Temporal Damping in Response to Broadband Noise. I. Inferior Colliculus

Midbrain sensitivity to auditory motion studied with dichotic sweeps of broadband noise

Many neurons in the central nucleus of the inferior colliculus (IC) show sensitivity to interaural time differences (ITDs), which is thought to be relayed from the brainstem. However, studies with interaural phase modulation of pure tones showed that IC neurons have a sensitivity to changes in ITD that is not present at the level of the brainstem. This sensitivity has been interpreted as a form of sensitivity to motion. A new type of stimulus is used here to study the sensitivity of IC neurons to dynamic changes in ITD, in which broad- or narrowband stimuli are swept through a range of ITDs with arbitrary start-ITD, end-ITD, speed, and direction. Extracellular recordings were obtained under barbiturate anesthesia in the cat. We applied the same analyses as previously introduced for the study of responses to tones. We find effects of motion which are similar to those described in response to interaural phase modulation of tones. The size of the effects strongly depended on the motion parameters but was overall smaller than reported for tones. We found that the effects of motion could largely be explained by the temporal response pattern of the neuron such as adaptation and build-up. Our data add to previous evidence questioning true coding of motion at the level of the IC.

Enhancement of phase-locking in rodents. II. An axonal recording study in chinchilla

The trapezoid body (TB) contains axons of neurons residing in the anteroventral cochlear nucleus (AVCN) that provide excitatory and inhibitory inputs to the main monaural and binaural nuclei in the superior olivary complex (SOC). To understand the monaural and binaural response properties of neurons in the medial and lateral superior olive (MSO and LSO), it is important to characterize the temporal firing properties of these inputs. Because of its exceptional low-frequency hearing, the chinchilla (Chinchilla lanigera) is one of the widely used small animal models for studies of hearing. However, the characterization of the output of its ventral cochlear nucleus to the nuclei of the SOC is fragmentary. We obtained responses of TB axons to stimuli typically used in binaural studies and compared these responses to those of auditory nerve (AN) fibers, with a focus on temporal coding. We found enhancement of phase-locking and entrainment, i.e., the ability of a neuron to fire action potentials at a certain stimulus phase for nearly every stimulus period, in TB axons relative to AN fibers. Enhancement in phase-locking and entrainment are quantitatively more modest than in the cat but greater than in the gerbil. As in these species, these phenomena occur not only in low-frequency neurons stimulated at their characteristic frequency but also in neurons tuned to higher frequencies when stimulated with low-frequency tones, to which complex phase-locking behavior with multiple modes of firing per stimulus cycle is frequently observed.NEW & NOTEWORTHY The sensitivity of neurons to small time differences in sustained sounds to both ears is important for binaural hearing, and this sensitivity is critically dependent on phase-locking in the monaural pathways. Although studies in cat showed a marked improvement in phase-locking from the peripheral to the central auditory nervous system, the evidence in rodents is mixed. Here, we recorded from AN and TB of chinchilla and found temporal enhancement, though more limited than in cat.

Use of reverse noise to measure ongoing delay

Counts of spike coincidences provide a powerful means to compare responses to different stimuli or of different neurons, particularly regarding temporal factors. A drawback is that these methods do not provide an absolute measure of latency, i.e., the temporal interval between stimulus features and response. It is desirable to have such a measure within the analysis framework of coincidence counting. Single neuron responses were obtained, from 130 fibers in several tracts (auditory nerve, trapezoid body, lateral lemniscus), to a broadband noise and its polarity-inverted version. The spike trains in response to these stimuli are the "forward noise" responses. The same stimuli were also played time-reversed. The resulting spike trains were then again time-reversed: These are the "reverse-noise" responses. The forward and reverse responses were then analyzed with the coincidence count methods we have introduced earlier. Correlograms between forward- and reverse-noise responses show maxima at values consistent with latencies measured with other methods; the pattern of latencies with characteristic frequency, sound pressure level, and recording location was also consistent. At low characteristic frequencies, correlograms were well-predicted by reverse-correlation functions. We conclude that reverse noise provides an easy and reliable means to estimate latency of auditory nerve and brainstem neurons.

