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1Eaton–Peabody Laboratory, Massachusetts Eye and Ear Infirmary, Boston; 2Speech and Hearing Bioscience and Technology Program, Harvard–Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge; and 3Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts
Submitted 26 October 2004; accepted in final form 24 April 2005
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ABSTRACT |
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INTRODUCTION |
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; Good et al. 1997; Lane et al. 2004; Saberi et al. 1991).
Psychophysical studies have shown that SRM occurs at all stimulus frequencies, although the mechanisms appear to differ at low and high frequencies. At high frequencies (>1.5 kHz), SRM appears to be achieved primarily through the monaural changes in signal-to-noise ratio (SNR) resulting from directionally dependent filtering by the head and pinna. In contrast, signal detection at low frequencies appears to be based on binaural hearing because masked thresholds in binaural listening are substantially better than those obtained by listening monaurally through either ear (Gilkey and Good 1995; Good et al. 1997). Here, we focus on low frequencies, which are important for speech recognition and are often spared in hearing-impaired listeners.
The ability of the binaural system to improve signal detection at low frequencies has been studied extensively by psychophysicists using the binaural masking level difference (BMLD) paradigm (for a thorough review see Durlach and Colburn 1978). In the most common BMLD experiments, identical noise is presented to both ears (N0), and the masked threshold for a signal (usually a pure tone) presented in phase at the two ears (S0) is compared with the threshold for a signal presented out of phase at the two ears (S
). These are the N0S0 and N0S conditions, respectively, and the N0S condition yields better thresholds because of the additional interaural phase cue. The difference in masked thresholds (the BMLD) between these two conditions is about 12–15 dB for a low-frequency (
500 Hz) pure tone in broadband noise. For the case when the noise is antiphasic instead of the signal (i.e., NS0 compared with N0S0), there is also a substantial, although slightly smaller BMLD of around 9–10 dB. Because there are no differences in signal-to-noise ratios at either ear between the BMLD conditions, the improvements in detectability must be attributed to the listener's ability to use the binaural system to exploit interaural timing cues.
A number of mathematical models are able to predict most psychophysical results on BMLDs (Colburn 1996; Colburn and Durlach 1978). Here, we focus on the cross-correlator model originally envisioned by Jeffress (1948) and developed by Colburn (1973, 1977a,b) because processing similar to the processing described by the model appears to occur in the medial superior olive (MSO) and because predictions of this model for the BMLD paradigm have been extensively studied. In this model, the stimulus waveform to each ear is processed by an auditory nerve fiber (ANF) model. Pairs of ANFs with the same characteristic frequencies (CFs) from each ear provide inputs to an array of delay lines and coincidence detectors, which fire when the neural inputs from the two sides coincide. The delay lines allow each coincidence detector to respond maximally to a different interaural time delay (ITD), its characteristic delay. Effectively, this model performs an instantaneous cross-correlation of the ANF responses, with each coincidence detector evaluating the correlation at its characteristic delay.
Colburn (1973, 1977a,b) studied how a cross-correlator network responds to BMLD stimuli. Briefly, coincidence detector units with a characteristic delay of 0 (0-ITD units) maximally respond to the N0 stimuli, whereas the remaining units produce a weaker response. When a signal is added in phase (N0S0), the firing rate for 0-ITD units tuned to the tone frequency increases, allowing the signal to be detected. If the signal is instead added out of phase (N0S), then the response of the 0-ITD units tuned to the tone frequency decreases because the signal decorrelates the inputs to the 0-ITD units at that frequency. This decrease in firing rate is more easily detected than the rate increase in the N0S0 case, thereby giving rise to the BMLD. In contrast, for the N stimulus, units with characteristic delays equal to one half the period of their CF ("-units") respond maximally. When the signal is added in phase (NS0), it causes the response of the -units tuned to the signal frequency to decrease as the signal reduces the correlation seen by the -units. By looking at the response changes across the neural population, Colburn's model successfully predicts most psychophysical results for BMLDs, including the threshold hierarchy where the N0S threshold is best, followed by NS0 and then N0S0.
