Auditory dynamic range derived from the mean rate-intensity function in the cat

Cross-modality modulation of auditory midbrain processing of intensity information

In nature, animals constantly receive a multitude of sensory stimuli, such as visual, auditory, and somatosensory. The integration across sensory modalities is advantageous for the precise processing of sensory inputs which is essential for animals to survival. Although some principles of cross-modality integration have been revealed by many studies, little insight has been gained into its functional potentials. In this study, the functional influence of cross-modality modulation on auditory processing of intensity information was investigated via recording neuronal activity in the auditory midbrain (i.e., inferior colliculus, IC) under the conditions of visual, auditory, and audiovisual stimuli, respectively. Results demonstrated that combined audiovisual stimuli either enhanced or suppressed the responses of IC neurons compared to auditory stimuli alone, even though the same visual stimuli alone induced no response. Audiovisual modulation appeared to be strongest when the combined audiovisual stimuli were located at the best auditory azimuth of neurons as well as when presented with intensity at near-threshold levels. Additionally, the rate-intensity function of IC neurons to auditory stimuli was expanded or compressed by audiovisual modulation, which was highly dependent on the minimal threshold (MT) of neurons. Lowering of the MT and greater audiovisual modulation for the neuron indicated an intensity-specific enhancement of auditory intensity sensitivity by cross-modality modulation. Overall, evidence suggests a potential functional role of cross-modality modulation in IC that serves to instruct adaptive plasticity to enhance the auditory perception of intensity information.

Simultaneous dichotic loudness balance (SDLB): Why loudness "fatigues" with two ears but not with one

In the laboratory method called simultaneous dichotic loudness balance (SDLB), the contribution-to-loudness that arises from the listener's continually exposed "fatiguing" ear is required to be matched (balanced) by the listener, by adjusting the intensity of a noncontinuous stimulus at the other ("comparison") ear. The latter intensity usually declines, allegedly indicating "fatigue" of the contribution-to-loudness from the "fatiguing" ear. However, no "fatigue" is found when one ear alone (with the other ear in quiet) experiences a continuous well-suprathreshold stimulus. This is a quandary that remains unresolved. The present article offers a resolution, through a novel conceptual model in which any ear experiencing stimuli acts through a well-characterized physiological structure, the olivocochlear bundle, to "turn down the volume" at the opposite ear. The model explains how "fatigue" varies in eight different SDLB conditions, some having several subconditions. Altogether, the model demonstrates that "fatigue" is an artifact of SDLB itself.

An improved model for the rate-level functions of auditory-nerve fibers

Acoustic information is conveyed to the brain by the spike patterns in auditory-nerve fibers (ANFs). In mammals, each ANF is excited via a single ribbon synapse in a single inner hair cell (IHC), and the spike patterns therefore also provide valuable information about those intriguing synapses. Here we reexamine and model a key property of ANFs, the dependence of their spike rates on the sound pressure level of acoustic stimuli (rate-level functions). We build upon the seminal model of Sachs and Abbas (1974), which provides good fits to experimental data but has limited utility for defining physiological mechanisms. We present an improved, physiologically plausible model according to which the spike rate follows a Hill equation and spontaneous activity and its experimentally observed tight correlation with ANF sensitivity are emergent properties. We apply it to 156 cat ANF rate-level functions using frequencies where the mechanics are linear and find that a single Hill coefficient of 3 can account for the population of functions. We also demonstrate a tight correspondence between ANF rate-level functions and the Ca(2+) dependence of exocytosis from IHCs, and derive estimates of the effective intracellular Ca(2+) concentrations at the individual active zones of IHCs. We argue that the Hill coefficient might reflect the intrinsic, biochemical Ca(2+) cooperativity of the Ca(2+) sensor involved in exocytosis from the IHC. The model also links ANF properties with properties of psychophysical absolute thresholds.