Temporal Correlates to Monaural Edge Pitch in the Distribution of Interspike Interval Statistics in the Auditory Nerve

Pitch is a perceptual attribute enabling perception of melody. There is no consensus regarding the fundamental nature of pitch and its underlying neural code. A stimulus which has received much interest in psychophysical and computational studies is noise with a sharp spectral edge. High-pass (HP) or low-pass (LP) noise gives rise to a pitch near the edge frequency (monaural edge pitch; MEP). The simplicity of this stimulus, combined with its spectral and autocorrelation properties, make it an interesting stimulus to examine spectral versus temporal cues that could underly its pitch. We recorded responses of single auditory nerve (AN) fibers in chinchilla to MEP-stimuli varying in edge frequency. Temporal cues were examined with shuffled autocorrelogram (SAC) analysis. Correspondence between the population’s dominant interspike interval and reported pitch estimates was poor. A fuller analysis of the population interspike interval distribution, which incorporates not only the dominant but all intervals, results in good matches with behavioral results, but not for the entire range of edge frequencies that generates pitch. Finally, we also examined temporal structure over a slower time scale, intermediate between average firing rate and interspike intervals, by studying the SAC envelope. We found that, in response to a given MEP stimulus, this feature also systematically varies with edge frequency, across fibers with different characteristic frequency (CF). Because neural mechanisms to extract envelope cues are well established, and because this cue is not limited by coding of stimulus fine-structure, this newly identified slower temporal cue is a more plausible basis for pitch than cues based on fine-structure.

Neural tuning matches frequency-dependent time differences between the ears

The time it takes a sound to travel from source to ear differs between the ears and creates an interaural delay. It varies systematically with spatial direction and is generally modeled as a pure time delay, independent of frequency. In acoustical recordings, we found that interaural delay varies with frequency at a fine scale. In physiological recordings of midbrain neurons sensitive to interaural delay, we found that preferred delay also varies with sound frequency. Similar observations reported earlier were not incorporated in a functional framework. We find that the frequency dependence of acoustical and physiological interaural delays are matched in key respects. This suggests that binaural neurons are tuned to acoustical features of ecological environments, rather than to fixed interaural delays. Using recordings from the nerve and brainstem we show that this tuning may emerge from neurons detecting coincidences between input fibers that are mistuned in frequency.

DOI:http://dx.doi.org/10.7554/eLife.06072.001

When you hear a sound, such as someone calling your name, it is often possible to make a good estimate of where that sound came from. If the sound came from the left, it would reach your left ear before your right ear, and vice versa if the sound originated from your right. The time that passes between the sound reaching each ear is known as the ‘interaural time difference’. Previous research has suggested that specific neurons in the brain respond to specific interaural time differences, and the brain then uses this interaural time difference to locate the sound.

Sounds come in various frequencies from high-pitched alarms to low bass tones, and how a neuron responds to interaural time differences appears to change according to the frequency of the sound being played. For example, a given neuron may respond to a 200- microsecond interaural time difference when a tone is played at a high frequency, but show no response to this time difference when the tone is played at a low frequency. To date, researchers had been unable to explain why this occurs.

Here, Benichoux et al. investigated this topic by playing a variety of sounds to anaesthetized cats. Electrodes were used to record the responses of individual neurons in the cats' brains, and the properties of the sound waves that reached the cats' ears were also recorded. These experiments revealed that the time it took a sound to travel from a location to each of the cats' ears, and consequently the interaural time difference, depended on whether it was a high-pitched or a low-pitched sound. This happened because different properties of the environment, such as the angle of the cat's head, affected specific frequencies in different ways.

As expected, the neurons' responses were also affected by sound frequency. Indeed, the neurons' behaviour mirrored that of the sound waves themselves. This shows that neurons do not, as previously thought, simply react to specific interaural differences. Instead, these neurons use both sound frequency and interaural time differences to produce a thorough approximation of the sound's location. The precise mechanisms that generate this brain adaptation to the animal's environment remain to be determined.