Although controversy exists over the precise neural mechanisms, the neurons in the MSO appear to perform a cross-correlation operation similar to the one hypothesized by Jeffress and Colburn (Batra and Yin 2004; Batra et al. 1997; Yin and Chan 1990). As expected for a coincidence detector, units in the MSO receive binaural excitatory inputs, are sensitive to the ITD of tones and noise, and their best interaural phase can be predicted from the phases of the monaural responses (Batra et al. 1997; Goldberg and Brown 1969; Yin and Chan 1990). ITD-sensitive units in the MSO form a major projection to the inferior colliculus (IC; Loftus et al. 2004), where units with similar ITD sensitivity are also found (see Spitzer and Semple 1998; Yin and Chan 1990 for a comparison between MSO and IC unit responses). Because essentially all of the ascending auditory pathways synapse in the tonotopically organized central nucleus of the IC, the IC represents the first nucleus after the cochlear nucleus that contains nearly all of the information available to the auditory system and is the first to receive inputs from binaural nuclei. This convergence of information makes the IC an interesting and convenient nucleus for studying the neural mechanisms of SRM. Furthermore, the responses of the units in the IC almost certainly reflect additional processing beyond that of the MSO, and this processing, whose nature is still poorly understood, is likely to be behaviorally relevant.
An extensive series of neurophysiological studies of BMLD (e.g., Jiang et al. 1997a,b; McAlpine et al. 1996; Palmer et al. 1999, 2000) tested some of the predictions of the Colburn model for low-frequency units in the IC of anesthetized guinea pigs. Using 500-Hz pure tones in broadband noise, the authors showed that the masked thresholds for individual units in the anesthetized guinea pig vary with the interaural phases and interaural delays of the signal and masker (McAlpine et al. 1996). Furthermore, they measured neural thresholds for the N0S0 and N0S conditions for both individual units and populations of units (Jiang et al. 1997a,b). They showed that individual units could show positive or negative BMLDs, but that when averaged across their sample, the thresholds were better for the N0S condition than for the N0S0 condition, similar to the psychophysical results. As expected from the Colburn model, the unit population with the best average thresholds had an excitatory response in the N0 condition and showed a decrease in firing rate when the antiphasic signal S was added. Additionally, for a majority of neurons, the decreases in rate caused by the addition of S were similar to the changes in response seen for noise when the interaural correlation is reduced (Palmer et al. 1999), validating the general concept of the cross-correlator model. Finally, for a different unit sample, Palmer et al. (2000) showed that the NS0 thresholds averaged across all units were better than those seen for N0S0; however, the BMLDs were smaller than those seen with N0S. Overall, the average unit N0S0 neural thresholds are worse than the NS0 thresholds, which are worse than the N0S thresholds, consistent with the psychophysical threshold hierarchy. Additionally, the responses of low-frequency IC units are generally consistent with the cross-correlator model. However, these authors reported that some units seemed to reflect the effects of additional inhibition; the responses of these units could not be entirely predicted by a simple cross-correlator model. Also, in some cases, the individual unit thresholds did not match the trends seen in the average unit thresholds.
The physiological studies of BMLD provide a nice link between the cross-correlator model and the behavioral results in BMLD experiments, but the BMLD paradigm is somewhat unnatural. First, the signal in BMLD experiments is a pure tone, which contains only one frequency and has a flat temporal envelope, whereas most natural sounds are broadband and have large amplitude modulations. Moreover, for the antiphasic condition used in the BMLD experiments, the stimulus is out of phase for all frequencies, essentially giving a different ITD for each frequency. This stimulus condition differs from the case when a broadband sound source is placed at a location off the median vertical plane, which gives a constant ITD across all frequencies. In this case, the constant ITD gives rise to a different interaural phase difference (IPD) for each frequency component. Figure 1F shows the sloping phase responses in the left and right ears for a stimulus placed at –90° for a spherical model of the cat head. Clearly, the different slopes resulting from the fixed ITD yield different IPDs at each frequency. Consequently, it is not entirely clear how the physiological BMLD results generalize to the more natural situation where broadband signals and maskers are placed at different spatial locations. Caird et al. (1991) previously addressed some of these issues by looking at the effect of masker ITD on masked thresholds using either a pure tone or a vowel. The signal was placed at the best delay for each neuron, and the masked threshold was measured as a function of noise ITD. The masked thresholds were shown to reflect sensitivity of the neuron to the noise ITD, but the effects of placing the signal at different delays were not explored. Consequently, the effect of signal and masker separation was not directly addressed, nor was the effect of placing the signal at the worst delay, the condition predicted by the cross-correlator model to give the best thresholds.
