Evaluating auditory performance limits: II. One-parameter discrimination with random-level variation

A Computational Model of Auditory Chirp-Velocity Sensitivity and Amplitude-Modulation Tuning in Inferior Colliculus Neurons

We demonstrate a model of chirp-velocity sensitivity in the inferior colliculus (IC) that retains the tuning to amplitude modulation (AM) that was established in earlier models. The mechanism of velocity sensitivity is sequence detection by octopus cells of the posteroventral cochlear nucleus, which have been proposed in physiological studies to respond preferentially to the order of arrival of cross-frequency inputs of different amplitudes. Model architecture is based on coincidence detection of a combination of excitatory and inhibitory inputs. Chirp-sensitivity of the IC output is largely controlled by the strength and timing of the chirp-sensitive octopus-cell inhibitory input. AM tuning is controlled by inhibition and excitation that are tuned to the same frequency. We present several example neurons that demonstrate the feasibility of the model in simulating realistic chirp-sensitivity and AM tuning for a wide range of characteristic frequencies. Additionally, we explore the systematic impact of varying parameters on model responses. The proposed model can be used to assess the contribution of IC chirp-velocity sensitivity to responses to complex sounds, such as speech.

Exploiting individual differences to assess the role of place and phase locking cues in auditory frequency discrimination at 2 kHz

The relative role of place and temporal mechanisms in auditory frequency discrimination was assessed for a centre frequency of 2 kHz. Four measures of frequency discrimination were obtained for 63 normal-hearing participants: detection of frequency modulation using modulation rates of 2 Hz (FM2) and 20 Hz (FM20); detection of a change in frequency across successive pure tones (difference limen for frequency, DLF); and detection of changes in the temporal fine structure of bandpass filtered complex tones centred at 2 kHz (TFS). Previous work has suggested that: FM2 depends on the use of both temporal and place cues; FM20 depends primarily on the use of place cues because the temporal mechanism cannot track rapid changes in frequency; DLF depends primarily on temporal cues; TFS depends exclusively on temporal cues. This led to the following predicted patterns of the correlations of scores across participants: DLF and TFS should be highly correlated; FM2 should be correlated with DLF and TFS; FM20 should not be correlated with DLF or TFS. The results were broadly consistent with these predictions and with the idea that frequency discrimination at 2 kHz depends partly or primarily on temporal cues except for frequency modulation detection at a high rate.

Assessing mechanisms of frequency discrimination by comparison of different measures over a wide frequency range

It has been hypothesized that auditory detection of frequency modulation (FM) for low FM rates depends on the use of both temporal (phase locking) and place cues, depending on the carrier frequency, while detection of FM at high rates depends primarily on the use of place cues. To test this, FM detection for 2 and 20 Hz rates was measured over a wide frequency range, 1-10 kHz, including high frequencies for which temporal cues are assumed to be very weak. Performance was measured over the same frequency range for a task involving detection of changes in the temporal fine structure (TFS) of bandpass filtered complex tones, for which performance is assumed to depend primarily on the use of temporal cues. FM thresholds were better for the 2- than for the 20-Hz rate for center frequencies up to 4 kHz, while the reverse was true for higher center frequencies. For both FM rates, the thresholds, expressed as a proportion of the center frequency, were roughly constant for center frequencies from 6 to 10 Hz, consistent with the use of place cues. For the TFS task, thresholds worsened progressively with increasing frequency above 4 kHz, consistent with the weakening of temporal cues.

