Cat Auditory-nerve Fibers Research Articles

Input-output curves of low and high spontaneous rate auditory nerve fibers are exponential near threshold

Input-output (IO) properties of cochlear transduction are frequently determined by analyzing the average discharge rates of auditory nerve fibers (ANFs) in response to relatively long tonal stimulation. The ANFs in cats have spontaneous discharge rates (SRs) that are bimodally distributed, peaking at low (<0.5 spikes/s) and high (∼60 spikes/s) rates, and rate-level characteristics differ depending upon SR. In an effort to assess the instantaneous IO properties of ANFs having different SRs, static IO-curves were constructed from period histograms based on phase-locking of spikes to the stimulus waveform. These curves provide information unavailable in conventional average rate-level curves. We find that all IO curves follow an exponential trajectory. It is argued that the exponential behavior represents the transduction in the IHC and that the difference among ANFs having different SRs is predominantly a difference in gain attributed most likely to synaptic drive. © 2018 The authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

A Model of Electrically Stimulated Auditory Nerve Fiber Responses with Peripheral and Central Sites of Spike Generation

A computational model of cat auditory nerve fiber (ANF) responses to electrical stimulation is presented. The model assumes that (1) there exist at least two sites of spike generation along the ANF and (2) both an anodic (positive) and a cathodic (negative) charge in isolation can evoke a spike. A single ANF is modeled as a network of two exponential integrate-and-fire point-neuron models, referred to as peripheral and central axons of the ANF. The peripheral axon is excited by the cathodic charge, inhibited by the anodic charge, and exhibits longer spike latencies than the central axon; the central axon is excited by the anodic charge, inhibited by the cathodic charge, and exhibits shorter spike latencies than the peripheral axon. The model also includes subthreshold and suprathreshold adaptive feedback loops which continuously modify the membrane potential and can account for effects of facilitation, accommodation, refractoriness, and spike-rate adaptation in ANF. Although the model is parameterized using data for either single or paired pulse stimulation with monophasic rectangular pulses, it correctly predicts effects of various stimulus pulse shapes, stimulation pulse rates, and level on the neural response statistics. The model may serve as a framework to explore the effects of different stimulus parameters on psychophysical performance measured in cochlear implant listeners.

Neurogram을 이용한 인공와우 자극기법 평가 연구

Electrical stimulation is delivered to auditory nerve (AN) through the electrodes in cochlear implant system. Neurogram is a spectrogram that includes information of neural response to electrical stimulation. We hypothesized that the similarity between a neurogram and an input-sound spectrogram could show how well a cochlear implant system works. In this study, we evaluated electrical stimulus configuration of CIS strategy using the computational model. The computational model includes stochastic property and anatomical features of cat auditory nerve fiber. To evaluate similarity between a neurogram and an input-sound spectrogram, we calculated Structural Similarity Index (SSIM). The results show that the dynamic range and the stimulation rate per channel influenced SSIM. Finally, we suggested the optimal configuration within the given stimulus CIS. We expect that the results and the evaluating procedure could be employed to improve the performance of a cochlear implant system.

Temporal Integration of Sound Pressure Determines Thresholds of Auditory-Nerve Fibers

Current propositions of the quantity of sound driving the central auditory system, specifically around threshold, are diverse and at variance with one another. They include sound pressure, sound power, or intensity, which are proportional to the square of pressure, and energy, i.e., the integral of sound power over time. Here we show that the relevant sound quantity and the nature of the threshold can be obtained from the timing of the first spike of auditory-nerve (AN) fibers after the onset of a stimulus. We reason that the first spike is triggered when the stimulus reaches threshold and occurs with fixed delay thereafter. By probing cat AN fibers with characteristic frequency tones of different sound pressure levels and rise times, we show that the differences in relative timing of the first spike (including latencies >100 msec of fibers with low spontaneous rates) can be well accounted for by essentially linear integration of pressure over time. The inclusion of a constant pressure loss or gain to the integrator improves the fit of the model and also accounts for most of the variation of spontaneous rates across fibers. In addition, there are tight correlations among delay, threshold, and spontaneous rate. First-spike timing cannot be explained by models based on a fixed pressure threshold, a fixed power or intensity threshold, or an energy threshold. This suggests that AN fiber thresholds are best measured in units of pressure by time. Possible mechanisms of pressure integration by the inner hair cell-AN fiber complex are discussed.