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and McAlpine et al. (1996), we find that the single-unit thresholds depend on the locations of the signal and the masker relative to the unit's preferred azimuth. To find a neural correlate of SRM, we turned to the response of the neural population as a whole, which includes units with a variety of preferred azimuths. We show that the best thresholds across the population of IC units are similar to human behavioral thresholds for similar stimuli, in both SNR values and dependency on signal and masker separation. We also show that a substantial number of the units have responses to the signal and masker that are not predictable from the cross-correlator model. Moreover, in contrast to Jiang et al. (1997a,b), we find that the best masked thresholds for broadband chirp trains in noise occur for units where adding the signal increases the overall rate response to the noise. We argue that our results do not contradict those of Jiang et al. (1997a,b), but instead reflect differences between the temporal and/or spectral properties of the signals used in the two studies. Preliminary reports of this work have been presented (Kopco et al. 2003; Lane 2003; Lane et al. 2003, 2004). |
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METHODS |
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Responses of single units in the anesthetized cat inferior colliculus were recorded using methods similar to those of Litovsky and Delgutte (2002). Healthy, adult cats were initially anesthetized with an intraperitoneal injection of Dial in urethane (75 mg/kg), and additional doses were provided throughout the experiment to maintain deep anesthesia. Dexamethasone was injected intramuscularly to prevent swelling of neural tissue. A rectal thermometer was used to monitor the animal's temperature, which was maintained at 37–38°C. A tracheal cannula was inserted, both pinnae were partially dissected away, and the ear canals were cut to allow insertion of acoustic assemblies. A small hole was drilled in each bulla, and a 30-cm plastic tube was inserted and glued in place to prevent static pressure from building up in the middle ear. The animal was placed in a double-walled, electrically shielded, sound-proof chamber. The posterior surface of the IC was exposed through a posterior fossa craniotomy and aspiration of the overlying cerebellum. Parylene-insulated tungsten stereo microelectrodes (Micro Probe, Potomac, MD) were mounted on a remote-controlled hydraulic microdrive and inserted into the IC. The electrodes were oriented nearly horizontally in a parasagittal plane, approximately parallel to the isofrequency planes (Merzenich and Reid 1974). To improve single-unit isolation, the difference between the signals recorded from the two electrodes, which were separated by 125 µm, was often used as the input to the amplifier and spike timer. Spikes from single units were amplified and spike times measured with 1-µs resolution were stored in a computer file for analysis and display.
Histological processing for reconstruction of an electrode track was performed for one cat with a particularly large data yield. Only one track was made in this experiment. Every third 40-µm parasagittal section of the IC was immunostained for calretinin to visualize putative projections from the MSO (Adams 1995), and the remaining sections were Nissl-stained. Staining for calretinin is thought to reveal terminals of MSO axons because the MSO is the only auditory structure projecting to the IC in which calretinin labeling is extensive. The electrode track was evident in the Nissl slice and one of the calretinin slices, and the track traversed the calretinin region. The microelectrode depths at which we found units indicate that we were recording from the calretinin region, suggesting that these units received inputs from the MSO. The other experiments had similar electrode placements and single-unit responses including ITD sensitivity. Therefore most of the units in our sample are likely to receive MSO inputs, as expected from the anatomical results of Loftus et al. (2004) showing large projections of MSO to the low-frequency IC.
Stimuli
The signal used was a 200-ms-long train of broadband chirps with a 40-Hz repetition rate presented in continuous broadband noise (see Fig. 1, A and C). Each chirp's frequency was swept from 300 Hz to 30 kHz logarithmically and had an exponentially increasing envelope designed to produce a flat power spectrum. Consequently, both signal and noise had a relatively flat spectrum (Fig. 1, B and D), from 300 Hz to 30 kHz, before they were shaped by the frequency response of the head-related transfer functions (see following text). In some cases, we also used 100-Hz click trains as signals similar to the stimuli used in the psychophysical literature on SRM (e.g., Gilkey and Good 1995; Saberi et al. 1991); however, units in the IC often responded with higher, more sustained rates to the chirp trains, presumably because of the lower repetition rate of the chirp train. Only results obtained with the 40-Hz chirp trains are presented here.
