step in pitch. We recorded single-unit responses from a population of auditory cortical cells in awake ferrets listening to the tritone pairs preceded by the contextual stimulus. We find that the responses adapt locally to the contextual stimulus, consistent with human MEG recordings from the auditory cortex under the same conditions. Decoding the population responses demonstrates that cells responding to pitch-class-changes are able to predict well the context-sensitive percept of the tritone pairs. Conversely, decoding the individual pitch-class representations and taking their distance in the circular Shepard tone space predicts the opposite of the percept. The various percepts can be readily captured and explained by a neural model of cortical activity based on populations of adapting, pitch-class and pitch-class-direction cells, aligned with the neurophysiological responses. Together, these decoding and model results suggest that contextual influences on perception may well be already encoded at the level of the primary sensory cortices, reflecting basic neural response properties commonly found in these areas.

Noises on-How the Brain Deals with Acoustic Noise

What is noise? When does a sound form part of the acoustic background and when might it come to our attention as part of the foreground? Our brain seems to filter out irrelevant sounds in a seemingly effortless process, but how this is achieved remains opaque and, to date, unparalleled by any algorithm. In this review, we discuss how noise can be both background and foreground, depending on what a listener/brain is trying to achieve. We do so by addressing questions concerning the brain's potential bias to interpret certain sounds as part of the background, the extent to which the interpretation of sounds depends on the context in which they are heard, as well as their ethological relevance, task-dependence, and a listener's overall mental state. We explore these questions with specific regard to the implicit, or statistical, learning of sounds and the role of feedback loops between cortical and subcortical auditory structures.

Adaptation to Noise in Spectrotemporal Modulation Detection and Word Recognition

Noise adaptation is the improvement in auditory function as the signal of interest is delayed in the noise. Here, we investigated if noise adaptation occurs in spectral, temporal, and spectrotemporal modulation detection as well as in speech recognition. Eighteen normal-hearing adults participated in the experiments. In the modulation detection tasks, the signal was a 200ms spectrally and/or temporally modulated ripple noise. The spectral modulation rate was two cycles per octave, the temporal modulation rate was 10 Hz, and the spectrotemporal modulations combined these two modulations, which resulted in a downward-moving ripple. A control experiment was performed to determine if the results generalized to upward-moving ripples. In the speech recognition task, the signal consisted of disyllabic words unprocessed or vocoded to maintain only envelope cues. Modulation detection thresholds at 0 dB signal-to-noise ratio and speech reception thresholds were measured in quiet and in white noise (at 60 dB SPL) for noise-signal onset delays of 50 ms (early condition) and 800 ms (late condition). Adaptation was calculated as the threshold difference between the early and late conditions. Adaptation in word recognition was statistically significant for vocoded words (2.1 dB) but not for natural words (0.6 dB). Adaptation was found to be statistically significant in spectral (2.1 dB) and temporal (2.2 dB) modulation detection but not in spectrotemporal modulation detection (downward ripple: 0.0 dB, upward ripple: -0.4 dB). Findings suggest that noise adaptation in speech recognition is unrelated to improvements in the encoding of spectrotemporal modulation cues.

Propofol-mediated loss of consciousness disrupts predictive routing and local field phase modulation of neural activity

Predictive coding is a fundamental function of the cortex. The predictive routing model proposes a neurophysiological implementation for predictive coding. Predictions are fed back from deep-layer cortex via alpha/beta (8-30Hz) oscillations. They inhibit the gamma (40-100Hz) and spiking that feed sensory inputs forward. Unpredicted inputs arrive in circuits unprepared by alpha/beta, resulting in enhanced gamma and spiking. To test the predictive routing model and its role in consciousness, we collected data from intracranial recordings of macaque monkeys during passive presentation of auditory oddballs (e.g., AAAAB) before and after propofol-mediated loss of consciousness (LOC). In line with the predictive routing model, alpha/beta oscillations in the awake state served to inhibit the processing of predictable stimuli. Propofol-mediated LOC eliminated alpha/beta modulation by a predictable stimulus in sensory cortex and alpha/beta coherence between sensory and frontal areas. As a result, oddball stimuli evoked enhanced gamma power, late (> 200 ms from stimulus onset) period spiking, and superficial layer sinks in sensory cortex. Therefore, auditory cortex was in a disinhibited state during propofol-mediated LOC. However, despite these enhanced feedforward responses in auditory cortex, there was a loss of differential spiking to oddballs in higher order cortex. This may be a consequence of a loss of within-area and inter-area spike-field coupling in the alpha/beta and gamma frequency bands. These results provide strong constraints for current theories of consciousness.

Significance statement

Neurophysiology studies have found alpha/beta oscillations (8-30Hz), gamma oscillations (40-100Hz), and spiking activity during cognition. Alpha/beta power has an inverse relationship with gamma power/spiking. This inverse relationship suggests that gamma/spiking are under the inhibitory control of alpha/beta. The predictive routing model hypothesizes that alpha/beta oscillations selectively inhibit (and thereby control) cortical activity that is predictable. We tested whether this inhibitory control is a signature of consciousness. We used multi-area neurophysiology recordings in monkeys presented with tone sequences that varied in predictability. We recorded brain activity as the anesthetic propofol was administered to manipulate consciousness. Compared to conscious processing, propofol-mediated unconsciousness disrupted alpha/beta inhibitory control during predictive processing. This led to a disinhibition of gamma/spiking, consistent with the predictive routing model.

Auditory brainstem mechanisms likely compensate for self-imposed peripheral inhibition

Feedback networks in the brain regulate downstream auditory function as peripheral as the cochlea. However, the upstream neural consequences of this peripheral regulation are less understood. For instance, the medial olivocochlear reflex (MOCR) in the brainstem causes putative attenuation of responses generated in the cochlea and cortex, but those generated in the brainstem are perplexingly unaffected. Based on known neural circuitry, we hypothesized that the inhibition of peripheral input is compensated for by positive feedback in the brainstem over time. We predicted that the inhibition could be captured at the brainstem with shorter (1.5 s) than previously employed long duration (240 s) stimuli where this inhibition is likely compensated for. Results from 16 normal-hearing human listeners support our hypothesis in that when the MOCR is activated, there is a robust reduction of responses generated at the periphery, brainstem, and cortex for short-duration stimuli. Such inhibition at the brainstem, however, diminishes for long-duration stimuli suggesting some compensatory mechanisms at play. Our findings provide a novel non-invasive window into potential gain compensation mechanisms in the brainstem that may have implications for auditory disorders such as tinnitus. Our methodology will be useful in the evaluation of efferent function in individuals with hearing loss.