Medial efferent effects on auditory-nerve responses to tail-frequency tones II: alteration of phase.

It is often assumed that at frequencies in the tuning-curve tail there is a passive, constant coupling of basilar-membrane motion to inner hair cell (IHC) stereocilia. This paper shows changes in the phase of auditory-nerve-fiber (ANF) responses to tail-frequency tones and calls into question whether basilar-membrane-to-IHC coupling is constant. In cat ANFs with characteristic frequencies > or = 10 kHz, efferent effects on the phase of ANF responses to tail-frequency tones were measured. Efferent stimulation caused substantial changes in ANF phase (deltaphi) (range -80 degrees to +60 degrees, average -15 degrees, a phase lag) with the largest changes at sound levels near threshold and 3-4 octaves below characteristic frequency (CF). At these tail frequencies, efferent stimulation had much less effect on the phase of the cochlear microphonic (CM) than on ANF phase. Thus, since CM is synchronous with basilar-membrane motion for low-frequency stimuli in the cochlear base, the efferent-induced change in ANF phase is unlikely to be due entirely to a change in basilar-membrane phase. At tail frequencies, ANF phase changed with sound level (often by 90 degrees-180 degrees) and the deltaphi from a fiber was positively correlated with the slope of its phase-versus-sound-level function at the same frequency, as if deltaphi were caused by a 2-4 dB increase in sound level. This correlation suggests that the processes that produce the change in ANF phase with sound level at tail frequencies are also involved in producing deltaphi. It is hypothesized that both efferent stimulation and increases in sound level produce similar phase changes because they both produce a similar mix of cochlear vibrational modes.

Cat auditory nerve fiber recovery functions

The literature contains few examples of auditory nerve fiber response recovery functions. To provide more information, hazard functions were compiled from 10–120 s samples of spontaneous and driven activity. Responses formed two groups that differed only in the amount of activity at short (&amp;lt;1.5 ms) inter-spike intervals. The larger group possessed hazard functions described by an equation with one time constant, i.e., R(t)=3D 1-e(S-t)/t, where R(t) is the amount of response recovery at time t, S is the starting time of the recovery process (analogous to absolute refractoriness), and t is the time constant. The other, smaller group possessed hazard functions similar to that reported by Gaumond et al. [J. Neurophysiol. 48, 856–873 (1982)], in which a portion of the response recovers rapidly at the end of the absolute refractory period and the remainder recovers more slowly. This behavior was described well by a two-process equation, i.e., R(t)=3DM (1−e(Se−t)/t e)+(1−M)=(1−e(Sl−t)/tl) where Se and Sl are the starting times of the ‘‘early’’ and ‘‘late’’ recovery processes, t e and t l are their time constants, and M is the amount of recovery attributable to the early process. S, Se and Sl ranged from 0.7–1.3 ms, t e ranged from 1.2–1.5 ms, t and t l ranged from 3–6 ms and usually decreased somewhat with level, and M ranged from 0.2–0.4.

Physiological mechanisms of frequency discrimination

I recall a conversation with Bob Bilger that occurred around 1971, when I was a graduate student in Bioacoustics at Pitt. He said (translated into English from the original Bilgerian), ‘‘Did it ever occur to you that you can apply principles of signal detection theory in physiological studies? That way, you could examine neural and psychophysical performance using comparable measures and possibly shed light on underlying mechanisms.’’ This idea, which predated its implementation in auditory research by at least 10 years, was initially lost on me. Much later, I recognized the possibilities and performed some pertinent experiments with various colleagues. We utilized 2AFC adaptive tracking procedures to investigate performance limits of single cat auditory nerve fibers and neural populations on pure-tone frequency discrimination tasks. For single-fiber responses, we found that: (1) decision strategies based on detecting differences in spike counts fail to account for perceptual findings by wide margins; and (2) strategies based on detecting differences in phase-locked activity produce data that match perceptual findings well at low frequencies but rapidly degrade at frequencies &amp;gt;1500 Hz. Examining neural population performance using a stochastic excitation pattern model, we found that a strategy which simply estimates the spatial location of the response peak accounts well for perceptual performance at most frequencies. See, Bob? Your idea works, and I WAS paying attention.