DOI:http://dx.doi.org/10.7554/eLife.06072.002

Estimating characteristic phase and delay from broadband interaural time difference tuning curves

Characteristic delay and characteristic phase are shape parameters of interaural time difference tuning curves. The standard procedure for the estimation of these parameters is based on the measurement of delay curves measured for tonal stimuli with varying frequencies. Common to all procedures is the detection of a linear behavior of the phase spectrum. Hence a reliable estimate can only be expected if sufficiently many relevant frequencies are tested. Thus, the estimation precision depends on the given bandwidth. Based on a linear model, we develop and implement methods for the estimation of characteristic phase and delay from a single broadband tuning curve. We present two different estimation algorithms, one based on a Fourier-analytic interpretation of characteristic delay and phase, and the other based on mean square error minimization. Estimation precision and robustness of the algorithms are tested on artificially generated data with predetermined characteristic delay and phase values, and on sample data from electrophysiological measurements in birds and in mammals. Increasing the signal-to-noise ratio or the bandwidth increases the estimation accuracy of the algorithms. Frequency band location and strong rectification also affect the estimation accuracy. For realistic bandwidths and signal-to-noise ratios, the minimization algorithm reliably and robustly estimates characteristic delay and phase and is superior to the Fourier-analytic method. Bandwidth-dependent significance thresholds allow to assess whether the estimated characteristic delay and phase values are meaningful shape parameters of a measured tuning curve. These thresholds also indicate the sampling rates needed to obtain reliable estimates from interaural time difference tuning curves.

Coincidence detection in the medial superior olive: mechanistic implications of an analysis of input spiking patterns

Coincidence detection by binaural neurons in the medial superior olive underlies sensitivity to interaural time difference (ITD) and interaural correlation (ρ). It is unclear whether this process is akin to a counting of individual coinciding spikes, or rather to a correlation of membrane potential waveforms resulting from converging inputs from each side. We analyzed spike trains of axons of the cat trapezoid body (TB) and auditory nerve (AN) in a binaural coincidence scheme. ITD was studied by delaying "ipsi-" vs. "contralateral" inputs; ρ was studied by using responses to different noises. We varied the number of inputs; the monaural and binaural threshold and the coincidence window duration. We examined physiological plausibility of output "spike trains" by comparing their rate and tuning to ITD and ρ to those of binaural cells. We found that multiple inputs are required to obtain a plausible output spike rate. In contrast to previous suggestions, monaural threshold almost invariably needed to exceed binaural threshold. Elevation of the binaural threshold to values larger than 2 spikes caused a drastic decrease in rate for a short coincidence window. Longer coincidence windows allowed a lower number of inputs and higher binaural thresholds, but decreased the depth of modulation. Compared to AN fibers, TB fibers allowed higher output spike rates for a low number of inputs, but also generated more monaural coincidences. We conclude that, within the parameter space explored, the temporal patterns of monaural fibers require convergence of multiple inputs to achieve physiological binaural spike rates; that monaural coincidences have to be suppressed relative to binaural ones; and that the neuron has to be sensitive to single binaural coincidences of spikes, for a number of excitatory inputs per side of 10 or less. These findings suggest that the fundamental operation in the mammalian binaural circuit is coincidence counting of single binaural input spikes.

The interaural time difference pathway: a comparison of spectral bandwidth and correlation sensitivity at three anatomical levels