Where is the mind within the brain? Transient selection of subnetworks by metabotropic receptors and G protein-gated ion channels

Perhaps the most important question posed by brain research is: How the brain gives rise to the mind. To answer this question, we have primarily relied on the connectionist paradigm: The brain's entire knowledge and thinking skills are thought to be stored in the connections; and the mental operations are executed by network computations. I propose here an alternative paradigm: Our knowledge and skills are stored in metabotropic receptors (MRs) and the G protein-gated ion channels (GPGICs). Here, mental operations are assumed to be executed by the functions of MRs and GPGICs. As GPGICs have the capacity to close or open branches of dendritic trees and axon terminals, their states transiently re-route neural activity throughout the nervous system. First, MRs detect ligands that signal the need to activate GPGICs. Next, GPGICs transiently select a subnetwork within the brain. The process of selecting this new subnetwork is what constitutes a mental operation - be it in a form of directed attention, perception or making a decision. Synaptic connections and network computations play only a secondary role, supporting MRs and GPGICs. According to this new paradigm, the mind emerges within the brain as the function of MRs and GPGICs whose primary function is to continually select the pathways over which neural activity will be allowed to pass. It is argued that MRs and GPGICs solve the scaling problem of intelligence from which the connectionism paradigm suffers.

Adaptation in auditory processing

Adaptation is an essential feature of auditory neurons, which reduces their responses to unchanging and recurring sounds and allows their response properties to be matched to the constantly changing statistics of sounds that reach the ears. As a consequence, processing in the auditory system highlights novel or unpredictable sounds and produces an efficient representation of the vast range of sounds that animals can perceive by continually adjusting the sensitivity and, to a lesser extent, the tuning properties of neurons to the most commonly encountered stimulus values. Together with attentional modulation, adaptation to sound statistics also helps to generate neural representations of sound that are tolerant to background noise and therefore plays a vital role in auditory scene analysis. In this review, we consider the diverse forms of adaptation that are found in the auditory system in terms of the processing levels at which they arise, the underlying neural mechanisms, and their impact on neural coding and perception. We also ask what the dynamics of adaptation, which can occur over multiple timescales, reveal about the statistical properties of the environment. Finally, we examine how adaptation to sound statistics is influenced by learning and experience and changes as a result of aging and hearing loss.

Quantitative models of auditory cortical processing

To generate insight from experimental data, it is critical to understand the inter-relationships between individual data points and place them in context within a structured framework. Quantitative modeling can provide the scaffolding for such an endeavor. Our main objective in this review is to provide a primer on the range of quantitative tools available to experimental auditory neuroscientists. Quantitative modeling is advantageous because it can provide a compact summary of observed data, make underlying assumptions explicit, and generate predictions for future experiments. Quantitative models may be developed to characterize or fit observed data, to test theories of how a task may be solved by neural circuits, to determine how observed biophysical details might contribute to measured activity patterns, or to predict how an experimental manipulation would affect neural activity. In complexity, quantitative models can range from those that are highly biophysically realistic and that include detailed simulations at the level of individual synapses, to those that use abstract and simplified neuron models to simulate entire networks. Here, we survey the landscape of recently developed models of auditory cortical processing, highlighting a small selection of models to demonstrate how they help generate insight into the mechanisms of auditory processing. We discuss examples ranging from models that use details of synaptic properties to explain the temporal pattern of cortical responses to those that use modern deep neural networks to gain insight into human fMRI data. We conclude by discussing a biologically realistic and interpretable model that our laboratory has developed to explore aspects of vocalization categorization in the auditory pathway.

The hunt for hidden hearing loss in humans: From preclinical studies to effective interventions

Many individuals experience hearing problems that are hidden under a normal audiogram. This not only impacts on individual sufferers, but also on clinicians who can offer little in the way of support. Animal studies using invasive methodologies have developed solid evidence for a range of pathologies underlying this hidden hearing loss (HHL), including cochlear synaptopathy, auditory nerve demyelination, elevated central gain, and neural mal-adaptation. Despite progress in pre-clinical models, evidence supporting the existence of HHL in humans remains inconclusive, and clinicians lack any non-invasive biomarkers sensitive to HHL, as well as a standardized protocol to manage hearing problems in the absence of elevated hearing thresholds. Here, we review animal models of HHL as well as the ongoing research for tools with which to diagnose and manage hearing difficulties associated with HHL. We also discuss new research opportunities facilitated by recent methodological tools that may overcome a series of barriers that have hampered meaningful progress in diagnosing and treating of HHL.

The Role of Efferent Reflexes in the Efficient Encoding of Speech by the Auditory Nerve

To avoid information loss, the auditory system must adapt the broad dynamic range of natural sounds to the restricted dynamic range of auditory nerve fibers. How it solves this dynamic range problem is not fully understood. Recent electrophysiological studies showed that dynamic-range adaptation occurs at the auditory nerve level, but the amount of adaptation found was insufficient to prevent information loss. We used the physiological MATLAB Auditory Periphery model to study the contribution of efferent reflexes to dynamic range adaptation. Simulating the healthy human auditory periphery provided adaptation predictions that suggest that the acoustic reflex shifts rate-level functions toward a given context level and the medial olivocochlear reflex sharpens the response of nerve fibers around that context level. A simulator of hearing was created to decode model-predicted firing of the auditory nerve back into an acoustic signal, for use in psychophysical tasks. Speech reception thresholds in noise obtained with a normal-hearing implementation of the simulator were just 1 dB above those measured with unprocessed stimuli. This result validates the simulator for speech stimuli. Disabling efferent reflexes elevated thresholds by 4 dB, reaching thresholds found in mild-to-moderately hearing-impaired individuals. Overall, our studies suggest that efferent reflexes may contribute to overcoming the dynamic range problem. Because specific sensorineural pathologies can be inserted in the model, the simulator can be used to obtain the psychophysical signatures of each pathology, thereby laying a path to differential diagnosis.SIGNIFICANCE STATEMENT The saturation of auditory nerve fibers at moderate sound levels seen in rate-level functions challenges our understanding of how sounds of wide dynamic range are encoded. Our physiologically inspired simulations suggest that efferent reflexes may play a major role in dynamic range adaptation, with the acoustic reflex moving auditory nerve rate-level function toward a given context level and the medial olivocochlear reflex increasing fiber sensitivity around that context level. A psychophysical task using advanced simulations showed how the existence of the efferent system could prevent unrecoverable information loss and severe impairment of speech-in-noise intelligibility. These findings illustrate how important the precise modeling of peripheral compression is to both simulations and the understanding of normal and impaired hearing.