Medial efferent effects on auditory-nerve responses to tail-frequency tones. I. Rate reduction

One way medial efferents are thought to inhibit responses of auditory-nerve fibers (ANFs) is by reducing the gain of the cochlear amplifier thereby reducing motion of the basilar membrane. If this is the only mechanism of medial efferent inhibition, then medial efferents would not be expected to inhibit responses where the cochlear amplifier has little effect, i.e., at sound frequencies in the tails of tuning curves. Inhibition at tail frequencies was tested for by obtaining randomized rate-level functions from cat ANFs with high characteristic frequencies (CF > or = 5 kHz), stimulated with tones two or more octaves below CF. It was found that electrical stimulation of medial efferents can indeed inhibit ANF responses to tail-frequency tones. The amplitude of efferent inhibition depended on both sound level (largest near to threshold) and frequency (largest two to three octaves below CF). On average, inhibition of high-CF ANFs responding to 1 kHz tones was around 5 dB. Although an efferent reduction of basilar-membrane motion cannot be ruled out as the mechanism producing the inhibition of ANF responses to tail frequency tones, it seems more likely that efferents produce this effect by changing the micromechanics of the cochlear partition.

A stochastic model of the electrically stimulated auditory nerve: single-pulse response.

Most models of neural response to electrical stimulation, such as the Hodgkin-Huxley equations, are deterministic, despite significant physiological evidence for the existence of stochastic activity. For instance, the range of discharge probabilities measured in response to single electrical pulses cannot be explained at all by deterministic models. Furthermore, there is growing evidence that the stochastic component of auditory nerve response to electrical stimulation may be fundamental to functionally significant physiological and psychophysical phenomena. In this paper we present a simple and computationally efficient stochastic model of single-fiber response to single biphasic electrical pulses, based on a deterministic threshold model of action potential generation. Comparisons with physiological data from cat auditory nerve fibers are made, and it is shown that the stochastic model predicts discharge probabilities measured in response to single biphasic pulses more accurately than does the equivalent deterministic model. In addition, physiological data show an increase in stochastic activity with increasing pulse width of anodic/cathodic biphasic pulses, a phenomenon not present for monophasic stimuli. These and other data from the auditory nerve are then used to develop a population model of the total auditory nerve, where each fiber is described by the single-fiber model.

A stochastic model of the electrically stimulated auditory nerve: pulse-train response.

The single-pulse model of the companion paper [1] is extended to describe responses to pulse trains by introducing a phenomenological refractory mechanism. Comparisons with physiological data from cat auditory nerve fibers are made for pulse rates between 100 and 800 pulses/s. First, it is shown that both the shape and slope of mean discharge rate curves are better predicted by the stochastic model than by the deterministic model. Second, while interpulse effects such as refractory effects do indeed increase the dynamic range at higher pulse rates, both the physiological data and the model indicate that much of the dynamic range for pulse-train stimuli is due to stochastic activity. Third, it is shown that the stochastic model is able to predict the general magnitude and behavior of variance in discharge rate as a function of pulse rate, while the deterministic model predicts no variance at all.

Discriminability of vowel representations in cat auditory-nerve fibers after acoustic trauma.

This paper attempts to connect deficits seen in the neural representation of speech with perceptual deficits. Responses of auditory-nerve fibers were studied in cats exposed to acoustic trauma. Four synthetic steady-state vowels were used as test signals; these stimuli are identical, except that the second format (F2) resonator in the synthesizer was set to 1.4, 1.5, 1.7, or 2 kHz, producing four spectra that differ mainly in the vicinity of the F2 frequency. These stimuli were presented to a large population (523) of auditory-nerve fibers in four cats with sloping high-frequency threshold shifts that reached 50-70 dB at 2-4 kHz. In normal animals, May et al. [Auditory Neurosci 3, 135-162 (1996)] showed previously that the discharge rates of fibers with best frequencies near the F2 frequencies provide enough information to allow discrimination of these stimuli at the performance levels shown by cats in behavioral experiments. Here it is shown that, after acoustic trauma, there is essentially no rate information which would allow the vowels with different F2 frequencies to be discriminated. However, information that could allow discrimination remains in the temporal (phase-locked) aspects of the responses.