Time Is of the Essence: Neural Codes, Synchronies, Oscillations, Architectures

Time is of the essence in how neural codes, synchronies, and oscillations might function in encoding, representation, transmission, integration, storage, and retrieval of information in brains. This Hypothesis and Theory article examines observed and possible relations between codes, synchronies, oscillations, and types of neural networks they require. Toward reverse-engineering informational functions in brains, prospective, alternative neural architectures incorporating principles from radio modulation and demodulation, active reverberant circuits, distributed content-addressable memory, signal-signal time-domain correlation and convolution operations, spike-correlation-based holography, and self-organizing, autoencoding anticipatory systems are outlined. Synchronies and oscillations are thought to subserve many possible functions: sensation, perception, action, cognition, motivation, affect, memory, attention, anticipation, and imagination. These include direct involvement in coding attributes of events and objects through phase-locking as well as characteristic patterns of spike latency and oscillatory response. They are thought to be involved in segmentation and binding, working memory, attention, gating and routing of signals, temporal reset mechanisms, inter-regional coordination, time discretization, time-warping transformations, and support for temporal wave-interference based operations. A high level, partial taxonomy of neural codes consists of channel, temporal pattern, and spike latency codes. The functional roles of synchronies and oscillations in candidate neural codes, including oscillatory phase-offset codes, are outlined. Various forms of multiplexing neural signals are considered: time-division, frequency-division, code-division, oscillatory-phase, synchronized channels, oscillatory hierarchies, polychronous ensembles. An expandable, annotative neural spike train framework for encoding low- and high-level attributes of events and objects is proposed. Coding schemes require appropriate neural architectures for their interpretation. Time-delay, oscillatory, wave-interference, synfire chain, polychronous, and neural timing networks are discussed. Some novel concepts for formulating an alternative, more time-centric theory of brain function are discussed. As in radio communication systems, brains can be regarded as networks of dynamic, adaptive transceivers that broadcast and selectively receive multiplexed temporally-patterned pulse signals. These signals enable complex signal interactions that select, reinforce, and bind common subpatterns and create emergent lower dimensional signals that propagate through spreading activation interference networks. If memory traces share the same kind of temporal pattern forms as do active neuronal representations, then distributed, holograph-like content-addressable memories are made possible via temporal pattern resonances.

Listening to Music Through Hearing Aids: Potential Lessons for Cochlear Implants

Some of the problems experienced by users of hearing aids (HAs) when listening to music are relevant to cochlear implants (CIs). One problem is related to the high peak levels (up to 120 dB SPL) that occur in live music. Some HAs and CIs overload at such levels, because of the limited dynamic range of the microphones and analogue-to-digital converters (ADCs), leading to perceived distortion. Potential solutions are to use 24-bit ADCs or to include an adjustable gain between the microphones and the ADCs. A related problem is how to squeeze the wide dynamic range of music into the limited dynamic range of the user, which can be only 6-20 dB for CI users. In HAs, this is usually done via multi-channel amplitude compression (automatic gain control, AGC). In CIs, a single-channel front-end AGC is applied to the broadband input signal or a control signal derived from a running average of the broadband signal level is used to control the mapping of the channel envelope magnitude to an electrical signal. This introduces several problems: (1) an intense narrowband signal (e.g. a strong bass sound) reduces the level for all frequency components, making some parts of the music harder to hear; (2) the AGC introduces cross-modulation effects that can make a steady sound (e.g. sustained strings or a sung note) appear to fluctuate in level. Potential solutions are to use several frequency channels to create slowly varying gain-control signals and to use slow-acting (or dual time-constant) AGC rather than fast-acting AGC.

Pitch perception at very high frequencies: On psychometric functions and integration of frequency information

Lau et al. [J. Neurosci. 37, 9013-9021 (2017)] showed that discrimination of the fundamental frequency (F0) of complex tones with components in a high-frequency region was better than predicted from the optimal combination of information from the individual harmonics. The predictions depend on the assumption that psychometric functions for frequency discrimination have a slope of 1 at high frequencies. This was tested by measuring psychometric functions for F0 discrimination and frequency discrimination. Difference limens for F0 (F0DLs) and difference limens for frequency for each frequency component were also measured. Complex tones contained harmonics 6-10 and had F0s of 280 or 1400 Hz. Thresholds were measured using 210-ms tones presented diotically in diotic threshold-equalizing noise (TEN), and 1000-ms tones presented diotically in dichotic TEN. The slopes of the psychometric functions were close to 1 for all frequencies and F0s. The ratio of predicted to observed F0DLs was around 1 or smaller for both F0s, i.e., not super-optimal, and was significantly smaller for the low than for the high F0. The results are consistent with the idea that place information alone can convey pitch, but pitch is more salient when phase-locking information is available.

