Power-law dynamics in an auditory-nerve model can account for neural adaptation to sound-level statistics

Biophysics-inspired spike rate adaptation for computationally efficient phenomenological nerve modeling

This study introduces and evaluates the PHAST+ model, part of a computational framework designed to simulate the behavior of auditory nerve fibers in response to the electrical stimulation from a cochlear implant. PHAST+ incorporates a highly efficient method for calculating accommodation and adaptation, making it particularly suited for simulations over extended stimulus durations. The proposed method uses a leaky integrator inspired by classic biophysical nerve models. Through evaluation against single-fiber animal data, our findings demonstrate the model's effectiveness across various stimuli, including short pulse trains with variable amplitudes and rates. Notably, the PHAST+ model performs better than its predecessor, PHAST (a phenomenological model by van Gendt et al.), particularly in simulations of prolonged neural responses. While PHAST+ is optimized primarily on spike rate decay, it shows good behavior on several other neural measures, such as vector strength and degree of adaptation. The future implications of this research are promising. PHAST+ drastically reduces the computational burden to allow the real-time simulation of neural behavior over extended periods, opening the door to future simulations of psychophysical experiments and multi-electrode stimuli for evaluating novel speech-coding strategies for cochlear implants.

Contextual Lateralization Based on Interaural Level Differences Is Preshaped by the Auditory Periphery and Predominantly Immune Against Sequential Segregation

The perceived azimuth of a target sound is determined by the interaural time difference and the interaural level difference (ILD) and is subject to contextual effects from precursor sounds. This study characterized ILD-based precursor effects (PEs) for high-frequency stimuli in a total of seven normal-hearing listeners. In Experiment 1, precursor and target were band-pass-filtered noises approximately centered at 4 kHz (1.2- and 1-octave bandwidth, respectively) separated by a 10-ms gap. The effects of precursor location (ipsilateral, contralateral, and central) on the perceived target azimuth were measured using a head-pointing task. Relative to control trials without a precursor, ipsilateral precursors biased the perceived target azimuth toward midline (medial bias) and contralateral precursors biased it contralaterally (lateral bias). Central precursors caused a symmetric lateral bias. An auditory periphery model that determines the "internal" ILD at the auditory nerve level, including either realistic efferent compression control or auditory nerve adaptation, explained about 50% of the variance in the PEs. These within-trial PEs were accompanied by an across-trial PE, inducing medial bias. Experiment 2 studied the role of sequential segregation in the within-trial PE by introducing a pitch difference between precursor and target. Segregation conditions caused increased PE for ipsilateral, no effect for contralateral, and either no effect or reduced PE for central precursors. Overall, the ILD-based within-trial PE appears to be preshaped already in the auditory periphery and the mechanism underlying at least the ipsilateral PE appears to be immune against sequential segregation.

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.

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.

A simplified physiological model of rate-level functions of auditory-nerve fibers

Several approaches have been used to describe the rate-level functions of auditory-nerve fibers (ANFs). One approach uses descriptive models that can be fitted easily to data. Another derives rate-level functions from comprehensive physiological models of auditory peripheral processing. Here, we seek to identify the minimal set of components needed to provide a physiologically plausible account of rate-level functions. Our model consists of a first-order Boltzmann mechanoelectrical transducer function relating the instantaneous stimulus pressure to an instantaneous output, followed by a lowpass filter that eliminates the AC component, followed by an exponential synaptic transfer function relating the DC component to the mean spike rate. This is perhaps the simplest physiologically plausible model capable of accounting for rate-level functions under the assumption that the model parameters for a given ANF and stimulus frequency are level-independent. We find that the model typically accounts well for rate-level functions from cat ANFs for all stimulus frequencies. More complicated model variants having saturating synaptic transfer functions do not perform significantly better, implying the system operates far away from synaptic saturation. Rate saturation in the model is caused by saturation of the DC component of the filter output (e.g., the receptor potential), which in turn is due to the saturation of the transducer function. The maximum mean spike rate is approximately constant across ANFs, such that the slope parameter of the exponential synaptic transfer function decreases with increasing spontaneous rate. If the synaptic parameters for a given ANF are assumed to be constant across stimulus frequencies, then frequency- and level-dependent input nonlinearities are derived that are qualitatively similar to those reported in the literature. Contrary to assumptions in the literature, such nonlinearities are obtained even for ANFs having high spontaneous rates. Finally, spike-rate adaptation is examined and found to be accounted for by a decrease in the slope parameter of the synaptic transfer function over time following stimulus onset.

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.

Auditory Brainstem Models: Adapting Cochlear Nuclei Improve Spatial Encoding by the Medial Superior Olive in Reverberation

Listeners typically perceive a sound as originating from the direction of its source, even as direct sound is followed milliseconds later by reflected sound from multiple different directions. Early-arriving sound is emphasised in the ascending auditory pathway, including the medial superior olive (MSO) where binaural neurons encode the interaural-time-difference (ITD) cue for spatial location. Perceptually, weighting of ITD conveyed during rising sound energy is stronger at 600 Hz than at 200 Hz, consistent with the minimum stimulus rate for binaural adaptation, and with the longer reverberation times at 600 Hz, compared with 200 Hz, in many natural outdoor environments. Here, we computationally explore the combined efficacy of adaptation prior to the binaural encoding of ITD cues, and excitatory binaural coincidence detection within MSO neurons, in emphasising ITDs conveyed in early-arriving sound. With excitatory inputs from adapting, nonlinear model spherical bushy cells (SBCs) of the bilateral cochlear nuclei, a nonlinear model MSO neuron with low-threshold potassium channels reproduces the rate-dependent emphasis of rising vs. peak sound energy in ITD encoding; adaptation is equally effective in the model MSO. Maintaining adaptation in model SBCs, and adjusting membrane speed in model MSO neurons, 'left' and 'right' populations of computationally efficient, linear model SBCs and MSO neurons reproduce this stronger weighting of ITD conveyed during rising sound energy at 600 Hz compared to 200 Hz. This hemispheric population model demonstrates a link between strong weighting of spatial information during rising sound energy, and correct unambiguous lateralisation of a speech source in reverberation.

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.