Effects of acoustic trauma on the representation of the vowel "eh" in cat auditory nerve fibers.

A population study of cat auditory-nerve fibers was used to characterize the permanent deficits induced by exposure to 110-115 dB SPL, narrow-band noise. Fibers in the region of acoustic trauma (roughly 1-6 kHz) showed a loss of sensitivity at best frequency (BF) of about 50-60 dB and an increased tuning bandwidth. A correlation between weakened two-tone suppression and loss of sensitivity was found for fibers with BFs above 1 kHz. Single-fiber responses to the vowel "eh" were recorded at intensities ranging from near threshold to a maximum of about 110 dB SPL. In normal cochleas, the temporal response patterns show a capture phenomenon, in which the first two formant frequencies dominate the responses at high sound levels among fibers with BFs near the formant frequencies. After acoustic trauma, fibers in the region of threshold shift synchronized to a broad range of the vowel's harmonics and thus did not show capture by the second formant at any sound level used. The broadband nature of this response is consistent with the broadened tuning observed in the damaged fibers, but may also reflect a weakening of compressive nonlinearities responsible for synchrony capture in the normal cochlea.

The periodogram and Allan variance reveal fractal exponents greater than unity in auditory-nerve spike trains

Auditory-nerve spike trains exhibit fractal behavior, and therefore traditional renewal-point-process models fail to describe them adequately. Previous measures of the fractal exponent of these spike trains are based on the Fano factor and consequently cannot exceed unity. Two estimates of the fractal exponent are considered which do not suffer from this limit: one derived from the Allan variance, which was developed by the authors, and one based on the periodogram. These measures indicate that fractal exponents do indeed exceed unity for some nerve-spike recordings from stimulated primary afferent cat auditory-nerve fibers.

Long-term adaptation in cat auditory-nerve fiber responses.

Driven responses of cat auditory-nerve fibers to long-duration characteristic-frequency (CF) tones could decrease substantially over time periods ranging from seconds to minutes. In extreme cases, the discharge rate could fall before the pre-stimulation spontaneous rate (SR). Reductions in response were characterized by two processes, each of which followed a decaying exponential function. long-term adaptation affects the discharge rate in the first several seconds following stimulus onset. The average amount in high-SR fibers was 42.5% for tones at 20-40 dB SL, and the mean time constant was 3.65 s. Long-term adaptation increased significantly with sensation level (SL, or level above threshold), decreased with SR, and was not significantly correlated with CF or fiber response threshold. Time constants did not depend on CF, SR, or SL. Very-long-term adaptation refers to further, smaller reductions in the discharge rate that accumulate over a period of minutes. Fiber responses formed two groups. The larger group adapted with a mean time constant of 45.22 s for CF tones at 20-40 dB SL, and the smaller group did not adapt over very long terms. Considerable variability in amounts of long-term and very-long-term effects do not arise from cochlear mechanics or middle ear muscle activity. No long-term effects were observed in responses of fibers directly stimulated by high-intensity electrical pulses present at rates up to 500/s through a cochlear implant. This suggests that the effects do not arise from fundamental differences in spike-generating properties of spiral ganglion cells. The data suggest that long-term adaptation may occur either when neurotransmitter utilization at inner hair cell synapses exceeds the rate at which it is replenished from global stores or uptake mechanisms, or which metabolic resources influencing neurotransmitter release become depleted. The neural data are related to perceptual findings in human listeners, in which unusually large amounts of tone decay may be observed at high frequencies, and they indicate that the perceptual effects originate peripherally.