The temporal weighting of loudness: effects of the level profile

In four experiments, we studied the influence of the level profile of time-varying sounds on temporal perceptual weights for loudness. The sounds consisted of contiguous wideband noise segments on which independent random-level perturbations were imposed. Experiment 1 showed that in sounds with a flat level profile, the first segment receives the highest weight (primacy effect). If, however, a gradual increase in level (fade-in) was imposed on the first few segments, the temporal weights showed a delayed primacy effect: The first unattenuated segment received the highest weight, while the fade-in segments were virtually ignored. This pattern argues against a capture of attention to the onset as the origin of the primacy effect. Experiment 2 demonstrated that listeners adjust their temporal weights to the level profile on a trial-by-trial basis. Experiment 3 ruled out potentially inferior intensity resolution at lower levels as the cause of the delayed primacy effect. Experiment 4 showed that the weighting patterns cannot be explained by perceptual segmentation of the sounds into a variable and a stable part. The results are interpreted in terms of memory and attention processes. We demonstrate that the prediction of loudness can be improved significantly by allowing for nonuniform temporal weights.

Intensity-difference limens predicted from the click-evoked peripheral N1: The mid-level hump and its implications for intensity encoding

The intensity-difference limen (DL) for an acoustic click rises at moderate click levels, a feature called the 'mid-level hump'. It has long been hypothesized that, because a click does not evoke sustained firing in any primary afferent, the DL must therefore originate from the initial burst of synchronized spikes in the eighth nerve. That burst causes the N1 component of the peripheral compound action potential (CAP). It should therefore be possible to predict click DLs from N1 potentials. Here, a Signal Detection model, using a series expansion, was used to derive equations in N1 for the level-dependence of the DL. The first-order equation predicts a dependence on the standard deviation of N1, and an inverse dependence on the rate-of-growth of the mean N1. The second-order equation is more complicated. Both approximations were applied to N1s from the cat. Both produced a mid-level hump; at its peak, the DLs from the second-order approximation were the smaller ones, and were of the same order of magnitude as the empirical DLs. Overall, the computations show that the rate-of-growth of the mean N1, not the standard deviation of N1, determines the hump in the empirical DL.

Dynamic range relations for auditory primary afferents

Dynamic range is one of four attributes typically assigned to the plot of firing rate vs. stimulus level of an auditory primary afferent. Dynamic range is generally understood to be the contiguous range of sound-pressure-level over which the neuron can indicate some small level change. Typically, however, dynamic range has been quantified as the width in decibels between the endpoints of the rate-level plot, which is not a measure of sensitivity to level change. A sensitivity measure is provided here by first deriving an equation for the intensity-difference limen (DL) in terms of attributes of the rate-level curve. The result is a generally U-shaped curve of DL vs. level. Any given criterion DL corresponds to a horizontal line cutting the DL curve at two points, with the separation in decibels between those points providing a dynamic range for that DL criterion. Plotting the dynamic ranges vs. the respective DLs yields a dynamic range curve. These were made for 62 afferents from the cat. The dynamic ranges of sloping-saturating rate-level plots do not exceed those for sigmoidal plots until the DL criterion reaches 50 dB, supporting the conclusion of Palmer and Evans [Cochlear fibre rate-intensity functions: no evidence for basilar membrane nonlinearities, Hearing Research 2 (1980) 319-326] that sloping saturation is not a reflection of cochlear nonlinearity.

Threshold vs duration for Gaussian-shaped tone-pips of one to four periods duration

Detection thresholds were obtained for Gaussian-shaped tone-pips of 1, 2, 3, or 4 periods duration for 20 frequencies spanning 50-3,000 Hz, in quiet, and in high-pass noise, for a single exceptionally patient and experienced listener. Thresholds were fitted by straight lines in decibels SPL versus the logarithm of duration. Slopes fell into 3 distinct regimes of frequency-dependence under both listening conditions. Models of Garner and of Formby, et al. do not account for this relation.

Afferent response parameters derived from postmasker probe-detection thresholds: 'the decay of sensation' revisited

The classical model of forward masking postulates that the detection threshold for a tone probe that follows a stimulus of similar frequency content is elevated relative to the quiet threshold because the probe must evoke a just-detectable increment in a decaying postmasker sensation. That postmasker decay is charted by probe-detection thresholds if the sensation increment is small and constant. This model was examined for a 2-kHz Gaussian-shaped probe and a 2-kHz forward masker, based on the model's assumption that a just-detectable increment in sensation results from a just-detectable increment in level. Psychometric functions for detection were obtained at 2.5-30 ms postmasker. Their means and standard deviations generally decreased with delay. It was assumed that standard deviation is related to the putative just-detectable level increment by a simple monotonic transformation. Thus, if the standard deviation of the psychometric function for probe detection is neither small nor constant, then the corresponding just-detectable increment in level is neither small nor constant, and the just-detectable increment in sensation is neither small nor constant. The classical model also fails to allow for the variability of internal events. The concept of detection threshold as a sensation increment was preserved in a Signal Detection model, that does allow for internal variability. In this model the postmasker residual is the input to a probe detector. The new model produces an equation for the just-detectable level increment as a function of probe delay. Comparison data were generated by again assuming some relation between the standard deviation of the psychometric function for detection, and the just-detectable increment in level. The fit of equation to data yields robust values for the probe detector's maximum firing rate, dynamic range, and spike-counting time. All that is required to account for the decay of sensation, for a pure tone, is a single neuron operating at some higher center.