Because SRM occurs for stimuli in all frequency ranges (Gilkey and Good 1995), we use head-related transfer functions (HRTFs) to simulate sounds at different azimuths. Using HRTFs allows us to simulate sounds in the free-field while still allowing complete control over the inputs to the two ears, thereby enabling us to easily present more traditional stimuli, such as monaural stimuli or binaural beats. The HRTFs represent the directionally dependent transformations of sound pressure from a specific location in free field to the ear canal (see Fig. 1, E and F). Virtual-space stimuli were synthesized by filtering the stimuli with the same HRTFs used by Litovsky and Delgutte (2002). The nonindividualized cat HRTFs were measured by Musicant et al. (1990) for frequencies >2 kHz and were simulated by a spherical-head model for frequencies <2 kHz. (The HRTF measurements were valid only for frequencies >2 kHz because of the limitations of the sound system and anechoic room.) The low-frequency HRTFs were the product of two components: 1) a directional component representing acoustic scattering by the cat head was provided by a rigid-sphere model with a diameter of 6.8 cm (Morse and Ingard 1968); and 2) a nondirectional, frequency-dependent gain representing the sound pressure amplification by the external ear was derived from measurements of acoustic impedance in the cat ear canal (Rosowski et al. 1988). Using a frequency-dependent weighting function, the model HRTF for frequencies <2 kHz was joined with the measured HRTF >2 kHz to obtain an HRTF covering the 0 to 40-kHz range.
This paper focuses on low-frequency neurons that are sensitive to ITD, the primary sound localization cue at low frequencies. Consequently, the spherical-head model provides most of the information in the HRTF for this work. Here the phase response of the HRTF was nearly a straight line for all azimuths, as expected for a pure delay, and the magnitude of the HRTF was relatively constant for different azimuths at these low frequencies (see example in Fig. 1, E and F). We expect our results to be similar to those obtained if only ITD was varied, provided the stimuli were appropriately shaped by the nondirectional HRTF magnitude (see RESULTS). However, the use of HRTFs would allow the present study of SRM to be easily extended to high frequencies in the future.
Experimental procedure
Search stimuli were either 200-ms chirp trains or broadband noise bursts. Both the azimuth and the mode of stimulation (binaural or monaural) of the search stimulus were varied in an effort to find a larger number of units and a more varied sample. Once a single unit was isolated, a frequency-tuning curve was measured by an automatic tracking procedure (Kiang and Moxon 1974) to determine the characteristic frequency (CFTC).
A noise-delay function was also measured: the unit response was measured as a function of the ITD of 200-ms bursts of "frozen" noise (Fig. 2A, solid line with error bars). The ITD was usually varied from –2,000 to 2,000 µs with a step size of 400 µs, although ITDs inside the physiological range (–290 to 290 µs as determined using our HRTFs) were often sampled more finely.
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A unit was included in this study if it had a low CFTC (
2.5 kHz), gave a sustained response to chirp trains at some signal azimuth, and was sensitive to ITD. We considered a unit ITD sensitive if the noise-delay function was modulated by 
50% (i.e., if the minimum discharge rate was less than half of the maximum rate). We measured the rate in a window that began 5 ms after the onset of the 200-ms noise burst and lasted 190 ms.
To determine the best ITD and best frequency (BFITD) for each unit, we fit the noise-delay function with a Gabor function (McAlpine and Palmer 2002b), which is a sinusoid with a Gaussian envelope
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); this constraint amounts to assuming that the characteristic phase (Yin and Kuwada 1983) is zero. Additionally, the Gabor function was half-wave rectified to ensure that the rate is never negative. A noise-delay function and its Gabor fit are shown in Fig. 2A. We show the Gabor function without half-wave rectification to emphasize the location of the worst ITD, which is not obvious from the rate function itself.1
To facilitate comparisons between the responses of units having different CFs and best ITDs, we define a relative IPD (
) in cycles by the equation
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In a few cases (four out of 31) for which the noise-delay functions were not sampled finely enough, the Gabor fit predicted best and worst azimuths at obviously incorrect locations. In these cases, the best ITD and BFITD were adjusted manually to give appropriate best and worst ITDs to match the best and worst azimuths.