Shapes of cat auditory nerve fiber tuning curves

Tuning curves of auditory nerve fibers in normal-hearing cats were fitted by a computational model comprising four processes. One process accounts for sensitivity in tuning curve tails and consists of an approximation to bandpass filtering by extracochlear structures. The second and third processes describe passive and active components of basilar membrane (BM) mechanics, respectively. The former consists of a lowpass filter function, which provides baseline threshold sensitivity and filtering above characteristic frequency (CF), and the latter consists of a Gaussian that accounts for sharp tuning and high sensitivity around CF. A fourth process, modeled as a high-pass filter, was needed in many fits to account for breaks and plateaus in threshold sensitivity at frequencies above CF. The latter three processes operated on cochlear spatial coordinates rather than stimulus frequency. The four-process description closely accounted for shapes of most tuning curves. Tuning curve tails possessed minima at 40–80 dB SPL, and minima increased with fiber CF. High-frequency cutoffs of tail filters tended to increase with CF, but low-frequency cutoffs were generally constant across CF. Functions describing tails varied from ear to ear but behaved in a similar manner for fibers from a single ear. Passive components of BM resonances possessed baselines with sensitivities that decreased with CF and cutoff slopes that increased with CF. The magnitude of the active component increased smoothly with CF over an 80 + dB range, and its spatial extent was essentially constant at 1.5 mm or 6% of cochlear length regardless of gain magnitude, fiber CF, or threshold sensitivity. Tuning curves from fibers with high and medium spontaneous rates (SRs) and similar CFs had nearly identical shapes, with the sole difference being essentially constant differences in sensitivity across the entire excitatory frequency range. Tuning curve shapes from fibers with low SRs were more variable. These could either resemble those obtained from similarly-tuned fibers with higher SRs, or they could exhibit lower tip-to-tail ratios and reduced active component magnitudes. The latter were typically associated with low maximum discharge rates.

Discharge-rate dependence of refractory behavior of cat auditory-nerve fibers

A descriptive model for auditory-nerve (AN) refractory periods is described. The model assumes that interspike intervals consist of a constant-length absolute refractory period (ARP), followed by random-length relative refractory period (RRP) and a random-length waiting time to the next spike. Both the RRP and waiting time are exponentially distributed. This model fits AN hazard functions sufficiently well to provide estimates of the ARP and RRP durations for each fiber. The ARP is found to be constant, independent of discharge rate, with mean value between 0.56 and 0.86 ms in data from 7 experiments. The RRP decreases in duration as discharge rate increases; RRP mean length is less than 2 ms in most cases. There is an additional, slow component of recovery, lasting 20–40 ms, which is not modeled. The variation in RRP with discharge rate is shown to be capable of accounting for the deviation of AN regularity from that predicted for a Poisson process. Finally, properties of peaks seen in hazard functions just at the end of the ARP are described; these peaks are not included in the model, but are shown to be especially prominent in low and medium spontaneous rate fibers.

Three-process model of auditory-nerve fiber tuning curve shapes

Cat auditory-nerve fiber tuning curves were fitted by a three-process model consisting of an acoustic resonance (for tuning curve ‘‘tails’’), a skewed function formed by subtracting two exponentials (for basilar membrane resonances), and a Gaussian function (for the active gain responsible for sharp tuning). Thresholds were taken as the sum of pressures provided by the two resonances, multiplicatively enhanced in a spatially dependent manner by the active gain. Fits were generally excellent. ‘‘Tails’’ possessed center frequencies (1–3 kHz) and minima (60–80 dB SPL) that varied from animal to animal and depended on fiber CF. Basilar membrane resonances possessed average minima of 30–50 dB SPL in low-threshold fibers, tuning that increased in sharpness with CF or threshold, and center frequencies that were typically lower (for CFs ≳1 kHz) or higher (for CFs &amp;lt;1 kHz) than the center frequencies of the associated active gains. Average active gain magnitude in low-threshold fibers increased smoothly from &amp;lt;10 dB in low-CF fibers to ≳40 dB in high-CF fibers, and was reduced or absent in most high-threshold fibers. The average spatial extent of the active gain was remarkably constant at 1 mm regardless of gain magnitude, fiber CF, or threshold sensitivity. [Work supported by NIDCD.]