Cortical adaptation to sound reverberation

In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation. Our brains usually cope well with reverberation, allowing us to recognize sound sources regardless of their environments. In contrast, reverberation can cause severe difficulties for speech recognition algorithms and hearing-impaired people. The present study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant counterparts. The model neurons achieved this by extending the inhibitory component of their receptive filters for more reverberant spaces, and did so in a frequency-dependent manner. These predicted effects were observed in the responses of auditory cortical neurons of ferrets in the same simulated reverberant environments. Together, these results suggest that auditory cortical neurons adapt to reverberation by adjusting their filtering properties in a manner consistent with dereverberation.

Adaptation to noise in normal and impaired hearing

Many aspects of hearing function are negatively affected by background noise. Listeners, however, have some ability to adapt to background noise. For instance, the detection of pure tones and the recognition of isolated words embedded in noise can improve gradually as tones and words are delayed a few hundred milliseconds in the noise. While some evidence suggests that adaptation to noise could be mediated by the medial olivocochlear reflex, adaptation can occur for people who do not have a functional reflex. Since adaptation can facilitate hearing in noise, and hearing in noise is often harder for hearing-impaired than for normal-hearing listeners, it is conceivable that adaptation is impaired with hearing loss. It remains unclear, however, if and to what extent this is the case, or whether impaired adaptation contributes to the greater difficulties experienced by hearing-impaired listeners understanding speech in noise. Here, we review adaptation to noise, the mechanisms potentially contributing to this adaptation, and factors that might reduce the ability to adapt to background noise, including cochlear hearing loss, cochlear synaptopathy, aging, and noise exposure. The review highlights few knowns and many unknowns about adaptation to noise, and thus paves the way for further research on this topic.

Hearing in Complex Environments: Auditory Gain Control, Attention, and Hearing Loss

Listening in noisy or complex sound environments is difficult for individuals with normal hearing and can be a debilitating impairment for those with hearing loss. Extracting meaningful information from a complex acoustic environment requires the ability to accurately encode specific sound features under highly variable listening conditions and segregate distinct sound streams from multiple overlapping sources. The auditory system employs a variety of mechanisms to achieve this auditory scene analysis. First, neurons across levels of the auditory system exhibit compensatory adaptations to their gain and dynamic range in response to prevailing sound stimulus statistics in the environment. These adaptations allow for robust representations of sound features that are to a large degree invariant to the level of background noise. Second, listeners can selectively attend to a desired sound target in an environment with multiple sound sources. This selective auditory attention is another form of sensory gain control, enhancing the representation of an attended sound source while suppressing responses to unattended sounds. This review will examine both “bottom-up” gain alterations in response to changes in environmental sound statistics as well as “top-down” mechanisms that allow for selective extraction of specific sound features in a complex auditory scene. Finally, we will discuss how hearing loss interacts with these gain control mechanisms, and the adaptive and/or maladaptive perceptual consequences of this plasticity.

Effect of Titrated Exposure to Non-Traumatic Noise on Unvoiced Speech Recognition in Human Listeners with Normal Audiological Profiles

Non-traumatic noise exposure has been shown in animal models to impact the processing of envelope cues. However, evidence in human studies has been conflicting, possibly because the measures have not been specifically parameterized based on listeners' exposure profiles. The current study examined young dental-school students, whose exposure to high-frequency non-traumatic dental-drill noise during their course of study is systematic and precisely quantifiable. Twenty-five dental students and twenty-seven non-dental participants were recruited. The listeners were asked to recognize unvoiced sentences that were processed to contain only envelope cues useful for recognition and have been filtered to frequency regions inside or outside the dental noise spectrum. The sentences were presented either in quiet or in one of the noise maskers, including a steady-state noise, a 16-Hz or 32-Hz temporally modulated noise, or a spectrally modulated noise. The dental students showed no difference from the control group in demographic information, audiological screening outcomes, extended high-frequency thresholds, or unvoiced speech in quiet, but consistently performed more poorly for unvoiced speech recognition in modulated noise. The group difference in noise depended on the filtering conditions. The dental group's degraded performances were observed in temporally modulated noise for high-pass filtered condition only and in spectrally modulated noise for low-pass filtered condition only. The current findings provide the most direct evidence to date of a link between non-traumatic noise exposure and supra-threshold envelope processing issues in human listeners despite the normal audiological profiles.

Human mirror neuron system responsivity to unimodal and multimodal presentations of action

This study aims to clarify unresolved questions from two earlier studies by McGarry et al. Exp Brain Res 218(4): 527-538, 2012 and Kaplan and Iacoboni Cogn Process 8: 103-113, 2007 on human mirror neuron system (hMNS) responsivity to multimodal presentations of actions. These questions are: (1) whether the two frontal areas originally identified by Kaplan and Iacoboni (ventral premotor cortex [vPMC] and inferior frontal gyrus [IFG]) are both part of the hMNS (i.e., do they respond to execution as well as observation), (2) whether both areas yield effects of biologicalness (biological, control) and modality (audio, visual, audiovisual), and (3) whether the vPMC is preferentially responsive to multimodal input. To resolve these questions about the hMNS, we replicated and extended McGarry et al.'s electroencephalography (EEG) study, while incorporating advanced source localization methods. Participants were asked to execute movements (ripping paper) as well as observe those movements across the same three modalities (audio, visual, and audiovisual), all while 64-channel EEG data was recorded. Two frontal sources consistent with those identified in prior studies showed mu event-related desynchronization (mu-ERD) under execution and observation conditions. These sources also showed a greater response to biological movement than to control stimuli as well as a distinct visual advantage, with greater responsivity to visual and audiovisual compared to audio conditions. Exploratory analyses of mu-ERD in the vPMC under visual and audiovisual observation conditions suggests that the hMNS tracks the magnitude of visual movement over time.