Temporal differences between the two ears are critical for spatial hearing. They can be described along axes of interaural time difference (ITD) and interaural correlation, and their processing starts in the brainstem with the convergence of monaural pathways which are tuned in frequency and which carry temporal information. In previous studies, we examined the bandwidth (BW) of frequency tuning at two stages: the auditory nerve (AN) and inferior colliculus (IC), and showed that BW depends on characteristic frequency (CF) but that there is no difference in the mean BW of these two structures when measured in a binaural, temporal framework. This suggested that there is little frequency convergence in the ITD pathway between AN and IC and that frequency selectivity determined by the cochlear filter is preserved up to the IC. Unexpectedly, we found that AN and IC neurons can be similar in CF and BW, yet responses to changes in interaural correlation in the IC were different than expected from coincidence patterns ("pseudo-binaural" responses) in the AN. To better understand this, we here examine the responses of bushy cells, which provide monaural inputs to binaural neurons. Using broadband noise, we measured BW and correlation sensitivity in the cat trapezoid body (TB), which contains the axons of bushy cells. This allowed us to compare these two metrics at three stages in the ITD pathway. We found that BWs in the TB are similar to those in the AN and IC. However, TB neurons were found to be more sensitive to changes in stimulus correlation than AN or IC neurons. This is consistent with findings that show that TB fibers are more temporally precise than AN fibers, but is surprising because it suggests that the temporal information available monaurally is not fully exploited binaurally.

Temporal properties of responses to sound in the ventral nucleus of the lateral lemniscus

Besides the rapid fluctuations in pressure that constitute the "fine structure" of a sound stimulus, slower fluctuations in the sound's envelope represent an important temporal feature. At various stages in the auditory system, neurons exhibit tuning to envelope frequency and have been described as modulation filters. We examine such tuning in the ventral nucleus of the lateral lemniscus (VNLL) of the pentobarbital-anesthetized cat. The VNLL is a large but poorly accessible auditory structure that provides a massive inhibitory input to the inferior colliculus. We test whether envelope filtering effectively applies to the envelope spectrum when multiple envelope components are simultaneously present. We find two broad classes of response with often complementary properties. The firing rate of onset neurons is tuned to a band of modulation frequencies, over which they also synchronize strongly to the envelope waveform. Although most sustained neurons show little firing rate dependence on modulation frequency, some of them are weakly tuned. The latter neurons are usually band-pass or low-pass tuned in synchronization, and a reverse-correlation approach demonstrates that their modulation tuning is preserved to nonperiodic, noisy envelope modulations of a tonal carrier. Modulation tuning to this type of stimulus is weaker for onset neurons. In response to broadband noise, sustained and onset neurons tend to filter out envelope components over a frequency range consistent with their modulation tuning to periodically modulated tones. The results support a role for VNLL in providing temporal reference signals to the auditory midbrain.

Axonal recordings from medial superior olive neurons obtained from the lateral lemniscus of the chinchilla (Chinchilla laniger)

Interaural time differences (ITDs) are a major cue for localizing low-frequency (<1.5 kHz) sounds. Sensitivity to this cue first occurs in the medial superior olive (MSO), which is thought to perform a coincidence analysis on its monaural inputs. Extracellular single-neuron recordings in MSO are difficult to obtain because (1) MSO action potentials are small and (2) a large field potential locked to the stimulus waveform hampers spike isolation. Consequently, only a limited number of studies report MSO data, and even in these studies data are limited in the variety of stimuli used, in the number of neurons studied, and in spike isolation. More high-quality data are needed to better understand the mechanisms underlying neuronal ITD-sensitivity. We circumvented these difficulties by recording from the axons of MSO neurons in the lateral lemniscus (LL) of the chinchilla, a species with pronounced low-frequency sensitivity. Employing sharp glass electrodes we successfully recorded from neurons with ITD sensitivity: the location, response properties, latency, and spike shape were consistent with an MSO axonal origin. The main difficulty encountered was mechanical stability. We obtained responses to binaural beats and dichotic noise bursts to characterize the best delay versus characteristic frequency distribution, and compared the data to recordings we obtained in the inferior colliculus (IC). In contrast to most reports in other rodents, many best delays were close to zero ITD, both in MSO and IC, with a majority of the neurons recorded in the LL firing maximally within the presumed ethological ITD range.