Tinnitus and hyperacusis: Central noise, gain and variance

Tinnitus is a phantom auditory sensation in the absence of external sounds, while hyperacusis is an atypical sensitivity to external sounds that leads them to be perceived as abnormally loud or even painful. Both conditions may reflect the brain's over-compensation for reduced input from the ear. The present work differentiates between two compensation models: The additive central noise compensates for hearing loss and is likely to generate tinnitus, whereas the multiplicative central gain compensates for hidden hearing loss and is likely to generate hyperacusis. Importantly, both models predict increased variance in central representations of sounds, especially a nonlinear increase in variance by the central gain. The increased central variance limits the amount of central compensation and reduces temporal synchrony, which can explain the insufficient central gain reported in the literature. Future studies need to collect trial-by-trial firing variance data so that the present variance-based model can be falsified.

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.

Short and long-term adaptation in the auditory nerve stimulated with high-rate electrical pulse trains are better described by a power law

Despite the introduction of many new sound-coding strategies speech perception outcomes in cochlear implant listeners have leveled off. Computer models may help speed up the evaluation of new sound-coding strategies, but most existing models of auditory nerve responses to electrical stimulation include limited temporal detail, as the effects of longer stimulation, such as adaptation, are not well-studied. Measured neural responses to stimulation with both short (400 ms) and long (10 min) duration high-rate (5kpps) pulse trains were compared in terms of spike rate and vector strength (VS) with model outcomes obtained with different forms of adaptation. A previously published model combining biophysical and phenomenological approaches was adjusted with adaptation modeled as a single decaying exponent, multiple exponents and a power law. For long duration data, power law adaptation by far outperforms the single exponent model, especially when it is optimized per fiber. For short duration data, all tested models performed comparably well, with slightly better performance of the single exponent model for VS and of the power law model for the spike rates. The power law parameter sets obtained when fitted to the long duration data also yielded adequate predictions for short duration stimulation, and vice versa. The power law function can be approximated with multiple exponents, which is physiologically more viable. The number of required exponents depends on the duration of simulation; the 400 ms data was well-replicated by two exponents (23 and 212 ms), whereas the 10-minute data required at least seven exponents (ranging from 4 ms to 600 s). Adaptation of the auditory nerve to high-rate electrical stimulation can best be described by a power-law or a sum of exponents. This gives an adequate fit for both short and long duration stimuli, such as CI speech segments.

Cooperative population coding facilitates efficient sound-source separability by adaptation to input statistics

Our sensory environment changes constantly. Accordingly, neural systems continually adapt to the concurrent stimulus statistics to remain sensitive over a wide range of conditions. Such dynamic range adaptation (DRA) is assumed to increase both the effectiveness of the neuronal code and perceptual sensitivity. However, direct demonstrations of DRA-based efficient neuronal processing that also produces perceptual benefits are lacking. Here, we investigated the impact of DRA on spatial coding in the rodent brain and the perception of human listeners. Complex spatial stimulation with dynamically changing source locations elicited prominent DRA already on the initial spatial processing stage, the Lateral Superior Olive (LSO) of gerbils. Surprisingly, on the level of individual neurons, DRA diminished spatial tuning because of large response variability across trials. However, when considering single-trial population averages of multiple neurons, DRA enhanced the coding efficiency specifically for the concurrently most probable source locations. Intrinsic LSO population imaging of energy consumption combined with pharmacology revealed that a slow-acting LSO gain-control mechanism distributes activity across a group of neurons during DRA, thereby enhancing population coding efficiency. Strikingly, such “efficient cooperative coding” also improved neuronal source separability specifically for the locations that were most likely to occur. These location-specific enhancements in neuronal coding were paralleled by human listeners exhibiting a selective improvement in spatial resolution. We conclude that, contrary to canonical models of sensory encoding, the primary motive of early spatial processing is efficiency optimization of neural populations for enhanced source separability in the concurrent environment.

The efficient coding hypothesis suggests that sensory processing adapts to the stimulus statistics to maximize information while minimizing energetic costs. This study finds that an auditory spatial processing circuit distributes activity across neurons to enhance processing efficiency, focally improving spatial resolution both in neurons and in human listeners.

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.

Effect of neural adaptation and degeneration on pulse-train ECAPs: A model study

Electrically evoked compound action potentials (eCAPs) are measurements of the auditory nerve's response to electrical stimulation. ECAP amplitudes during pulse trains can exhibit temporal alternations. The magnitude of this alternation tends to diminish over time during the stimulus. How this pattern relates to the temporal behavior of nerve fibers is not known. We hypothesized that the stochasticity, refractoriness, adaptation of the threshold and spike-times influence pulse-train eCAP responses. Thirty thousand auditory nerve fibers were modeled in a three-dimensional cochlear model incorporating pulse-shape effects, pulse-history effects, and stochasticity in the individual neural responses. ECAPs in response to pulse trains of different rates and amplitudes were modeled for fibers with different stochastic properties (by variation of the relative spread) and different temporal properties (by variation of the refractory periods, adaptation and latency). The model predicts alternation of peak amplitudes similar to available human data. In addition, the peak alternation was affected by changing the refractoriness, adaptation, and relative spread of auditory nerve fibers. As these parameters are related to factors such as the duration of deafness and neural survival, this study suggests that the eCAP pattern in response to pulse trains could be used to assess the underlying temporal and stochastic behavior of the auditory nerve. As these properties affect the nerve's response to pulse trains, they are of uttermost importance to sound perception with cochlear implants.

Modeling and MEG evidence of early consonance processing in auditory cortex

Pitch is a fundamental attribute of auditory perception. The interaction of concurrent pitches gives rise to a sensation that can be characterized by its degree of consonance or dissonance. In this work, we propose that human auditory cortex (AC) processes pitch and consonance through a common neural network mechanism operating at early cortical levels. First, we developed a new model of neural ensembles incorporating realistic neuronal and synaptic parameters to assess pitch processing mechanisms at early stages of AC. Next, we designed a magnetoencephalography (MEG) experiment to measure the neuromagnetic activity evoked by dyads with varying degrees of consonance or dissonance. MEG results show that dissonant dyads evoke a pitch onset response (POR) with a latency up to 36 ms longer than consonant dyads. Additionally, we used the model to predict the processing time of concurrent pitches; here, consonant pitch combinations were decoded faster than dissonant combinations, in line with the experimental observations. Specifically, we found a striking match between the predicted and the observed latency of the POR as elicited by the dyads. These novel results suggest that consonance processing starts early in human auditory cortex and may share the network mechanisms that are responsible for (single) pitch processing.