Coding of sound intensity in the chick cochlear nerve

Tuning curves, spontaneous activity, and rate-intensity (RI) functions were obtained from units in the chick cochlear nerve. The characteristic frequency (CF) was determined from each tuning curve. The shape of each RI function was subjectively evaluated and assigned to one of four RI types. The breakpoint, discharge rate at the highest SPLs, and slopes of the primary and secondary segments were quantified for each function. The CF and RI type were then related to these variables. A new RI function was observed in which the discharge activity in the secondary segment diminished as stimulus level increased above the breakpoint. This function was called a "sloping-down" type. In 959 units, saturating, sloping-up, sloping-down, and straight RI types were identified in 39.2, 35.5, 12.6, and 12.7% of the sample, respectively. The slope of the primary segment was nearly the same in each of the four types and averaged 5.48 S. s(-1). dB(-1) across all units. The slopes of the secondary segments formed four groupings when segregated by RI type based on the subjective assignments and averaged 0.03, 1.22, -0.90, and 3.95 S. s(-1). dB(-1) in the saturating, sloping-up, sloping-down, and straight types, respectively. The data describing the secondary segments of all units were fit with a multi-compartment polynomial and showed a continuous distribution that segregated, with some overlap, into the different RI categories. The proportion of RI types, as well as the secondary and primary slopes were approximately constant across CFs. In addition, it would appear that the other parameters that define the four types were, for the most part, homogeneously distributed across the frequency axis of the chick inner ear. Finally, a comparison of RI functions having a common CF suggested that the compressive nonlinearity that determines RI type may be a phenomenon localized to individual hair cells in the bird ear.

Estimating auditory neuronal dynamic range using a fitted function

To obtain the dynamic range of an auditory afferent, the neuron's firing rate is plotted versus stimulus level, and the dynamic range is taken as the difference between the threshold for evoked firing, and the level at which firing rate saturates. Those dynamic range endpoints are typically defined in terms of the neuron's spontaneous firing rate and its maximum firing rate, according to a plurality of schemes, each of which depends on user-chosen sets of numerical criteria. The dynamic ranges predicted by some of these schemes are compared for the first time, and the resulting estimates can differ by a factor of 2. A step can be taken towards standardizing the measurement of neuronal dynamic range, if dynamic range is incorporated into a rate-level function as a parameter. To build this function, it is first assumed that the neuron's rate-level response reaches half its maximum at a level half-way between the threshold and the level at saturation, i.e. at threshold plus half the dynamic range. Then the firing rates at threshold and at threshold plus dynamic range are defined according to the most popular of the endpoint schemes. The resulting equation produces credible estimates of neuronal properties when fitted, and correctly predicts the behavior of the slope of the empirical rate-level plot [McGee, 1983. M.S. thesis, Creighton University; Ohlemiller et al., 1991. J. Acoust. Soc. Am. 90, 274-287]. Thus, despite not being deterministic, the new equation has remarkable predictive power. When two of the rate-level functions are added and weighted, the resulting equation fits sloping-saturating data better than any functions presently employed.

The intensity-difference limen for Gaussian-enveloped stimuli as a function of level: tones and broadband noise