A model for the responses of low-frequency auditory-nerve fibers in cat

A computational model was developed for the responses of low-frequency auditory-nerve (AN) fibers in cat. The goal was to produce realistic temporal response properties and average discharge rates in response to simple and complex stimuli. Temporal and average-rate properties of AN responses change as a function of sound-pressure level due to nonlinearities in the auditory periphery. The input stage of the AN model is a narrow-band filter that simulates the mechanical tuning of the basilar membrane. The parameters of this filter vary continuously as a function of stimulus level via a feedback mechanism, simulating the compressive nonlinearity associated with the mechanics of the basilar membrane. A memoryless, saturating nonlinearity and two low-pass filters simulate transduction and membrane properties of the inner hair cell (IHC). A diffusion model for the IHC-AN synapse introduces adaptation. Finally, a nonhomogeneous Poisson process, modified by absolute and relative refractoriness, provides the output discharge times. Responses to several different stimuli are presented. These responses illustrate nonlinear temporal response properties that cannot be achieved with linear models for AN fibers.

A composite model of the auditory periphery for the processing of speech based on the filter response functions of single auditory-nerve fibers

A composite model of the auditory periphery, based upon a unique analysis technique for deriving filter response characteristics from cat auditory-nerve fibers, is presented. The model is distinctive in its ability to capture a significant broadening of auditory-nerve fiber frequency selectivity as a function of increasing sound-pressure level within a computationally tractable time-invariant structure. The output of the model shows the tonotopic distribution of synchrony activity of single fibers in response to the steady-state vowel [e] presented over a 40-dB range of sound-pressure levels and is compared with the population-response data of Young and Sachs (1979). The model, while limited by its time invariance, accurately captures most of the place-synchrony response patterns reported by the Johns Hopkins group. In both the physiology and in the model, auditory-nerve fibers spanning a broad tonotopic range synchronize to the first formant (F1), with the proportion of units phase-locked to F1 increasing appreciably at moderate to high sound-pressure levels. A smaller proportion of fibers maintain phase locking to the second and third formants across the same intensity range. At sound-pressure levels of 60 dB and above, the vast majority of fibers with characteristic frequencies greater than 3 kHz synchronize to F1 (512 Hz), rather than to frequencies in the most sensitive portion of their response range. On the basis of these response patterns it is suggested that neural synchrony is the dominant auditory-nerve representation of formant information under "normal" listening conditions in which speech signals occur across a wide range of intensities and against a background of unpredictable and frequently intense acoustic interference.

Coding of spectral fine structure in the auditory nerve. II: Level-dependent nonlinear responses

Phase-locked discharge patterns of single cat auditory-nerve fibers were analyzed in response to complex tones centered at fiber characteristic frequency (CF). Signals were octave-bandwidth harmonic complexes defined by a center frequency F and an intercomponent spacing factor N, such that F/N was the fundamental frequency. Parameters that were manipulated included the phase spectrum, the number of components, and the intensity of the center component. Analyses employed Fourier transforms of period histograms to assess the degree to which responses were synchronized to the frequencies present in the acoustic stimulus. Several nonlinearities were observed in the response as intensity was varied between threshold and 80-90 dB SPL. Response nonlinearities were strong for all signals except those with random phase spectra. The most commonly observed nonlinearity was an emphasis of one or more stimulus components in the response. The degree of nonlinearity usually increased with intensity and signal complexity and decreased with fiber frequency selectivity. Half-wave rectification introduced synchronization to the missing fundamental. The strength of the response at the fundamental was related to stimulus crest factor. Signals with low center frequencies and high crest factors often elicited instantaneous discharge rates at the theoretical maximum of pi CF. This suggests that the probability of spike generation approaches one during high-amplitude waveform segments. Response nonlinearity was interpreted as arising from three sources, namely, cochlear mechanics, compression of instantaneous discharge rate, and saturation of average discharge rate. At near-threshold intensities, fibers with high spontaneous rates exhibited responses that were linear functions of stimulus waveshape, whereas fibers with low spontaneous spike rates produced responses that were best described in terms of an expansive nonlinearity.