Fractional integral-like processing in retinal cones reduces noise and improves adaptation

In the human retina, rod and cone cells detect incoming light with a molecule called rhodopsin. After rhodopsin molecules are activated (by photon impact), these molecules activate the rest of the signalling process for a brief period of time until they are deactivated by a multistage process. First, active rhodopsin is phosphorylated multiple times. Following this, they are further inhibited by the binding of molecules called arrestins. Finally, they decay into opsins. The time required for each of these stages becomes progressively longer, and each stage further reduces the activity of rhodopsin. However, while this deactivation process itself is well researched, the roles of the above stages in signal (and image) processing are poorly understood. In this paper, we will show that the activity of rhodopsin molecules during the deactivation process can be described as the fractional integration of an incoming signal. Furthermore, we show how this affects an image; specifically, the effect of fractional integration in video and signal processing and how it reduces noise and the improves adaptability under different lighting conditions. Our experimental results provide a better understanding of vertebrate and human vision, and why the rods and cones of the retina differ from the light detectors in cameras.

Neuronal adaptation to sound statistics in the inferior colliculus of behaving macaques does not reduce the effectiveness of the masking noise

The detectability of target sounds embedded within noisy backgrounds is affected by the regularities that summarize acoustic sceneries. Previous studies suggested that the dynamic range of neurons in the inferior colliculus (IC) of anesthetized guinea pigs shifts toward the mean sound pressure level in irregular acoustic environments. Yet, it is unclear how this neuronal adaptation processes may influence the effectiveness of sounds as a masker, both behaviorally and in terms of neuronal encoding. To answer this question, we measured the neural response of IC neurons while macaque monkeys performed a Go/No-Go tone detection task. Macaques detected a 50-ms tone that was either simultaneously gated with a burst of noise or embedded within a continuous noise background, whose levels were randomly sampled (every 50 ms) from a probability distribution. The mean of the distribution matched the level of the gated burst of noise. Psychometric and IC neurometric thresholds to tones did not differ between the two masking conditions. However, the neuronal firing rate versus level function was significantly affected by the temporal characteristics of the noise masker. Simultaneously gated noise caused higher baseline responses and greater dynamic range compression compared with noise distribution. The slopes of psychometric and neurometric functions were significantly shallower for higher variance distributions, suggesting that neuronal sensitivity might change with the variability of the sound. Our results suggest that the adaptive response of IC neurons to sound regularities does not affect the effectiveness of the noise-masking signal, which remains invariant to surrounding noise amplitudes. NEW & NOTEWORTHY Auditory neurons adapt to the statistics of sound levels in the acoustic scene. However, it is still unclear to what extent such adaptation influences the effectiveness of the stimulus as a masker. Our study represents the first attempt to investigate how the adaptation to the statistics of masking stimuli may be related to the effectiveness of masking, and to the single-unit encoding of the midbrain auditory neurons in behaving animals.

A Computational Model of Thalamocortical Dysrhythmia in People With Tinnitus

Tinnitus is a problem that affects a diverse range of people. One common trait amongst people with tinnitus is the presence of hearing loss, which is apparent in over 90% of the cohort. It is postulated that the remainder of people with tinnitus have hidden hearing loss in the form of cochlear synaptopathy. The loss of hearing sensation is thought to cause a reduction in the bottom-up excitatory signals of the auditory pathway leading to a change in the frequency of thalamocortical oscillations known as thalamocortical dysrhythmia (TCD). The downward shift in oscillatory behavior, characteristic of TCD, has been recorded experimentally but the underlying mechanisms responsible for TCD in tinnitus subjects cannot be directly observed. This paper investigates these underlying mechanisms by creating a biologically faithful model of the auditory periphery and thalamocortical network, called the central auditory processing (CAP) model. The proposed model replicates tinnitus related activity in the presence of hearing loss and hidden hearing loss in the form of cochlear synaptopathy. The results of this paper show that, both the bottom-up and top-down changes are required in the auditory system for tinnitus related hyperactivity to coexist with TCD, contrary to the theoretical model for TCD. The CAP model provides a novel modeling approach to account for tinnitus related activity with and without hearing loss. Moreover, the results provide additional clarity to the understanding of TCD and tinnitus and provide direction for future approaches to treating tinnitus.

A framework for computational modelling of interaural time difference discrimination of normal and hearing-impaired listeners

Different computational models have been developed to study the interaural time difference (ITD) perception. However, only few have used a physiologically inspired architecture to study ITD discrimination. Furthermore, they do not include aspects of hearing impairment. In this work, a framework was developed to predict ITD thresholds in listeners with normal and impaired hearing. It combines the physiologically inspired model of the auditory periphery proposed by Zilany, Bruce, Nelson, and Carney [(2009). J. Acoust. Soc. Am. 126(5), 2390-2412] as a front end with a coincidence detection stage and a neurometric decision device as a back end. It was validated by comparing its predictions against behavioral data for narrowband stimuli from literature. The framework is able to model ITD discrimination of normal-hearing and hearing-impaired listeners at a group level. Additionally, it was used to explore the effect of different proportions of outer- and inner-hair cell impairment on ITD discrimination.