Simulating electrical modulation detection thresholds using a biophysical model of the auditory nerve

Modulation detection thresholds (MDTs) assess listeners' sensitivity to changes in the temporal envelope of a signal and have been shown to strongly correlate with speech perception in cochlear implant users. MDTs are simulated with a stochastic model of a population of auditory nerve fibers that has been verified to accurately simulate a number of physiologically important temporal response properties. The procedure to estimate detection thresholds has previously been applied to stimulus discrimination tasks. The population model simulates the MDT-stimulus intensity relationship measured in cochlear implant users. The model also recreates the shape of the modulation transfer function and the relationship between MDTs and carrier rate. Discrimination based on fluctuations in synchronous firing activity predicts better performance at low carrier rates, but quantitative measures of modulation coding predict better neural representation of high carrier rate stimuli. Manipulating the number of fibers and a temporal integration parameter, the width of a sliding temporal integration window, varies properties of the MDTs, such as cutoff frequency and peak threshold. These results demonstrate the importance of using a multi-diameter fiber population in modeling the MDTs and demonstrate a wider applicability of this model to simulating behavioral performance in cochlear implant listeners.

Separating the contributions of primary and unwanted cues in psychophysical studies

A fundamental issue in the design and the interpretation of experimental studies of perception relates to the question of whether the participants in these experiments could perform the perceptual task assigned to them using another feature, or cue, than that intended by the experimenter. An approach frequently used by auditory- and visual-perception researchers to guard against this possibility involves applying random variations to the stimuli across presentations or trials so as to make the "unwanted" cue unreliable for the participants. However, the theoretical basis of this widespread practice is not well developed. In this article, we describe a 2-channel model based on general principles of psychophysical signal detection theory, which can be used to assess the respective contributions of the unwanted cue and of the primary cue to performance or thresholds measured in perceptual discrimination experiments involving stimulus randomization. Example applications of the model to the analysis of results obtained in representative studies from the auditory- and visual-perception literature are provided. In several cases, the results of the model-based analyses indicate that the effectiveness of the randomization procedure was less than originally assumed by the authors of these studies. These findings underscore the importance of quantifying the potential influence of unwanted cues on the results of psychophysical experiments, even when stimulus randomization is used.

Characterizing the dependence of pure-tone frequency difference limens on frequency, duration, and level

This study examined the relationship between the difference limen for frequency (DLF) of pure tones and three commonly explored stimulus parameters of frequency, duration, and sensation level. Data from 12 published studies of pure-tone frequency discrimination (a total of 583 DLF measurements across 77 normal-hearing listeners) were analyzed using hierarchical (or "mixed-effects") generalized linear models. Model parameters were estimated using two approaches (Bayesian and maximum likelihood). A model in which log-transformed DLFs were predicted using a sum of power-law functions plus a random subject- or group-specific term was found to explain a substantial proportion of the variability in the psychophysical data. The results confirmed earlier findings of an inverse-square-root relationship between log-transformed DLFs and duration, and of an inverse relationship between log(DLF) and sensation level. However, they did not confirm earlier suggestions that log(DLF) increases approximately linearly with the square-root of frequency; instead, the relationship between frequency and log(DLF) was best fitted using a power function of frequency with an exponent of about 0.8. These results, and the comprehensive quantitative analysis of pure-tone frequency discrimination on which they are based, provide a new reference for the quantitative evaluation of models of frequency (or pitch) discrimination.