Van Schijndel et al. [J. Acoust. Soc. Am. 105, 3425-3435 (1999)] have proposed that the internal excitation evoked by an auditory stimulus is segmented into "windows" according to the stimulus spectrum and stimulus length. This "multiple looks" model accounts for the mid-duration hump they observed in plots of intensity-difference limens (DLs) versus pip duration for Gaussian-shaped 1- and 4-kHz tones, an effect replicated by Baer et al. [J. Acoust. Soc. Am. 106, 1907-1916 (1999)]. However, van Schijndel et al. and Baer et al. used few levels. A greater number of levels were used by Nizami (1999) for Gaussian-shaped 2-kHz tone-pips whose equivalent rectangular duration (D) was 1.25 ms. The DLs show the mid-level hump known for clicks [Raab and Taub, J. Acoust. Soc. Am. 46, 965-968 (1969)]. At some duration this pattern must become the "near-miss to Weber's law." To determine this duration, as well as the level-dependence of the mid-duration hump, DLs were established for Gaussian-shaped 2-kHz tone-pips of D = 1.25, 2.51, and 10.03 ms at levels of 30-90 dB SPL. The across-subject average DLs for the tone-pips rise up at mid-levels for D= 1.25 and D = 2.51 ms. The DLs for D=2.51 ms are larger, creating the mid-duration hump. At all durations, the new DLs are smaller at high levels than at low levels, consistent with the near-miss to Weber's law. DLs were also obtained here for Gaussian-shaped broadband-noise pips of D=0.63, 1.25, 2.51, 5.02, and 10.03 ms. The DLs for the noise-pip show a mid-level hump for all pip durations. The noise-pip DLs decrease as the pip lengthens, such that the plot of DL versus log duration shows a linear decline, with no mid-duration hump. Analysis of variance reveals that the mid-level hump coexists with the classical patterns of level-dependence, perhaps reflecting the existence of two level-encoding mechanisms, one that depends on firing-rates counted over single neurons and which is responsible for the classical patterns, and one that depends on the initial coordinated burst of neuronal spikes caused by rapid ramping, and which presumably causes the mid-level hump.

Neural representation of sound amplitude by functionally different auditory receptors in crickets

The physiological characteristics of auditory receptor fibers (ARFs) of crickets, a model system for studying auditory behaviors and their neural mechanisms, are investigated. Unlike auditory receptor neurons of many animals, cricket ARFs fall into three distinct populations based on characteristic frequency (CF) [Imaizumi and Pollack, J. Neurosci. 19, 1508-1516 (1999)]. Two of these have CFs similar to the frequency component of communication signals or of ultrasound produced by predators, and a third population has intermediate CF. Here, sound-amplitude coding by ARFs is examined to gain insights to how behaviorally relevant sounds are encoded by populations of receptor neurons. ARFs involved in acoustic communication comprise two distinct anatomical types, which also differ in physiological parameters (threshold, response slope, dynamic range, minimum latency, and sharpness of tuning). Thus, based on CF and anatomy, ARFs comprise four populations. Physiological parameters are diverse, but within each population they are systematically related to threshold. The details of these relationships differ among the four populations. These findings open the possibility that different ARF populations differ in functional organization.

Discharge rate of the auditory nerve during noise revealed by electrocochlear stimulation

The purpose of this study was to evaluate the average discharge rate of all fibres in the whole auditory nerve (R(wn)) when a broad-band noise with steady-state effects is applied to the ear. We assessed the R(wn) parameter by detecting the state of refractoriness of the nerve during noise stimulation using an electric stimulus (ES) as a probe. The technique, applied in awake pre-implanted guinea pigs (Charlet de Sauvage et al., 1994), made it possible to obtain electro-acoustic responses (EARs), from which an estimate of the R(wn) parameter could be deduced. Negative current pulses of 100 micros duration, each followed by an identical pulse of positive polarity for charge balance, were applied between round window and indifferent vertex electrodes at intervals of 160 ms. The 120 ms wide-band noise masker started 92 ms before every other negative ES. The signal on the stimulating electrodes was averaged over a 5.12 ms window in synchrony with the negative pulse. EARs were obtained by alternately subtracting recordings during noise from those during silence. The R(wn) parameter was determined by comparing experimental and computed EAR patterns. For this purpose, a model of unit response incorporating changes in amplitude and conduction velocity during the relative refractory period was designed. The recovery function of the firing probability in response to ES was evaluated. Fibres were classified in different categories according to their background discharge rates. The probability of response of single fibres to ES in each category was calculated on the basis of their interval histograms during silence and noise. Individual spikes were combined accordingly to obtain the computed EAR waveform. R(wn) was determined by adjusting the EAR amplitude of the model in relation to that of the experimental EAR. R(wn) generally increases in a linear fashion with respect to noise intensity expressed in dB, thus following the increase in loudness perception estimated by Weber's law. At the highest noise levels, R(wn) tends to saturate. The estimated saturation rate was found to be about 380 spikes/s.