Supra-Threshold Hearing and Fluctuation Profiles: Implications for Sensorineural and Hidden Hearing Loss

An important topic in contemporary auditory science is supra-threshold hearing. Difficulty hearing at conversational speech levels in background noise has long been recognized as a problem of sensorineural hearing loss, including that associated with aging (presbyacusis). Such difficulty in listeners with normal thresholds has received more attention recently, especially associated with descriptions of synaptopathy, the loss of auditory nerve (AN) fibers as a result of noise exposure or aging. Synaptopathy has been reported to cause a disproportionate loss of low- and medium-spontaneous rate (L/MSR) AN fibers. Several studies of synaptopathy have assumed that the wide dynamic ranges of L/MSR AN fiber rates are critical for coding supra-threshold sounds. First, this review will present data from the literature that argues against a direct role for average discharge rates of L/MSR AN fibers in coding sounds at moderate to high sound levels. Second, the encoding of sounds at supra-threshold levels is examined. A key assumption in many studies is that saturation of AN fiber discharge rates limits neural encoding, even though the majority of AN fibers, high-spontaneous rate (HSR) fibers, have saturated average rates at conversational sound levels. It is argued here that the cross-frequency profile of low-frequency neural fluctuation amplitudes, not average rates, encodes complex sounds. As described below, this fluctuation-profile coding mechanism benefits from both saturation of inner hair cell (IHC) transduction and average rate saturation associated with the IHC-AN synapse. Third, the role of the auditory efferent system, which receives inputs from L/MSR fibers, is revisited in the context of fluctuation-profile coding. The auditory efferent system is hypothesized to maintain and enhance neural fluctuation profiles. Lastly, central mechanisms sensitive to neural fluctuations are reviewed. Low-frequency fluctuations in AN responses are accentuated by cochlear nucleus neurons which, either directly or via other brainstem nuclei, relay fluctuation profiles to the inferior colliculus (IC). IC neurons are sensitive to the frequency and amplitude of low-frequency fluctuations and convert fluctuation profiles from the periphery into a phase-locked rate profile that is robust across a wide range of sound levels and in background noise. The descending projection from the midbrain (IC) to the efferent system completes a functional loop that, combined with inputs from the L/MSR pathway, is hypothesized to maintain "sharp" supra-threshold hearing, reminiscent of visual mechanisms that regulate optical accommodation. Examples from speech coding and detection in noise are reviewed. Implications for the effects of synaptopathy on control mechanisms hypothesized to influence supra-threshold hearing are discussed. This framework for understanding neural coding and control mechanisms for supra-threshold hearing suggests strategies for the design of novel hearing aid signal-processing and electrical stimulation patterns for cochlear implants.

Perceptual and Model-Based Evaluation of Ideal Time-Frequency Noise Reduction in Hearing-Impaired Listeners

State-of-the-art hearing aids (HAs) try to overcome the deficit of poor speech intelligibility (SI) in noisy listening environments using digital noise reduction (NR) techniques. The application of time-frequency masks to the noisy sound input is a common NR technique to increase SI. The binary mask with its binary weights and the Wiener filter with continuous weights are representatives of a hard- and a soft-decision approach for time-frequency masking. In normal-hearing listeners, the ideal Wiener filter (IWF) outperforms the ideal binary mask (IBM) in terms of SI and speech quality with perfect SI even at very low signal-to-noise ratios. In this paper, both approaches were investigated for hearing-impaired (HI) listeners. Perceptual and auditory model-based measures were used for the evaluation. The IWF outperformed the IBM in terms of SI. Quality-wise, there was no overall difference between the NR algorithms perceived. Additionally, the processed signals were evaluated based on an auditory nerve model using the neurogram similarity metric (NSIM). The mean NSIM values were significantly different for intelligible and unintelligible sentences. The results suggest that a soft-mask seems to be promising for application in HAs.

Incorporating behavioral and sensory context into spectro-temporal models of auditory encoding

For several decades, auditory neuroscientists have used spectro-temporal encoding models to understand how neurons in the auditory system represent sound. Derived from early applications of systems identification tools to the auditory periphery, the spectro-temporal receptive field (STRF) and more sophisticated variants have emerged as an efficient means of characterizing representation throughout the auditory system. Most of these encoding models describe neurons as static sensory filters. However, auditory neural coding is not static. Sensory context, reflecting the acoustic environment, and behavioral context, reflecting the internal state of the listener, can both influence sound-evoked activity, particularly in central auditory areas. This review explores recent efforts to integrate context into spectro-temporal encoding models. It begins with a brief tutorial on the basics of estimating and interpreting STRFs. Then it describes three recent studies that have characterized contextual effects on STRFs, emerging over a range of timescales, from many minutes to tens of milliseconds. An important theme of this work is not simply that context influences auditory coding, but also that contextual effects span a large continuum of internal states. The added complexity of these context-dependent models introduces new experimental and theoretical challenges that must be addressed in order to be used effectively. Several new methodological advances promise to address these limitations and allow the development of more comprehensive context-dependent models in the future.

Predicting phoneme and word recognition in noise using a computational model of the auditory periphery

Several filterbank-based metrics have been proposed to predict speech intelligibility (SI). However, these metrics incorporate little knowledge of the auditory periphery. Neurogram-based metrics provide an alternative, incorporating knowledge of the physiology of hearing by using a mathematical model of the auditory nerve response. In this work, SI was assessed utilizing different filterbank-based metrics (the speech intelligibility index and the speech-based envelope power spectrum model) and neurogram-based metrics, using the biologically inspired model of the auditory nerve proposed by Zilany, Bruce, Nelson, and Carney [(2009), J. Acoust. Soc. Am. 126(5), 2390-2412] as a front-end and the neurogram similarity metric and spectro temporal modulation index as a back-end. Then, the correlations with behavioural scores were computed. Results showed that neurogram-based metrics representing the speech envelope showed higher correlations with the behavioural scores at a word level. At a per-phoneme level, it was found that phoneme transitions contribute to higher correlations between objective measures that use speech envelope information at the auditory periphery level and behavioural data. The presented framework could function as a useful tool for the validation and tuning of speech materials, as well as a benchmark for the development of speech processing algorithms.