Context Sensitivity across Multiple Time scales with a Flexible Frequency Bandwidth

Everyday auditory streams are complex, including spectro-temporal content that varies at multiple timescales. Using EEG, we investigated the sensitivity of human auditory cortex to the content of past stimulation in unattended sequences of equiprobable tones. In 3 experiments including 82 participants overall, we found that neural responses measured at different latencies after stimulus onset were sensitive to frequency intervals computed over distinct timescales. Importantly, early responses were sensitive to a longer history of stimulation than later responses. To account for these results, we tested a model consisting of neural populations with frequency-specific but broad tuning that undergo adaptation with exponential recovery. We found that the coexistence of neural populations with distinct recovery rates can explain our results. Furthermore, the adaptation bandwidth of these populations depended on spectral context-it was wider when the stimulation sequence had a wider frequency range. Our results provide electrophysiological evidence as well as a possible mechanistic explanation for dynamic and multiscale context-dependent auditory processing in the human cortex.

Sound level context modulates neural activity in the human brainstem

Optimal perception requires adaptation to sounds in the environment. Adaptation involves representing the acoustic stimulation history in neural response patterns, for example, by altering response magnitude or latency as sound-level context changes. Neurons in the auditory brainstem of rodents are sensitive to acoustic stimulation history and sound-level context (often referred to as sensitivity to stimulus statistics), but the degree to which the human brainstem exhibits such neural adaptation is unclear. In six electroencephalography experiments with over 125 participants, we demonstrate that the response latency of the human brainstem is sensitive to the history of acoustic stimulation over a few tens of milliseconds. We further show that human brainstem responses adapt to sound-level context in, at least, the last 44 ms, but that neural sensitivity to sound-level context decreases when the time window over which acoustic stimuli need to be integrated becomes wider. Our study thus provides evidence of adaptation to sound-level context in the human brainstem and of the timescale over which sound-level information affects neural responses to sound. The research delivers an important link to studies on neural adaptation in non-human animals.

Understanding degraded speech leads to perceptual gating of a brainstem reflex in human listeners

The ability to navigate "cocktail party" situations by focusing on sounds of interest over irrelevant, background sounds is often considered in terms of cortical mechanisms. However, subcortical circuits such as the pathway underlying the medial olivocochlear (MOC) reflex modulate the activity of the inner ear itself, supporting the extraction of salient features from auditory scene prior to any cortical processing. To understand the contribution of auditory subcortical nuclei and the cochlea in complex listening tasks, we made physiological recordings along the auditory pathway while listeners engaged in detecting non(sense) words in lists of words. Both naturally spoken and intrinsically noisy, vocoded speech-filtering that mimics processing by a cochlear implant (CI)-significantly activated the MOC reflex, but this was not the case for speech in background noise, which more engaged midbrain and cortical resources. A model of the initial stages of auditory processing reproduced specific effects of each form of speech degradation, providing a rationale for goal-directed gating of the MOC reflex based on enhancing the representation of the energy envelope of the acoustic waveform. Our data reveal the coexistence of 2 strategies in the auditory system that may facilitate speech understanding in situations where the signal is either intrinsically degraded or masked by extrinsic acoustic energy. Whereas intrinsically degraded streams recruit the MOC reflex to improve representation of speech cues peripherally, extrinsically masked streams rely more on higher auditory centres to denoise signals.

When and How Does the Auditory Cortex Influence Subcortical Auditory Structures? New Insights About the Roles of Descending Cortical Projections

For decades, the corticofugal descending projections have been anatomically well described but their functional role remains a puzzling question. In this review, we will first describe the contributions of neuronal networks in representing communication sounds in various types of degraded acoustic conditions from the cochlear nucleus to the primary and secondary auditory cortex. In such situations, the discrimination abilities of collicular and thalamic neurons are clearly better than those of cortical neurons although the latter remain very little affected by degraded acoustic conditions. Second, we will report the functional effects resulting from activating or inactivating corticofugal projections on functional properties of subcortical neurons. In general, modest effects have been observed in anesthetized and in awake, passively listening, animals. In contrast, in behavioral tasks including challenging conditions, behavioral performance was severely reduced by removing or transiently silencing the corticofugal descending projections. This suggests that the discriminative abilities of subcortical neurons may be sufficient in many acoustic situations. It is only in particularly challenging situations, either due to the task difficulties and/or to the degraded acoustic conditions that the corticofugal descending connections bring additional abilities. Here, we propose that it is both the top-down influences from the prefrontal cortex, and those from the neuromodulatory systems, which allow the cortical descending projections to impact behavioral performance in reshaping the functional circuitry of subcortical structures. We aim at proposing potential scenarios to explain how, and under which circumstances, these projections impact on subcortical processing and on behavioral responses.

Dynamic functional network connectivity associated with musical emotions evoked by different tempi

Background：Music tempo has strong clinical maneuverability and positive emotional effect in music therapy, which can directly evoke multiple emotions and dynamic neural changes in the whole-brain. However, the precise relationship between music tempo and its emotional effects remains unclear. The present study aimed to investigate the dynamic functional network connectivity (dFNC) associated with emotions elicited by music at different tempi.

METHODS

We obtained emotion ratings of fast- (155-170 bpm), middle- (90 bpm), and slow-tempo (50-60 bpm) piano music from 40 participants both during and after functional magnetic resonance imaging (fMRI). Group independent component analysis (ICA), sliding time window correlations, and k-means clustering were used to assess dFNC of fMRI data. Paired t-tests were conducted to compare the difference of neural networks.