A point process framework for modeling electrical stimulation of the auditory nerve

Model-based studies of responses of auditory nerve fibers to electrical stimulation can provide insight into the functioning of cochlear implants. Ideally, these studies can identify limitations in sound processing strategies and lead to improved methods for providing sound information to cochlear implant users. To accomplish this, models must accurately describe spiking activity while avoiding excessive complexity that would preclude large-scale simulations of populations of auditory nerve fibers and obscure insight into the mechanisms that influence neural encoding of sound information. In this spirit, we develop a point process model of individual auditory nerve fibers that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the proposed model consists of a cascade of linear and nonlinear stages. We show how each of these stages can be associated with biophysical mechanisms and related to models of neuronal dynamics. Moreover, we derive a semianalytical procedure that uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation. Throughout, we compare model predictions to published physiological data of response to high and low pulse rate stimulation. We find that the model, although constructed to fit data from single and paired pulse experiments, can accurately predict responses to unmodulated and modulated pulse train stimuli. We close by performing an ideal observer analysis of simulated spike trains in response to sinusoidally amplitude modulated stimuli and find that carrier pulse rate does not affect modulation detection thresholds.

On the interpretation of sensitivity analyses of neural responses

Responses of auditory neurons vary with many dimensions of acoustical stimuli. As a consequence, there is a difference between sensitivity to a particular dimension (e.g., ITD or level), which is assessed when only that dimension is varied while other dimensions are fixed (yielding tuning curves), and information about that dimension, which requires that all natural variability be considered. In particular, the rate of a neuron can be very sensitive to a dimension while poorly informative about it, if it is also sensitive to other dimensions. One implication is that in a multi-dimensional world, stimulus properties such as ITD are optimally coded with heterogeneous neural populations.

Encoding and decoding amplitude-modulated cochlear implant stimuli--a point process analysis

Cochlear implant speech processors stimulate the auditory nerve by delivering amplitude-modulated electrical pulse trains to intracochlear electrodes. Studying how auditory nerve cells encode modulation information is of fundamental importance, therefore, to understanding cochlear implant function and improving speech perception in cochlear implant users. In this paper, we analyze simulated responses of the auditory nerve to amplitude-modulated cochlear implant stimuli using a point process model. First, we quantify the information encoded in the spike trains by testing an ideal observer's ability to detect amplitude modulation in a two-alternative forced-choice task. We vary the amount of information available to the observer to probe how spike timing and averaged firing rate encode modulation. Second, we construct a neural decoding method that predicts several qualitative trends observed in psychophysical tests of amplitude modulation detection in cochlear implant listeners. We find that modulation information is primarily available in the sequence of spike times. The performance of an ideal observer, however, is inconsistent with observed trends in psychophysical data. Using a neural decoding method that jitters spike times to degrade its temporal resolution and then computes a common measure of phase locking from spike trains of a heterogeneous population of model nerve cells, we predict the correct qualitative dependence of modulation detection thresholds on modulation frequency and stimulus level. The decoder does not predict the observed loss of modulation sensitivity at high carrier pulse rates, but this framework can be applied to future models that better represent auditory nerve responses to high carrier pulse rate stimuli. The supplemental material of this article contains the article's data in an active, re-usable format.

Effect of noise on the detectability and fundamental frequency discrimination of complex tones

Percent correct performance for discrimination of the fundamental frequency (0) of a complex tone was measured as a function of the level of a background pink noise (using fixed values of the difference in F0, deltaF0) and compared with percent correct performance for detection of the complex tone in noise, again as a function of noise level. The tone included some low, resolvable components, but not the fundamental component. The results were used to test the hypothesis that the worsening in F0 discrimination with increasing noise level was caused by the reduced detectability of the tone rather than by reduced precision of the internal representation of F0. For small values of deltaF0, the hypothesis was rejected because measured performance fell below that predicted by the hypothesis. However, this was true only for high noise levels, within 2-4.5 dB of the level required for masked threshold. The results indicate that the mechanism for extracting the F0 of a complex tone with resolved harmonics is remarkably robust. They also indicate that adding a background noise to a complex tone containing resolved harmonics is not a good means for equating its pitch salience with that of a complex tone containing only unresolved harmonics.