Linear summation in the barn owl's brainstem underlies responses to interaural time differences

The neurophonic potential is a synchronized frequency-following extracellular field potential that can be recorded in the nucleus laminaris (NL) in the brainstem of the barn owl. Putative generators of the neurophonic are the afferent axons from the nucleus magnocellularis, synapses onto NL neurons, and spikes of NL neurons. The outputs of NL, i.e., action potentials of NL neurons, are only weakly represented in the neurophonic. Instead, the inputs to NL, i.e., afferent axons and their synaptic potentials, are the predominant origin of the neurophonic (Kuokkanen PT, Wagner H, Ashida G, Carr CE, Kempter R. J Neurophysiol 104: 2274-2290, 2010). Thus in NL the monaural inputs from the two brain sides converge and create a binaural neurophonic. If these monaural inputs contribute independently to the extracellular field, the response to binaural stimulation can be predicted from the sum of the responses to ipsi- and contralateral stimulation. We found that a linear summation model explains the dependence of the responses on interaural time difference as measured experimentally with binaural stimulation. The fit between model predictions and data was excellent, even without taking into account the nonlinear responses of NL coincidence detector neurons, although their firing rate and synchrony strongly depend on the interaural time difference. These results are consistent with the view that the afferent axons and their synaptic potentials in NL are the primary origin of the neurophonic.

Auditory abstraction from spectro-temporal features to coding auditory entities

The auditory system extracts behaviorally relevant information from acoustic stimuli. The average activity in auditory cortex is known to be sensitive to spectro-temporal patterns in sounds. However, it is not known whether the auditory cortex also processes more abstract features of sounds, which may be more behaviorally relevant than spectro-temporal patterns. Using recordings from three stations of the auditory pathway, the inferior colliculus (IC), the ventral division of the medial geniculate body (MGB) of the thalamus, and the primary auditory cortex (A1) of the cat in response to natural sounds, we compared the amount of information that spikes contained about two aspects of the stimuli: spectro-temporal patterns, and abstract entities present in the same stimuli such as a bird chirp, its echoes, and the ambient noise. IC spikes conveyed on average approximately the same amount of information about spectro-temporal patterns as they conveyed about abstract auditory entities, but A1 and the MGB neurons conveyed on average three times more information about abstract auditory entities than about spectro-temporal patterns. Thus, the majority of neurons in auditory thalamus and cortex coded well the presence of abstract entities in the sounds without containing much information about their spectro-temporal structure, suggesting that they are sensitive to abstract features in these sounds.

Mechanisms of Sound Localization in Mammals

The ability to determine the location of a sound source is fundamental to hearing. However, auditory space is not represented in any systematic manner on the basilar membrane of the cochlea, the sensory surface of the receptor organ for hearing. Understanding the means by which sensitivity to spatial cues is computed in central neurons can therefore contribute to our understanding of the basic nature of complex neural representations. We review recent evidence concerning the nature of the neural representation of auditory space in the mammalian brain and elaborate on recent advances in the understanding of mammalian subcortical processing of auditory spatial cues that challenge the “textbook” version of sound localization, in particular brain mechanisms contributing to binaural hearing.

Comparison of bandwidths in the inferior colliculus and the auditory nerve. II: Measurement using a temporally manipulated stimulus

To localize low-frequency sounds, humans rely on an interaural comparison of the temporally encoded sound waveform after peripheral filtering. This process can be compared with cross-correlation. For a broadband stimulus, after filtering, the correlation function has a damped oscillatory shape where the periodicity reflects the filter's center frequency and the damping reflects the bandwidth (BW). The physiological equivalent of the correlation function is the noise delay (ND) function, which is obtained from binaural cells by measuring response rate to broadband noise with varying interaural time delays (ITDs). For monaural neurons, delay functions are obtained by counting coincidences for varying delays across spike trains obtained to the same stimulus. Previously, we showed that BWs in monaural and binaural neurons were similar. However, earlier work showed that the damping of delay functions differs significantly between these two populations. Here, we address this paradox by looking at the role of sensitivity to changes in interaural correlation. We measured delay and correlation functions in the cat inferior colliculus (IC) and auditory nerve (AN). We find that, at a population level, AN and IC neurons with similar characteristic frequencies (CF) and BWs can have different responses to changes in correlation. Notably, binaural neurons often show compression, which is not found in the AN and which makes the shape of delay functions more invariant with CF at the level of the IC than at the AN. We conclude that binaural sensitivity is more dependent on correlation sensitivity than has hitherto been appreciated and that the mechanisms underlying correlation sensitivity should be addressed in future studies.