RESULTS

(1) Fast music was associated with higher ratings of emotional valence and arousal, which were accompanied with increasing dFNC between somatomotor (SM) and cingulo-opercular (CO) networks and decreasing dFNC between fronto-parietal and SM networks. (2) Even with stronger activation in auditory (AUD) networks, slow music was associated with weaker emotion than fast music, with decreasing FNC across the brain and the participation of default mode (DM). (3) Middle-tempo music elicited moderate emotional activation with the most stable dFNC in the whole brain.

CONCLUSION

Faster music increases neural activity in the SM and CO regions, increasing the intensity of the emotional experience. In contrast, slower music was associated with decreasing engagement of AUD and stable engagement of DM, resulting in a weak emotional experience. These findings suggested that the time-varying aspects of functional connectivity can help to uncover the dynamic neural substrates of tempo-evoked emotion while listening to music.

The role of the medial olivocochlear reflex in psychophysical masking and intensity resolution in humans: a review

This review addresses the putative role of the medial olivocochlear (MOC) reflex in psychophysical masking and intensity resolution in humans. A framework for interpreting psychophysical results in terms of the expected influence of the MOC reflex is introduced. This framework is used to review the effects of a precursor or contralateral acoustic stimulation on 1) simultaneous masking of brief tones, 2) behavioral estimates of cochlear gain and frequency resolution in forward masking, 3) the buildup and decay of forward masking, and 4) measures of intensity resolution. Support, or lack thereof, for a role of the MOC reflex in psychophysical perception is discussed in terms of studies on estimates of MOC strength from otoacoustic emissions and the effects of resection of the olivocochlear bundle in patients with vestibular neurectomy. Novel, innovative approaches are needed to resolve the dissatisfying conclusion that current results are unable to definitively confirm or refute the role of the MOC reflex in masking and intensity resolution.

Contributions of natural signal statistics to spectral context effects in consonant categorization

Speech perception, like all perception, takes place in context. Recognition of a given speech sound is influenced by the acoustic properties of surrounding sounds. When the spectral composition of earlier (context) sounds (e.g., a sentence with more energy at lower third formant [F₃] frequencies) differs from that of a later (target) sound (e.g., consonant with intermediate F₃ onset frequency), the auditory system magnifies this difference, biasing target categorization (e.g., towards higher-F₃-onset /d/). Historically, these studies used filters to force context stimuli to possess certain spectral compositions. Recently, these effects were produced using unfiltered context sounds that already possessed the desired spectral compositions (Stilp & Assgari, 2019, Attention, Perception, & Psychophysics, 81, 2037-2052). Here, this natural signal statistics approach is extended to consonant categorization (/g/-/d/). Context sentences were either unfiltered (already possessing the desired spectral composition) or filtered (to imbue specific spectral characteristics). Long-term spectral characteristics of unfiltered contexts were poor predictors of shifts in consonant categorization, but short-term characteristics (last 475 ms) were excellent predictors. This diverges from vowel data, where long-term and shorter-term intervals (last 1,000 ms) were equally strong predictors. Thus, time scale plays a critical role in how listeners attune to signal statistics in the acoustic environment.

Robustness to Noise in the Auditory System: A Distributed and Predictable Property

Background noise strongly penalizes auditory perception of speech in humans or vocalizations in animals. Despite this, auditory neurons are still able to detect communications sounds against considerable levels of background noise. We collected neuronal recordings in cochlear nucleus (CN), inferior colliculus (IC), auditory thalamus, and primary and secondary auditory cortex in response to vocalizations presented either against a stationary or a chorus noise in anesthetized guinea pigs at three signal-to-noise ratios (SNRs; −10, 0, and 10 dB). We provide evidence that, at each level of the auditory system, five behaviors in noise exist within a continuum, from neurons with high-fidelity representations of the signal, mostly found in IC and thalamus, to neurons with high-fidelity representations of the noise, mostly found in CN for the stationary noise and in similar proportions in each structure for the chorus noise. The two cortical areas displayed fewer robust responses than the IC and thalamus. Furthermore, between 21% and 72% of the neurons (depending on the structure) switch categories from one background noise to another, even if the initial assignment of these neurons to a category was confirmed by a severe bootstrap procedure. Importantly, supervised learning pointed out that assigning a recording to one of the five categories can be predicted up to a maximum of 70% based on both the response to signal alone and noise alone.

Temporal Prediction Signals for Periodic Sensory Events in the Primate Central Thalamus

Prediction of periodic event timing is an important function for everyday activities, while the exact neural mechanism remains unclear. Previous studies in nonhuman primates have demonstrated that neurons in the cerebellar dentate nucleus and those in the caudate nucleus exhibit periodic firing modulation when the animals attempt to detect a single omission of isochronous repetitive audiovisual stimuli. To understand how these subcortical signals are sent and processed through the thalamocortical pathways, we examined single-neuron activities in the central thalamus of two macaque monkeys (one female and one male). We found that three types of neurons responded to each stimulus in the sequence in the absence of movements. Reactive-type neurons showed sensory adaptation and gradually waned the transient response to each stimulus. Predictive-type neurons steadily increased the magnitude of the suppressive response, similar to neurons previously reported in the cerebellum. Switch-type neurons initially showed a transient response, but after several cycles, the direction of firing modulation reversed and the activity decreased for each repetitive stimulus. The time course of Switch-type activity was well explained by the weighted sum of activities of the other types of neurons. Furthermore, for only Switch-type neurons the activity just before stimulus omission significantly correlated with behavioral latency, indicating that this type of neuron may carry a more advanced signal in the system detecting stimulus omission. These results suggest that the central thalamus may transmit integrated signals to the cerebral cortex for temporal information processing, which are necessary to accurately predict rhythmic event timing.SIGNIFICANCE STATEMENT Several cortical and subcortical regions are involved in temporal information processing, and the thalamus will play a role in functionally linking them. The present study aimed to clarify how the paralaminar part of the thalamus transmits and modifies signals for temporal prediction of rhythmic events. Three types of thalamic neurons exhibited periodic activity when monkeys attempted to detect a single omission of isochronous repetitive stimuli. The activity of one type of neuron correlated with the behavioral latency and appeared to be generated by integrating the signals carried by the other types of neurons. Our results revealed the neuronal signals in the thalamus for temporal prediction of sensory events, providing a clue to elucidate information processing in the thalamocortical pathways.