Masked thresholds
To obtain neural thresholds that can be directly compared with psychophysical thresholds, which are based on a percentage correct criterion near 75%, masked threshold was defined as the lowest signal-to-noise ratio (SNR) at which the signal can be detected for 75% of the stimulus repetitions. Two different response metrics, mean rate and synchronized rate, were used to define detection thresholds. Mean rate is simply the number of spikes in the measurement window (from 5 to 195 ms post-stimulus onset), and the synchronized rate (Kim and Molnar 1979) is the Fourier component of the peristimulus time histogram at the signal repetition rate, 40 Hz. The synchronized rate, which is also the mean rate multiplied by the synchronization index or vector strength (Goldberg and Brown 1969), contains information about the spike timing as well as the number of spikes. Figure 3 shows both the mean rate (third row) and the synchronized rate (fourth row) as a function of noise level for one unit. The 200-ms chirp-train signal was held at 43 dB SPL, and the locations of the signal and the masker differ for each column of panels. To determine the masked threshold, we calculate the percentage of stimulus presentations for which the detection metric (mean rate or synchronized rate) is greater in the signal-plus-noise window compared with the noise-alone window (Fig. 3, bottom row). To improve the reliability of threshold estimates, the percentage-correct values were converted to z-scores by a Gaussian transform (Green and Swets 1974), smoothed with a three-point triangular filter, and then converted back to a percentage value. Because a signal can be detected through either an increase or decrease in rate (Jiang et al. 1997b), thresholds (circles in Fig. 3, bottom row) can occur when the percentage curve crosses either 75% (an increase in rate, see dashed lines) or 25% (a decrease in rate). This criterion gives the highest noise level or, equivalently, the lowest SNR, where the signal can still be detected 75% of the time.
We determined confidence intervals for the masked thresholds using bootstrapping methods (Efron and Tibshirani 1993). For each noise level, we sample the responses to each stimulus presentation with replacement, obtaining a new, "bootstrapped" set of spike trains. We then recompute the percentage curves for the new set of spike trains and recalculate the thresholds. The threshold is recomputed in this way 100 times. The error bars for the masked thresholds are then the range between the 10th and the 90th percentiles. Reliably estimating the thresholds was difficult because the percentage curves could be nonmonotonic, especially when the signal suppressed the noise response. Consequently, we required that, for a 25% threshold to be accepted, the signal had to decrease the overall rate below 25% for 80 out of 100 of the bootstrapped percentage curves. If this criterion was not met, then the 75% threshold was used. This requirement eliminated very low threshold SNRs that occurred as a result of spurious estimates of percentage-correct points. For the actual threshold estimate, we took the median of all the bootstrapped percentage curves and determined the threshold for this median curve.2
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RESULTS |
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) and cats (Hancock and Delgutte 2004; Joris et al. 2004), the best ITDs for our units tend to decrease with increasing BFITD, and a majority of the best ITDs and worst ITDs are outside the physiological range. For our purposes, the physiological range here is defined as ±290 µs, which corresponds to the ITDs measured at ±90° in our HRTFs. When azimuth is varied, other localization cues present in the HRTFs (interaural level differences and spectral cues) vary as well as ITD. For the majority of our units (n = 19), we compared the response for changes in noise azimuth and changes in noise ITD. Figure 2B shows one unit's rate response as a function of both noise azimuth (solid line) and noise-ITD (dash–dot line). The two responses were similar (Fig. 2B), although the rate was higher for the ITD-only condition. For the 19 units in our sample for which this measurement was taken, Fig. 2C compares the rate when only ITD was varied to the rate when the noise azimuth was varied. Except for very low discharge rates, the two responses are similar for all of these units, indicating that ITD largely determines these units' azimuth sensitivities.3
Dependency of single-unit masked thresholds on signal and masker azimuths
Based on previous physiological results (Caird et al. 1991; McAlpine et al. 1996), we expect that masked thresholds would change with signal and masker azimuth. Fig. 3 shows a typical unit's responses to the signal in noise and the noise alone as a function of noise level for three signal and masker configurations. The unit had a BFITD of 740 Hz and a best ITD of 290 µs, which corresponds to +90°; because the unit's worst ITD (about –380 µs) was outside the physiological range, its worst azimuth was –90°.