Temporal damping in response to broadband noise. II. Auditory nerve

II. Auditory nerve. Low-frequency neurons in the inferior colliculus (IC) show a damped oscillatory response as a function of interaural time differences (ITDs) of broadband noise. It was previously shown that several features of such noise-delay functions are well predicted by the composite curve, generated by the linear summation of responses to tones with varying ITD. This indicates a surprising degree of linearity at the midbrain level of the auditory pathway. A similar comparison between responses to tones and to noise has not been made at a more peripheral, monaural level and it is therefore unclear to what extent this linearity reflects peripheral physiology. Here, we compare cat auditory nerve responses to broadband noise and to isolevel tones. We constructed shuffled autocorrelograms for responses to tones and summed across frequencies to obtain a monaural composite curve. We then compare this composite curve to the shuffled autocorrelogram of responses to broadband noise and find that the match between tonal and noise responses is poorer at the level of the auditory nerve than at the level of the IC. The apparent linearity of responses in the IC is thus even more surprising than was apparent from its original report because it results from mechanisms interposed between the auditory nerve and the IC.

Comparison of bandwidths in the inferior colliculus and the auditory nerve. I. Measurement using a spectrally manipulated stimulus

A defining feature of auditory systems across animal divisions is the ability to sort different frequency components of a sound into separate neural frequency channels. Narrowband filtering in the auditory periphery is of obvious advantage for the representation of sound spectrum and manifests itself pervasively in human psychophysical studies as the critical band. Peripheral filtering also alters coding of the temporal waveform, so that temporal responses in the auditory periphery reflect both the stimulus waveform and peripheral filtering. Temporal coding is essential for the measurement of the time delay between waveforms at the two ears-a critical component of sound localization. A number of human psychophysical studies have shown a wider effective critical bandwidth with binaural stimuli than with monaural stimuli, although other studies found no difference. Here we directly compare binaural and monaural bandwidths (BWs) in the anesthetized cat. We measure monaural BW in the auditory nerve (AN) and binaural BW in the inferior colliculus (IC) using spectrally manipulated broadband noise and response metrics that reflect spike timing. The stimulus was a pair of noise tokens that were interaurally in phase for all frequencies below a certain flip frequency (f(flip)) and that had an interaural phase difference of pi above f(flip). The response was measured as a function of f(flip) and, using a separate stimulus protocol, as a function of interaural correlation. We find that both AN and IC filter BW depend on characteristic frequency, but that there is no difference in mean BW between the AN and IC.

Binaural and cochlear disparities

Binaural auditory neurons exhibit "best delays" (BDs): They are maximally activated at certain acoustic delays between sounds at the two ears and thereby signal spatial sound location. BDs arise from delays internal to the auditory system, but their source is controversial. According to the classic Jeffress model, they reflect pure time delays generated by differences in axonal length between the inputs from the two ears to binaural neurons. However, a relationship has been reported between BDs and the frequency to which binaural neurons are most sensitive (the characteristic frequency), and this relationship is not predicted by the Jeffress model. An alternative hypothesis proposes that binaural neurons derive their input from slightly different places along the two cochleas, which induces BDs by virtue of the slowness of the cochlear traveling wave. To test this hypothesis, we performed a coincidence analysis on spiketrains of pairs of auditory nerve fibers originating from different cochlear locations. In effect, this analysis mimics the processing of phase-locked inputs from each ear by binaural neurons. We find that auditory nerve fibers that innervate different cochlear sites show a maximum number of coincidences when they are delayed relative to each other, and that the optimum delays decrease with characteristic frequency as in binaural neurons. These findings suggest that cochlear disparities make an important contribution to the internal delays observed in binaural neurons.