Effects of task and language nativeness on the Lombard effect and on its onset and offset timing

This study focuses on the differences in speech sound pressure levels (here, called speech loudness) of Lombard speech (i.e., speech produced in the presence of an energetic masker) associated with different tasks and language nativeness. Vocalizations were produced by native speakers of Japanese with normal hearing and limited English proficiency while performing four tasks: dialog, a competitive game (both communicative), soliloquy, and text passage reading (noncommunicative). Relative to the native language (L1), larger loudness increments were observed in the game and text reading when performed in the second language (L2). Communicative tasks yielded louder vocalizations and larger increments of speech loudness than did noncommunicative tasks regardless of the spoken language. The period in which speakers increased their loudness after the onset of the masker was about fourfold longer than the time in which they decreased their loudness after the offset of the masker. Results suggest that when relying on acoustic signals, speakers use similar vocalization strategies in L1 and L2, and these depend on the complexity of the task, the need for accurate pronunciation, and the presence of a listener. Results also suggest that speakers use different strategies depending on the onset or offset of an energetic masker.

The effect of input noises on the activity of auditory neurons using GLM-based metrics

CONTEXT

The auditory system is extremely efficient in extracting auditory information in the presence of background noise. However, people with auditory implants have a hard time understanding speech in noisy conditions. Understanding the mechanisms of perception in noise could lead to better stimulation or preprocessing strategies for such implants.

OBJECTIVE

The neural mechanisms related to the processing of background noise, especially in the inferior colliculus (IC) where the auditory midbrain implant is located, are still not well understood. We thus wish to investigate if there is a difference in the activity of neurons in the IC when presenting noisy vocalizations with different types of noise (stationary vs. non-stationary), input signal-to-noise ratios (SNR) and signal levels.

APPROACH

We developed novel metrics based on a generalized linear model (GLM) to investigate the effect of a given input noise on neural activity. We used these metrics to analyze neural data recorded from the IC in ketamine-anesthetized female Hartley guinea pigs while presenting noisy vocalizations.

MAIN RESULTS

We found that non-stationary noise clearly contributes to the multi-unit neural activity in the IC by causing excitation, regardless of the SNR, input level or vocalization type. However, when presenting white or natural stationary noises, a great diversity of responses was observed for the different conditions, where the multi-unit activity of some sites was affected by the presence of noise and the activity of others was not.

SIGNIFICANCE

The GLM-based metrics allowed the identification of a clear distinction between the effect of white or natural stationary noises and that of non-stationary noise on the multi-unit activity in the IC. This had not been observed before and indicates that the so-called noise invariance in the IC is dependent on the input noisy conditions. This could suggest different preprocessing or stimulation approaches for auditory midbrain implants depending on the noisy conditions.

Effects of noise precursors on the detection of amplitude and frequency modulation for tones in noise

Recent studies on amplitude modulation (AM) detection for tones in noise reported that AM-detection thresholds improve when the AM stimulus is preceded by a noise precursor. The physiological mechanisms underlying this AM unmasking are unknown. One possibility is that adaptation to the level of the noise precursor facilitates AM encoding by causing a shift in neural rate-level functions to optimize level encoding around the precursor level. The aims of this study were to investigate whether such a dynamic-range adaptation is a plausible mechanism for the AM unmasking and whether frequency modulation (FM), thought to be encoded via AM, also exhibits the unmasking effect. Detection thresholds for AM and FM of tones in noise were measured with and without a fixed-level precursor. Listeners showing the unmasking effect were then tested with the precursor level roved over a wide range to modulate the effect of adaptation to the precursor level on the detection of the subsequent AM. It was found that FM detection benefits from a precursor and the magnitude of FM unmasking correlates with that of AM unmasking. Moreover, consistent with dynamic-range adaptation, the unmasking magnitude weakens as the level difference between the precursor and simultaneous masker of the tone increases.

Simple transformations capture auditory input to cortex

Sounds are processed by the ear and central auditory pathway. These processing steps are biologically complex, and many aspects of the transformation from sound waveforms to cortical response remain unclear. To understand this transformation, we combined models of the auditory periphery with various encoding models to predict auditory cortical responses to natural sounds. The cochlear models ranged from detailed biophysical simulations of the cochlea and auditory nerve to simple spectrogram-like approximations of the information processing in these structures. For three different stimulus sets, we tested the capacity of these models to predict the time course of single-unit neural responses recorded in ferret primary auditory cortex. We found that simple models based on a log-spaced spectrogram with approximately logarithmic compression perform similarly to the best-performing biophysically detailed models of the auditory periphery, and more consistently well over diverse natural and synthetic sounds. Furthermore, we demonstrated that including approximations of the three categories of auditory nerve fiber in these simple models can substantially improve prediction, particularly when combined with a network encoding model. Our findings imply that the properties of the auditory periphery and central pathway may together result in a simpler than expected functional transformation from ear to cortex. Thus, much of the detailed biological complexity seen in the auditory periphery does not appear to be important for understanding the cortical representation of sound.

Foreground stimuli and task engagement enhance neuronal adaptation to background noise in the inferior colliculus of macaques

Auditory neuronal responses are modified by background noise. Inferior colliculus (IC) neuronal responses adapt to the most frequent sound level within an acoustic scene (adaptation to stimulus statistics), a mechanism that may preserve neuronal and behavioral thresholds for signal detection. However, it is still unclear whether the presence of foreground stimuli and/or task involvement can modify neuronal adaptation. To investigate how task engagement interacts with this mechanism, we compared the response of IC neurons to background noise, which caused adaptation to stimulus statistics, while macaque monkeys performed a masked tone detection task (task-driven condition) with responses recorded when the same background noise was presented alone (passive listening condition). In the task-dependent condition, monkeys performed a Go/No-Go task while 50-ms tones were embedded within an adaptation-inducing continuous background noise whose levels changed every 50 ms and were drawn from a probability distribution. The adaptation to noise stimulus statistics in IC neuronal responses was significantly enhanced in the task-driven condition compared with the passive listening condition, showing that foreground stimuli and/or task-engagement can modify IC neuronal responses. Additionally, the response of IC neurons to noise was significantly affected by the preceding sensory information (history effect) regardless of task involvement. These studies show that dynamic range adaptation in IC preserves behavioral and neurometric thresholds irrespective of noise type and a dependence of neuronal activity on task-related factors at subcortical levels of processing.NEW & NOTEWORTHY Auditory neuronal responses are influenced by maskers and distractors. However, it is still unclear whether the neuronal sensitivity to the masker stimulus is influenced by task-dependent factors. Our study represents one of the first attempts to investigate how task involvement influences the neural representation of background sounds in the subcortical, midbrain auditory neurons of behaving animals.