Neural Spike-Train Analyses of the Speech-Based Envelope Power Spectrum Model

Diagnosing and treating hearing impairment is challenging because people with similar degrees of sensorineural hearing loss (SNHL) often have different speech-recognition abilities. The speech-based envelope power spectrum model (sEPSM) has demonstrated that the signal-to-noise ratio (SNR_ENV) from a modulation filter bank provides a robust speech-intelligibility measure across a wider range of degraded conditions than many long-standing models. In the sEPSM, noise (N) is assumed to: (a) reduce S + N envelope power by filling in dips within clean speech (S) and (b) introduce an envelope noise floor from intrinsic fluctuations in the noise itself. While the promise of SNR_ENV has been demonstrated for normal-hearing listeners, it has not been thoroughly extended to hearing-impaired listeners because of limited physiological knowledge of how SNHL affects speech-in-noise envelope coding relative to noise alone. Here, envelope coding to speech-in-noise stimuli was quantified from auditory-nerve model spike trains using shuffled correlograms, which were analyzed in the modulation-frequency domain to compute modulation-band estimates of neural SNR_ENV. Preliminary spike-train analyses show strong similarities to the sEPSM, demonstrating feasibility of neural SNR_ENV computations. Results suggest that individual differences can occur based on differential degrees of outer- and inner-hair-cell dysfunction in listeners currently diagnosed into the single audiological SNHL category. The predicted acoustic-SNR dependence in individual differences suggests that the SNR-dependent rate of susceptibility could be an important metric in diagnosing individual differences. Future measurements of the neural SNR_ENV in animal studies with various forms of SNHL will provide valuable insight for understanding individual differences in speech-in-noise intelligibility.

Speech Coding in the Brain: Representation of Vowel Formants by Midbrain Neurons Tuned to Sound Fluctuations

Current models for neural coding of vowels are typically based on linear descriptions of the auditory periphery, and fail at high sound levels and in background noise. These models rely on either auditory nerve discharge rates or phase locking to temporal fine structure. However, both discharge rates and phase locking saturate at moderate to high sound levels, and phase locking is degraded in the CNS at middle to high frequencies. The fact that speech intelligibility is robust over a wide range of sound levels is problematic for codes that deteriorate as the sound level increases. Additionally, a successful neural code must function for speech in background noise at levels that are tolerated by listeners. The model presented here resolves these problems, and incorporates several key response properties of the nonlinear auditory periphery, including saturation, synchrony capture, and phase locking to both fine structure and envelope temporal features. The model also includes the properties of the auditory midbrain, where discharge rates are tuned to amplitude fluctuation rates. The nonlinear peripheral response features create contrasts in the amplitudes of low-frequency neural rate fluctuations across the population. These patterns of fluctuations result in a response profile in the midbrain that encodes vowel formants over a wide range of levels and in background noise. The hypothesized code is supported by electrophysiological recordings from the inferior colliculus of awake rabbits. This model provides information for understanding the structure of cross-linguistic vowel spaces, and suggests strategies for automatic formant detection and speech enhancement for listeners with hearing loss.

Abnormal Auditory Gain in Hyperacusis: Investigation with a Computational Model

Hyperacusis is a frequent auditory disorder that is characterized by abnormal loudness perception where sounds of relatively normal volume are perceived as too loud or even painfully loud. As hyperacusis patients show decreased loudness discomfort levels (LDLs) and steeper loudness growth functions, it has been hypothesized that hyperacusis might be caused by an increase in neuronal response gain in the auditory system. Moreover, since about 85% of hyperacusis patients also experience tinnitus, the conditions might be caused by a common mechanism. However, the mechanisms that give rise to hyperacusis have remained unclear. Here, we have used a computational model of the auditory system to investigate candidate mechanisms for hyperacusis. Assuming that perceived loudness is proportional to the summed activity of all auditory nerve (AN) fibers, the model was tuned to reproduce normal loudness perception. We then evaluated a variety of potential hyperacusis gain mechanisms by determining their effects on model equal-loudness contours and comparing the results to the LDLs of hyperacusis patients with normal hearing thresholds. Hyperacusis was best accounted for by an increase in non-linear gain in the central auditory system. Good fits to the average patient LDLs were obtained for a general increase in gain that affected all frequency channels to the same degree, and also for a frequency-specific gain increase in the high-frequency range. Moreover, the gain needed to be applied after subtraction of spontaneous activity of the AN, which is in contrast to current theories of tinnitus generation based on amplification of spontaneous activity. Hyperacusis and tinnitus might therefore be caused by different changes in neuronal processing in the central auditory system.

Modeling auditory coding: from sound to spikes

Models are valuable tools to assess how deeply we understand complex systems: only if we are able to replicate the output of a system based on the function of its subcomponents can we assume that we have probably grasped its principles of operation. On the other hand, discrepancies between model results and measurements reveal gaps in our current knowledge, which can in turn be targeted by matched experiments. Models of the auditory periphery have improved greatly during the last decades, and account for many phenomena observed in experiments. While the cochlea is only partly accessible in experiments, models can extrapolate its behavior without gap from base to apex and with arbitrary input signals. With models we can for example evaluate speech coding with large speech databases, which is not possible experimentally, and models have been tuned to replicate features of the human hearing organ, for which practically no invasive electrophysiological measurements are available. Auditory models have become instrumental in evaluating models of neuronal sound processing in the auditory brainstem and even at higher levels, where they are used to provide realistic input, and finally, models can be used to illustrate how such a complicated system as the inner ear works by visualizing its responses. The big advantage there is that intermediate steps in various domains (mechanical, electrical, and chemical) are available, such that a consistent picture of the evolvement of its output can be drawn. However, it must be kept in mind that no model is able to replicate all physiological characteristics (yet) and therefore it is critical to choose the most appropriate model—or models—for every research question. To facilitate this task, this paper not only reviews three recent auditory models, it also introduces a framework that allows researchers to easily switch between models. It also provides uniform evaluation and visualization scripts, which allow for direct comparisons between models.

Modeling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics

We propose several modifications to an existing computational model of stochastic vesicle release in inner hair cell ribbon synapses, with the aim of producing simulated auditory nerve fiber spiking data that more closely matches empirical data. Specifically, we studied the inter-spike-interval (ISI) distribution, and long and short term ISI correlations in spontaneous spiking in post-synaptic auditory nerve fibers. We introduced short term plasticity to the pre-synaptic release probability, in a manner analogous to standard stochastic models of cortical short term synaptic depression. This modification resulted in a similar distribution of vesicle release intervals to that estimated from empirical data. We also introduced a biophysical stochastic model of calcium channel opening and closing, but showed that this model is insufficient for generating a match with empirically observed spike correlations. However, by combining a phenomenological model of channel noise and our short term depression model, we generated short and long term correlations in auditory nerve spontaneous activity that qualitatively match empirical data.