Perceptual Consequences of Disrupted Auditory Nerve Activity

Perceptual consequences of disrupted auditory nerve activity were systematically studied in 21 subjects who had been clinically diagnosed with auditory neuropathy (AN), a recently defined disorder characterized by normal outer hair cell function but disrupted auditory nerve function. Neurological and electrophysical evidence suggests that disrupted auditory nerve activity is due to desynchronized or reduced neural activity or both. Psychophysical measures showed that the disrupted neural activity has minimal effects on intensity-related perception, such as loudness discrimination, pitch discrimination at high frequencies, and sound localization using interaural level differences. In contrast, the disrupted neural activity significantly impairs timing related perception, such as pitch discrimination at low frequencies, temporal integration, gap detection, temporal modulation detection, backward and forward masking, signal detection in noise, binaural beats, and sound localization using interaural time differences. These perceptual consequences are the opposite of what is typically observed in cochlear-impaired subjects who have impaired intensity perception but relatively normal temporal processing after taking their impaired intensity perception into account. These differences in perceptual consequences between auditory neuropathy and cochlear damage suggest the use of different neural codes in auditory perception: a suboptimal spike count code for intensity processing, a synchronized spike code for temporal processing, and a duplex code for frequency processing. We also proposed two underlying physiological models based on desynchronized and reduced discharge in the auditory nerve to successfully account for the observed neurological and behavioral data. These methods and measures cannot differentiate between these two AN models, but future studies using electric stimulation of the auditory nerve via a cochlear implant might. These results not only show the unique contribution of neural synchrony to sensory perception but also provide guidance for translational research in terms of better diagnosis and management of human communication disorders.

Predicting Auditory Tone-in-Noise Detection Performance: The Effects of Neural Variability

Collecting and analyzing psychophysical data is a fundamental mechanism for the study of auditory processing. However, because this approach relies on human listening experiments, it can be costly in terms of time and money spent gathering the data. The development of a theoretical, model-based procedure capable of accurately predicting psychophysical behavior could alleviate these issues by enabling researchers to rapidly evaluate hypotheses prior to conducting experiments. This approach may also provide additional insight into auditory processing by establishing a link between psychophysical behavior and physiology. Signal detection theory has previously been combined with an auditory model to generate theoretical predictions of psychophysical behavior. Commonly, the ideal processor outperforms human subjects. In order for this model-based technique to enhance the study of auditory processing, discrepancies must be eliminated or explained. In this paper, we investigate the possibility that neural variability, which results from the randomness inherent in auditory nerve fiber responses, may explain some of the previously observed discrepancies. In addition, we study the impact of combining information across nerve fibers and investigate several models of multiple-fiber signal processing. Our findings suggest that neural variability can account for much, but not all, of the discrepancy between theoretical and experimental data.

Rate and timing cues associated with the cochlear amplifier: level discrimination based on monaural cross-frequency coincidence detection

The perceptual significance of the cochlear amplifier was evaluated by predicting level-discrimination performance based on stochastic auditory-nerve (AN) activity. Performance was calculated for three models of processing: the optimal all-information processor (based on discharge times), the optimal rate-place processor (based on discharge counts), and a monaural coincidence-based processor that uses a non-optimal combination of rate and temporal information. An analytical AN model included compressive magnitude and level-dependent-phase responses associated with the cochlear amplifier, and high-, medium-, and low-spontaneous-rate (SR) fibers with characteristic frequencies (CFs) spanning the AN population. The relative contributions of nonlinear magnitude and nonlinear phase responses to level encoding were compared by using four versions of the model, which included and excluded the nonlinear gain and phase responses in all possible combinations. Nonlinear basilar-membrane (BM) phase responses are robustly encoded in near-CF AN fibers at low frequencies. Strongly compressive BM responses at high frequencies near CF interact with the high thresholds of low-SR AN fibers to produce large dynamic ranges. Coincidence performance based on a narrow range of AN CFs was robust across a wide dynamic range at both low and high frequencies, and matched human performance levels. Coincidence performance based on all CFs demonstrated the "near-miss" to Weber's law at low frequencies and the high-frequency "mid-level bump." Monaural coincidence detection is a physiologically realistic mechanism that is extremely general in that it can utilize AN information (average-rate, synchrony, and nonlinear-phase cues) from all SR groups.