Adaptation to Noise in Human Speech Recognition Depends on Noise-Level Statistics and Fast Dynamic-Range Compression

Human hearing adapts to background noise, as evidenced by the fact that listeners recognize more isolated words when words are presented later rather than earlier in noise. This adaptation likely occurs because the leading noise shifts ("adapts") the dynamic range of auditory neurons, which can improve the neural encoding of speech spectral and temporal cues. Because neural dynamic range adaptation depends on stimulus-level statistics, here we investigated the importance of "statistical" adaptation for improving speech recognition in noisy backgrounds. We compared the recognition of noised-masked words in the presence and in the absence of adapting noise precursors whose level was either constant or was changing every 50 ms according to different statistical distributions. Adaptation was measured for 28 listeners (9 men) and was quantified as the recognition improvement in the precursor relative to the no-precursor condition. Adaptation was largest for constant-level precursors and did not occur for highly fluctuating precursors, even when the two types of precursors had the same mean level and both activated the medial olivocochlear reflex. Instantaneous amplitude compression of the highly fluctuating precursor produced as much adaptation as the constant-level precursor did without compression. Together, results suggest that noise adaptation in speech recognition is probably mediated by neural dynamic range adaptation to the most frequent sound level. Further, they suggest that auditory peripheral compression per se, rather than the medial olivocochlear reflex, could facilitate noise adaptation by reducing the level fluctuations in the noise.SIGNIFICANCE STATEMENT Recognizing speech in noise is challenging but can be facilitated by noise adaptation. The neural mechanisms underlying this adaptation remain unclear. Here, we report some benefits of adaptation for word-in-noise recognition and show that (1) adaptation occurs for stationary but not for highly fluctuating precursors with equal mean level; (2) both stationary and highly fluctuating noises activate the medial olivocochlear reflex; and (3) adaptation occurs even for highly fluctuating precursors when the stimuli are passed through a fast amplitude compressor. These findings suggest that noise adaptation reflects neural dynamic range adaptation to the most frequent noise level and that auditory peripheral compression, rather than the medial olivocochlear reflex, could facilitate noise adaptation.

Estimating and interpreting nonlinear receptive field of sensory neural responses with deep neural network models

Our understanding of nonlinear stimulus transformations by neural circuits is hindered by the lack of comprehensive yet interpretable computational modeling frameworks. Here, we propose a data-driven approach based on deep neural networks to directly model arbitrarily nonlinear stimulus-response mappings. Reformulating the exact function of a trained neural network as a collection of stimulus-dependent linear functions enables a locally linear receptive field interpretation of the neural network. Predicting the neural responses recorded invasively from the auditory cortex of neurosurgical patients as they listened to speech, this approach significantly improves the prediction accuracy of auditory cortical responses, particularly in nonprimary areas. Moreover, interpreting the functions learned by neural networks uncovered three distinct types of nonlinear transformations of speech that varied considerably from primary to nonprimary auditory regions. The ability of this framework to capture arbitrary stimulus-response mappings while maintaining model interpretability leads to a better understanding of cortical processing of sensory signals.

Neural Responses and Perceptual Sensitivity to Sound Depend on Sound-Level Statistics

Sensitivity to sound-level statistics is crucial for optimal perception, but research has focused mostly on neurophysiological recordings, whereas behavioral evidence is sparse. We use electroencephalography (EEG) and behavioral methods to investigate how sound-level statistics affect neural activity and the detection of near-threshold changes in sound amplitude. We presented noise bursts with sound levels drawn from distributions with either a low or a high modal sound level. One participant group listened to the stimulation while EEG was recorded (Experiment I). A second group performed a behavioral amplitude-modulation detection task (Experiment II). Neural activity depended on sound-level statistical context in two different ways. Consistent with an account positing that the sensitivity of neurons to sound intensity adapts to ambient sound level, responses for higher-intensity bursts were larger in low-mode than high-mode contexts, whereas responses for lower-intensity bursts did not differ between contexts. In contrast, a concurrent slow neural response indicated prediction-error processing: The response was larger for bursts at intensities that deviated from the predicted statistical context compared to those not deviating. Behavioral responses were consistent with prediction-error processing, but not with neural adaptation. Hence, neural activity adapts to sound-level statistics, but fine-tuning of perceptual sensitivity appears to involve neural prediction-error responses.

Auditory cortical representation of music favours the perceived beat

Previous research has shown that musical beat perception is a surprisingly complex phenomenon involving widespread neural coordination across higher-order sensory, motor and cognitive areas. However, the question of how low-level auditory processing must necessarily shape these dynamics, and therefore perception, is not well understood. Here, we present evidence that the auditory cortical representation of music, even in the absence of motor or top-down activations, already favours the beat that will be perceived. Extracellular firing rates in the rat auditory cortex were recorded in response to 20 musical excerpts diverse in tempo and genre, for which musical beat perception had been characterized by the tapping behaviour of 40 human listeners. We found that firing rates in the rat auditory cortex were on average higher on the beat than off the beat. This 'neural emphasis' distinguished the beat that was perceived from other possible interpretations of the beat, was predictive of the degree of tapping consensus across human listeners, and was accounted for by a spectrotemporal receptive field model. These findings strongly suggest that the 'bottom-up' processing of music performed by the auditory system predisposes the timing and clarity of the perceived musical beat.

Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints

Optimality principles have been useful in explaining many aspects of biological systems. In the context of neural encoding in sensory areas, optimality is naturally formulated in a Bayesian setting as neural tuning which minimizes mean decoding error. Many works optimize Fisher information, which approximates the minimum mean square error (MMSE) of the optimal decoder for long encoding time but may be misleading for short encoding times. We study MMSE-optimal neural encoding of a multivariate stimulus by uniform populations of spiking neurons, under firing rate constraints for each neuron as well as for the entire population. We show that the population-level constraint is essential for the formulation of a well-posed problem having finite optimal tuning widths and optimal tuning aligns with the principal components of the prior distribution. Numerical evaluation of the two-dimensional case shows that encoding only the dimension with higher variance is optimal for short encoding times. We also compare direct MMSE optimization to optimization of several proxies to MMSE: Fisher information, maximum likelihood estimation error, and the Bayesian Cramér-Rao bound. We find that optimization of these measures yields qualitatively misleading results regarding MMSE-optimal tuning and its dependence on encoding time and energy constraints.

Mechanisms underlying gain modulation in the cortex

Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition.

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

Perception of vocalizations and other behaviorally relevant sounds requires integrating acoustic information over hundreds of milliseconds. Sound-evoked activity in auditory cortex typically has much shorter latency, but the acoustic context, i.e., sound history, can modulate sound evoked activity over longer periods. Contextual effects are attributed to modulatory phenomena, such as stimulus-specific adaption and contrast gain control. However, an encoding model that links context to natural sound processing has yet to be established. We tested whether a model in which spectrally tuned inputs undergo adaptation mimicking short-term synaptic plasticity (STP) can account for contextual effects during natural sound processing. Single-unit activity was recorded from primary auditory cortex of awake ferrets during presentation of noise with natural temporal dynamics and fully natural sounds. Encoding properties were characterized by a standard linear-nonlinear spectro-temporal receptive field (LN) model and variants that incorporated STP-like adaptation. In the adapting models, STP was applied either globally across all input spectral channels or locally to subsets of channels. For most neurons, models incorporating local STP predicted neural activity as well or better than LN and global STP models. The strength of nonlinear adaptation varied across neurons. Within neurons, adaptation was generally stronger for spectral channels with excitatory than inhibitory gain. Neurons showing improved STP model performance also tended to undergo stimulus-specific adaptation, suggesting a common mechanism for these phenomena. When STP models were compared between passive and active behavior conditions, response gain often changed, but average STP parameters were stable. Thus, spectrally and temporally heterogeneous adaptation, subserved by a mechanism with STP-like dynamics, may support representation of the complex spectro-temporal patterns that comprise natural sounds across wide-ranging sensory contexts.

Adaptation in the auditory system of a beluga whale: effect of adapting sound parameters

The effects of adapting sounds (pip trains or pure tones) on auditory evoked potentials (the rate following response, RFR) were investigated in a beluga whale. During RFR acquisition, adapting signals lasting 128 ms each were alternated with test signals lasting 16 ms each; the test signal levels varied randomly. Adapting signals were trains of cosine-enveloped tone pips or pure tones. Pip rate varied with the envelope cosine cycle maintained at 0.125 of pip intervals and the cosine rise-fall time maintained at 0.0625 of pip intervals. Adapting signals shifted the amplitude-level function upward compared to the baseline (no adapting signal) function. The higher the adapting signal level was, the bigger the shift in the amplitude-level function was. The slower the pips were in the adapting signal, the smaller the adaptation effect was. A train of pips with a 0.0625-ms rise-fall time and 125 dB SPL shifted the function by 35-40 dB, whereas a train of pips with a 1-ms rise-fall time or a pure tone with the same SPL shifted the function by approximately 15 dB. The difference between the "fast" and "slow" adapting signals is supposed to be associated with their abilities to stimulate the auditory system in odontocetes.

Coding Principles in Adaptation

Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena. In this review, we discuss several of these theoretical frameworks, highlighting relationships among them and clarifying distinctions. We summarize observations of the varied manifestations of adaptation, particularly as they relate to these theoretical frameworks, focusing throughout on the visual system and making connections to other sensory systems.

Exploring the Role of Medial Olivocochlear Efferents on the Detection of Amplitude Modulation for Tones Presented in Noise

The medial olivocochlear reflex has been hypothesized to improve the detection and discrimination of dynamic signals in noisy backgrounds. This hypothesis was tested here by comparing behavioral outcomes with otoacoustic emissions. The effects of a precursor on amplitude-modulation (AM) detection were measured for a 1- and 6-kHz carrier at levels of 40, 60, and 80 dB SPL in a two-octave-wide noise masker with a level designed to produce poor, but above-chance, performance. Three types of precursor were used: a two-octave noise band, an inharmonic complex tone, and a pure tone. Precursors had the same overall level as the simultaneous noise masker that immediately followed the precursor. The noise precursor produced a large improvement in AM detection for both carrier frequencies and at all three levels. The complex tone produced a similarly large improvement in AM detection at the highest level but had a smaller effect for the two lower carrier levels. The tonal precursor did not significantly affect AM detection in noise. Comparisons of behavioral thresholds and medial olivocochlear efferent effects on stimulus frequency otoacoustic emissions measured with similar stimuli did not support the hypothesis that efferent-based reduction of cochlear responses contributes to the precursor effects on AM detection.

Contrast and luminance adaptation alter neuronal coding and perception of stimulus orientation

Sensory systems face a barrage of stimulation that continually changes along multiple dimensions. These simultaneous changes create a formidable problem for the nervous system, as neurons must dynamically encode each stimulus dimension, despite changes in other dimensions. Here, we measured how neurons in visual cortex encode orientation following changes in luminance and contrast, which are critical for visual processing, but nuisance variables in the context of orientation coding. Using information theoretic analysis and population decoding approaches, we find that orientation discriminability is luminance and contrast dependent, changing over time due to firing rate adaptation. We also show that orientation discrimination in human observers changes during adaptation, in a manner consistent with the neuronal data. Our results suggest that adaptation does not maintain information rates per se, but instead acts to keep sensory systems operating within the limited dynamic range afforded by spiking activity, despite a wide range of possible inputs.

Sensory systems produce stable stimulus representations despite constant changes across multiple stimulus dimensions. Here, the authors reveal dynamic neural coding mechanisms by testing how coding of one dimension (orientation) changes with adaptations to other dimensions (luminance and contrast).

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.