Tone-in-noise detection using envelope cues: comparison of signal-processing-based and physiological models

Tone-in-noise detection tasks with reproducible noise maskers have been used to identify cues that listeners use to detect signals in noisy environments. Previous studies have shown that energy, envelope, and fine-structure cues are significantly correlated to listeners' performance for detection of a 500-Hz tone in noise. In this study, envelope cues were examined for both diotic and dichotic tone-in-noise detection using both stimulus-based signal processing and physiological models. For stimulus-based envelope cues, a modified envelope slope model was used for the diotic condition and the binaural slope of the interaural envelope difference model for the dichotic condition. Stimulus-based models do not include key nonlinear transformations in the auditory periphery such as compression, rate and dynamic range adaptation, and rate saturation, all of which affect the encoding of the stimulus envelope. For physiological envelope cues, stimuli were passed through models for the auditory nerve (AN), cochlear nucleus, and inferior colliculus (IC). The AN and cochlear nucleus models included appropriate modulation gain, another transformation of the stimulus envelope that is not typically included in stimulus-based models. A model IC cell was simulated with a linear band-pass modulation filter. The average discharge rate and response fluctuations of the model IC cell were compared to human performance. Previous studies have predicted a significant amount of the variance across reproducible noise maskers in listeners' detection using stimulus-based envelope cues. In this study, a physiological model that includes neural mechanisms that affect encoding of the stimulus envelope predicts a similar amount of the variance in listeners' performance across noise maskers.

Spectrotemporal resolution tradeoff in auditory processing as revealed by human auditory brainstem responses and psychophysical indices

Auditory filter theory dictates a physiological compromise between frequency and temporal resolution of cochlear signal processing. We examined neurophysiological correlates of these spectrotemporal tradeoffs in the human auditory system using auditory evoked brain potentials and psychophysical responses. Temporal resolution was assessed using scalp-recorded auditory brainstem responses (ABRs) elicited by paired clicks. The inter-click interval (ICI) between successive pulses was parameterized from 0.7 to 25 ms to map ABR amplitude recovery as a function of stimulus spacing. Behavioral frequency difference limens (FDLs) and auditory filter selectivity (Q10 of psychophysical tuning curves) were obtained to assess relations between behavioral spectral acuity and electrophysiological estimates of temporal resolvability. Neural responses increased monotonically in amplitude with increasing ICI, ranging from total suppression (0.7 ms) to full recovery (25 ms) with a temporal resolution of ∼3-4 ms. ABR temporal thresholds were correlated with behavioral Q10 (frequency selectivity) but not FDLs (frequency discrimination); no correspondence was observed between Q10 and FDLs. Results suggest that finer frequency selectivity, but not discrimination, is associated with poorer temporal resolution. The inverse relation between ABR recovery and perceptual frequency tuning demonstrates a time-frequency tradeoff between the temporal and spectral resolving power of the human auditory system.

Independent population coding of speech with sub-millisecond precision

To understand the strategies used by the brain to analyze complex environments, we must first characterize how the features of sensory stimuli are encoded in the spiking of neuronal populations. Characterizing a population code requires identifying the temporal precision of spiking and the extent to which spiking is correlated, both between cells and over time. In this study, we characterize the population code for speech in the gerbil inferior colliculus (IC), the hub of the auditory system where inputs from parallel brainstem pathways are integrated for transmission to the cortex. We find that IC spike trains can carry information about speech with sub-millisecond precision, and, consequently, that the temporal correlations imposed by refractoriness can play a significant role in shaping spike patterns. We also find that, in contrast to most other brain areas, the noise correlations between IC cells are extremely weak, indicating that spiking in the population is conditionally independent. These results demonstrate that the problem of understanding the population coding of speech can be reduced to the problem of understanding the stimulus-driven spiking of individual cells, suggesting that a comprehensive model of the subcortical processing of speech may be attainable in the near future.

Effects of tonotopicity, adaptation, modulation tuning, and temporal coherence in "primitive" auditory stream segregation

The perceptual organization of two-tone sequences into auditory streams was investigated using a modeling framework consisting of an auditory pre-processing front end [Dau et al., J. Acoust. Soc. Am. 102, 2892-2905 (1997)] combined with a temporal coherence-analysis back end [Elhilali et al., Neuron 61, 317-329 (2009)]. Two experimental paradigms were considered: (i) Stream segregation as a function of tone repetition time (TRT) and frequency separation (Δf) and (ii) grouping of distant spectral components based on onset/offset synchrony. The simulated and experimental results of the present study supported the hypothesis that forward masking enhances the ability to perceptually segregate spectrally close tone sequences. Furthermore, the modeling suggested that effects of neural adaptation and processing though modulation-frequency selective filters may enhance the sensitivity to onset asynchrony of spectral components, facilitating the listeners' ability to segregate temporally overlapping sounds into separate auditory objects. Overall, the modeling framework may be useful to study the contributions of bottom-up auditory features on "primitive" grouping, also in more complex acoustic scenarios than those considered here.

Updated parameters and expanded simulation options for a model of the auditory periphery

A phenomenological model of the auditory periphery in cats was previously developed by Zilany and colleagues [J. Acoust. Soc. Am. 126, 2390-2412 (2009)] to examine the detailed transformation of acoustic signals into the auditory-nerve representation. In this paper, a few issues arising from the responses of the previous version have been addressed. The parameters of the synapse model have been readjusted to better simulate reported physiological discharge rates at saturation for higher characteristic frequencies [Liberman, J. Acoust. Soc. Am. 63, 442-455 (1978)]. This modification also corrects the responses of higher-characteristic frequency (CF) model fibers to low-frequency tones that were erroneously much higher than the responses of low-CF model fibers in the previous version. In addition, an analytical method has been implemented to compute the mean discharge rate and variance from the model's synapse output that takes into account the effects of absolute refractoriness.

Constructing noise-invariant representations of sound in the auditory pathway

Along the auditory pathway from auditory nerve to midbrain to cortex, individual neurons adapt progressively to sound statistics, enabling the discernment of foreground sounds, such as speech, over background noise.