Interaural delay-dependent changes in the binaural difference potential of the human auditory brain stem response

Binaural difference potentials (BDs) are thought to be generated by neural units in the brain stem responding specifically to binaural stimulation. They are computed by subtracting the sum of monaural responses from the binaural response, BD = B - (L + R). BDs in dependency on the interaural time difference (ITD) have been measured and compared to the Jeffress model in a number of studies with conflicting results. The classical Jeffress model assuming binaural coincidence detector cells innervated by bilateral excitatory cells via two delay lines predicts a BD latency increase of ITD/2. A modification of the model using only a single delay line as found in birds yields a BD latency increase of ITD. The objective of this study is to measure BDs with a high signal-to-noise ratio for a large range of ITDs and to compare the data with the predictions of some models in the literature including that of Jeffress. Chirp evoked BDs were recorded for 17 ITDs in the range from 0 to 2 ms at a level of 40 dB nHL for four channels (A1, A2, PO9, PO10) from 11 normal hearing subjects. For each binaural condition 10,000 epochs were collected while 40,000 epochs were recorded for each of the two monaural conditions. Significant BD components are observed for ITDs up to 2 ms. The peak-to-peak amplitude of the first components of the BD, DP1-DN1, is monotonically decreasing with ITD. This is in contrast with click studies which reported a constant BD-amplitude for ITDs up to 1 ms. The latency of the BD-component DN1 is monotonically, but nonlinearly increasing with ITD. In the current study, DN1 latency is found to increase faster than ITD/2 but slower than ITD incompatible with either variant of the Jeffress model. To describe BD waveforms, the computational model proposed by Ungan et al. [Hearing Research 106, 66-82, 1997] using ipsilateral excitatory and contralateral inhibitory inputs to the binaural cells was implemented with only four parameters and successfully fitted to the BD data. Despite its simplicity the model predicts features which can be physiologically tested: the inhibitory input must arrive slightly before the excitatory input, and the duration of the inhibition must be considerably longer than the standard deviations of excitatory and inhibitory arrival times to the binaural cells. With these characteristics, the model can accurately describe BD amplitude and latency as a function of the ITD.

Correlation index: a new metric to quantify temporal coding

The standard procedure to study temporal encoding of sound waveforms in the auditory system has been Fourier analysis of responses to periodic stimuli. We introduce a new metric--correlation index (CI)--which is based on a simple counting of spike coincidences. It can be used for responses to aperiodic stimuli and does not require knowledge of the stimulus. Moreover, the basic procedure of comparing spiketimes in spiketrains is more physiological than currently used methods for temporal analysis. The CI is the peak value of the normalized shuffled autocorrelogram (SAC), which provides a quantitative summary of temporal structure in the neural response to arbitrary stimuli. We illustrate the CI and SACs by comparing temporal coding in the auditory nerve and output fibers of the cochlear nucleus.

Auditory midbrain and nerve responses to sinusoidal variations in interaural correlation

The human sensitivity to interaural temporal differences in the acoustic waveforms to the two ears shows remarkable acuity but is also very sluggish. Fast changes in binaural parameters are not detectable, and this inability contrasts sharply with the excellent temporal resolution of the monaural auditory system. We studied the response of binaural neurons in the inferior colliculus of the cat to sinusoidal changes in the interaural correlation of broadband noise. Responses to the same waveforms were also obtained from auditory nerve fibers and further analyzed with a coincidence analysis. Overall, the auditory nerve and inferior colliculus showed a similar ability to code changes in interaural correlation. This ability extended to modulation frequencies an order of magnitude higher than the highest frequencies detected binaurally in humans. We conclude that binaural sluggishness is not caused by a lack of temporal encoding of fast binaural changes at the level of the midbrain. We hypothesize that there is no neural substrate at the level of the midbrain or higher to read out this temporal code and that this constitutes a low-pass "sluggishness" filter.