Identifying behaviorally relevant sounds in the presence of background noise is one of the most important and poorly understood challenges faced by the auditory system. An elegant solution to this problem would be for the auditory system to represent sounds in a noise-invariant fashion. Since a major effect of background noise is to alter the statistics of the sounds reaching the ear, noise-invariant representations could be promoted by neurons adapting to stimulus statistics. Here we investigated the extent of neuronal adaptation to the mean and contrast of auditory stimulation as one ascends the auditory pathway. We measured these forms of adaptation by presenting complex synthetic and natural sounds, recording neuronal responses in the inferior colliculus and primary fields of the auditory cortex of anaesthetized ferrets, and comparing these responses with a sophisticated model of the auditory nerve. We find that the strength of both forms of adaptation increases as one ascends the auditory pathway. To investigate whether this adaptation to stimulus statistics contributes to the construction of noise-invariant sound representations, we also presented complex, natural sounds embedded in stationary noise, and used a decoding approach to assess the noise tolerance of the neuronal population code. We find that the code for complex sounds in the periphery is affected more by the addition of noise than the cortical code. We also find that noise tolerance is correlated with adaptation to stimulus statistics, so that populations that show the strongest adaptation to stimulus statistics are also the most noise-tolerant. This suggests that the increase in adaptation to sound statistics from auditory nerve to midbrain to cortex is an important stage in the construction of noise-invariant sound representations in the higher auditory brain.

We rarely hear sounds (such as someone talking) in isolation, but rather against a background of noise. When mixtures of sounds and background noise reach the ears, peripheral auditory neurons represent the whole sound mixture. Previous evidence suggests, however, that the higher auditory brain represents just the sounds of interest, and is less affected by the presence of background noise. The neural mechanisms underlying this transformation are poorly understood. Here, we investigate these mechanisms by studying the representation of sound by populations of neurons at three stages along the auditory pathway; we simulate the auditory nerve and record from neurons in the midbrain and primary auditory cortex of anesthetized ferrets. We find that the transformation from noise-sensitive representations of sound to noise-tolerant processing takes place gradually along the pathway from auditory nerve to midbrain to cortex. Our results suggest that this results from neurons adapting to the statistics of heard sounds.

Speed-invariant encoding of looming object distance requires power law spike rate adaptation

Neural representations of a moving object's distance and approach speed are essential for determining appropriate orienting responses, such as those observed in the localization behaviors of the weakly electric fish, Apteronotus leptorhynchus. We demonstrate that a power law form of spike rate adaptation transforms an electroreceptor afferent's response to "looming" object motion, effectively parsing information about distance and approach speed into distinct measures of the firing rate. Neurons with dynamics characterized by fixed time scales are shown to confound estimates of object distance and speed. Conversely, power law adaptation modifies an electroreceptor afferent's response according to the time scales present in the stimulus, generating a rate code for looming object distance that is invariant to speed and acceleration. Consequently, estimates of both object distance and approach speed can be uniquely determined from an electroreceptor afferent's firing rate, a multiplexed neural code operating over the extended time scales associated with behaviorally relevant stimuli.

Modeling the anti-masking effects of the olivocochlear reflex in auditory nerve responses to tones in sustained noise

The medial olivocochlear reflex (MOCR) has been hypothesized to provide benefit for listening in noise. Strong physiological support for an anti-masking role for the MOCR has come from the observation that auditory nerve (AN) fibers exhibit reduced firing to sustained noise and increased sensitivity to tones when the MOCR is elicited. The present study extended a well-established computational model for normal-hearing and hearing-impaired AN responses to demonstrate that these anti-masking effects can be accounted for by reducing outer hair cell (OHC) gain, which is a primary effect of the MOCR. Tone responses in noise were examined systematically as a function of tone level, noise level, and OHC gain. Signal detection theory was used to predict detection and discrimination for different spontaneous rate fiber groups. Decreasing OHC gain decreased the sustained noise response and increased maximum discharge rate to the tone, thus modeling the ability of the MOCR to decompress AN fiber rate-level functions. Comparing the present modeling results with previous data from AN fibers in decerebrate cats suggests that the ipsilateral masking noise used in the physiological study may have elicited up to 20 dB of OHC gain reduction in addition to that inferred from the contralateral noise effects. Reducing OHC gain in the model also extended the dynamic range for discrimination over a wide range of background noise levels. For each masker level, an optimal OHC gain reduction was predicted (i.e., where maximum discrimination was achieved without increased detection threshold). These optimal gain reductions increased with masker level and were physiologically realistic. Thus, reducing OHC gain can improve tone-in-noise discrimination even though it may produce a “hearing loss” in quiet. Combining MOCR effects with the sensorineural hearing loss effects already captured by this computational AN model will be beneficial for exploring the implications of their interaction for the difficulties hearing-impaired listeners have in noisy situations.

Adaptation in the auditory midbrain of the barn owl (Tyto alba) induced by tonal double stimulation

During hunting, the barn owl typically listens to several successive sounds as generated, for example, by rustling mice. As auditory cells exhibit adaptive coding, the earlier stimuli may influence the detection of the later stimuli. This situation was mimicked with two double-stimulus paradigms, and adaptation was investigated in neurons of the barn owl's central nucleus of the inferior colliculus. Each double-stimulus paradigm consisted of a first or reference stimulus and a second stimulus (probe). In one paradigm (second level tuning), the probe level was varied, whereas in the other paradigm (inter-stimulus interval tuning), the stimulus interval between the first and second stimulus was changed systematically. Neurons were stimulated with monaural pure tones at the best frequency, while the response was recorded extracellularly. The responses to the probe were significantly reduced when the reference stimulus and probe had the same level and the inter-stimulus interval was short. This indicated response adaptation, which could be compensated for by an increase of the probe level of 5-7 dB over the reference level, if the latter was in the lower half of the dynamic range of a neuron's rate-level function. Recovery from adaptation could be best fitted with a double exponential showing a fast (1.25 ms) and a slow (800 ms) component. These results suggest that neurons in the auditory system show dynamic coding properties to tonal double stimulation that might be relevant for faithful upstream signal propagation. Furthermore, the overall stimulus level of the masker also seems to affect the recovery capabilities of auditory neurons.

An active loudness model suggesting tinnitus as increased central noise and hyperacusis as increased nonlinear gain

The present study uses a systems engineering approach to delineate the relationship between tinnitus and hyperacusis as a result of either hearing loss in the ear or an imbalanced state in the brain. Specifically examined is the input-output function, or loudness growth as a function of intensity in both normal and pathological conditions. Tinnitus reduces the output dynamic range by raising the floor, while hyperacusis reduces the input dynamic range by lowering the ceiling or sound tolerance level. Tinnitus does not necessarily steepen the loudness growth function but hyperacusis always does. An active loudness model that consists of an expansion stage following a compression stage can account for these key properties in tinnitus and hyperacusis loudness functions. The active loudness model suggests that tinnitus is a result of increased central noise, while hyperacusis is due to increased nonlinear gain. The active loudness model also generates specific predictions on loudness growth in tinnitus, hyperacusis, hearing loss or any combinations of the three conditions. These predictions need to be verified by experimental data and have explicit implications for treatment of tinnitus and hyperacusis.

Time course of dynamic range adaptation in the auditory nerve

Auditory adaptation to sound-level statistics occurs as early as in the auditory nerve (AN), the first stage of neural auditory processing. In addition to firing rate adaptation characterized by a rate decrement dependent on previous spike activity, AN fibers show dynamic range adaptation, which is characterized by a shift of the rate-level function or dynamic range toward the most frequently occurring levels in a dynamic stimulus, thereby improving the precision of coding of the most common sound levels (Wen B, Wang GI, Dean I, Delgutte B. J Neurosci 29: 13797-13808, 2009). We investigated the time course of dynamic range adaptation by recording from AN fibers with a stimulus in which the sound levels periodically switch from one nonuniform level distribution to another (Dean I, Robinson BL, Harper NS, McAlpine D. J Neurosci 28: 6430-6438, 2008). Dynamic range adaptation occurred rapidly, but its exact time course was difficult to determine directly from the data because of the concomitant firing rate adaptation. To characterize the time course of dynamic range adaptation without the confound of firing rate adaptation, we developed a phenomenological "dual adaptation" model that accounts for both forms of AN adaptation. When fitted to the data, the model predicts that dynamic range adaptation occurs as rapidly as firing rate adaptation, over 100-400 ms, and the time constants of the two forms of adaptation are correlated. These findings suggest that adaptive processing in the auditory periphery in response to changes in mean sound level occurs rapidly enough to have significant impact on the coding of natural sounds.

Investigating temporal asymmetry using masking period patterns and models of peripheral auditory processing

Two experiments were conducted in conjunction with modeling to evaluate the role of peripheral nonlinearity and neural adaptation in the perception of temporally asymmetric sounds. In both experiments, maskers were broadband noises amplitude modulated with ramped and damped exponential modulators that repeated at 40 Hz. Masking period patterns (MPPs) were constructed by measuring detection threshold of a 5-ms, 1000-Hz tone burst as function of the signal's onset delay. Experiment I showed that varying modulator half-life from 1 to 16 ms led to differences in the damped and the ramped MPPs that were largest at the short half-lives and diminished at the longer half-lives. When masker level was varied (experiment II), the largest difference between ramped and damped MPPs occurred at moderate stimulus levels. Two peripheral auditory models were evaluated, one a simple auditory filter followed by a power-law nonlinearity and another, a model of auditory nerve processing [J. Acoust. Soc. Am. 126, 2390-2412 (2009)] that includes neural adaptation. Neither models predicted differences between the ramped and damped MPPs, providing indirect support that the central auditory system has a role in perceptual temporal asymmetry.

Evaluating adaptation and olivocochlear efferent feedback as potential explanations of psychophysical overshoot

Masked detection threshold for a short tone in noise improves as the tone's onset is delayed from the masker's onset. This improvement, known as "overshoot," is maximal at mid-masker levels and is reduced by temporary and permanent cochlear hearing loss. Computational modeling was used in the present study to evaluate proposed physiological mechanisms of overshoot, including classic firing rate adaptation and medial olivocochlear (MOC) feedback, for both normal hearing and cochlear hearing loss conditions. These theories were tested using an established model of the auditory periphery and signal detection theory techniques. The influence of several analysis variables on predicted tone-pip detection in broadband noise was evaluated, including: auditory nerve fiber spontaneous-rate (SR) pooling, range of characteristic frequencies, number of synapses per characteristic frequency, analysis window duration, and detection rule. The results revealed that overshoot similar to perceptual data in terms of both magnitude and level dependence could be predicted when the effects of MOC efferent feedback were included in the auditory nerve model. Conversely, simulations without MOC feedback effects never produced overshoot despite the model's ability to account for classic firing rate adaptation and dynamic range adaptation in auditory nerve responses. Cochlear hearing loss was predicted to reduce the size of overshoot only for model versions that included the effects of MOC efferent feedback. These findings suggest that overshoot in normal and hearing-impaired listeners is mediated by some form of dynamic range adaptation other than what is observed in the auditory nerve of anesthetized animals. Mechanisms for this adaptation may occur at several levels along the auditory pathway. Among these mechanisms, the MOC reflex may play a leading role.

Songbirds tradeoff auditory frequency resolution and temporal resolution

Physical tradeoffs may in some cases constrain the evolution of sensory systems. The peripheral auditory system, for example, performs a spectral decomposition of sound that should result in a tradeoff between frequency resolution and temporal resolution. We assessed temporal resolution in three songbird species using auditory brainstem responses to paired click stimuli. Temporal resolution was greater in house sparrows (Passer domesticus) than Carolina chickadees (Poecile carolinensis) and white-breasted nuthatches (Sitta carolinensis), as predicted based on previous observations of broader auditory filters (lower frequency resolution) in house sparrows. Furthermore, within chickadees, individuals with broader auditory filters had greater temporal resolution. In contrast to predictions however, temporal resolution was similar between chickadees and nuthatches despite broader auditory filters in chickadees. These results and the results of a model simulation exploring the effect of broadened auditory filter bandwidth on temporal resolution in the auditory periphery strongly suggest that frequency resolution constrains temporal resolution in songbirds. Furthermore, our results suggest that songbirds have greater temporal resolution than some mammals, in agreement with recent behavioral studies. Species differences in temporal resolution may reflect adaptations for efficient processing of species-specific vocalizations, while individual differences within species may reflect experience-based developmental plasticity or hormonal effects.