The first row sketches the three signal and masker configurations: signal and masker co-located at +90° (column A, S90, N90); signal at +90° and noise at –90° (column B, S90, N–90); and signal at –90° and noise at +90° (column C, S–90, N90). The second row in Fig. 3 shows the temporal discharge patterns for the signal-plus-noise interval (S +N) and the noise-alone interval (N) as a function of noise level. In these dot rasters, every dot represents a spike, and the solid lines separate the blocks of stimulus presentations for each noise level. As the noise level is raised, the signal response can be either overwhelmed by the noise response (excitatory or "line-busy" masking, columns A and C) or suppressed by the noise (suppressive masking, column B).
These rasters show a wide variety of potential cues for detecting the signal in noise. The types of cues available depend on the signal and masker configuration. For the signal at +90° in low-level noise (Fig. 3, columns A and B), the unit shows a highly synchronized response to the 40-Hz repetition rate of the chirp train. For this signal azimuth, the response to the signal plus noise is always greater than the noise-alone response. In contrast, the response to the signal at –90° (column C) is much weaker, consisting of only an onset response at the lowest noise level. At moderate noise levels for S–90, the signal suppresses the noise response, and a weak response at the signal repetition rate can be discerned, possibly reflecting a recovery from suppression during the silent periods between individual chirps in the train (see Fig. 1A). The signal can also alter the distribution of spike arrival times without causing a change in mean firing rate (column A). It is possible that any or all of these cues could be used to detect the signal, and an optimal central processor would use the best combination of cues, perhaps through the use of a signal template. Because we had only a few stimulus presentations for each stimulus condition, developing a reliable signal template was not feasible; instead, we chose to detect the signal through more traditional methods involving changes in mean rate and spike synchrony.
In the following, we first present all the results for thresholds based on mean rate and then discuss how synchronized rate thresholds differ at the end of the RESULTS section. As described in METHODS, the rate-based masked threshold is the highest noise level (or lowest signal-to-noise ratio) where the signal can still be detected 75% of the time, based on either an increase (75% mark in Fig. 3, row 5) or a decrease (25% mark in Fig. 3, row 5) in mean rate. The rate thresholds for the unit of Fig. 3 (shown as circles in the rows) differ substantially for the three signal and masker configurations. Specifically, the threshold for the co-located condition S+90, N+90 (column A) is about 18 dB poorer than the threshold for S+90, N–90 (column B), and the threshold for S–90, N+90 (column C) falls between the two despite the weak signal response in this case. In column C, the signal causes an increase in rate at the lowest noise levels; then, once the noise level is raised a few decibels, the signal can be detected through a decrease in rate (see dot raster; the signal's presence is shown by the suppression of the noise response). The signal can still clearly be detected for noise levels >49 dB (as shown in the dot raster), making about 52 dB the masked threshold. It is apparent from this example that by only allowing the signal to be detected through increases in rate (the 75% mark), the threshold signal-to-noise ratios for individual neurons would be systematically overestimated (Jiang et al. 1997).
Figure 4 (top) shows rate-based masked thresholds as a function of noise azimuth for the unit in Fig. 3 for four different signal azimuths. When the signal is at either +45 or +90°, moving the noise away from the signal to the ipsilateral side (negative azimuths) improves thresholds by 20 dB. When the signal is at 0°, thresholds also improve as the noise is moved away from the midline to the ipsilateral side, but they become slightly worse as the noise moves to the contralateral side (positive azimuths). For these three signal locations (S90, S45, and S0), the worst thresholds occur when the noise is placed near +90°, regardless of the signal location. However, when the signal is placed at –90°, the pattern is different: the thresholds increase slightly and then decrease as the noise is moved away from the signal.
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; Colburn 1973, 1977a,b; Jiang et al. 1997a,b) suggest that finding a correlate of SRM may require looking across a population of neurons and that individual unit thresholds are determined by the unit's azimuth/ITD preference. Indeed, this unit's thresholds do not consistently improve with spatial separation, even though they can improve by up to 20 dB in some cases. Instead, with the exception of S–90, the worst thresholds for this unit tend to occur when the noise is at the best azimuth (+90°). Figure 4, bottom, shows a histogram of the maximum threshold change seen as the masker is separated from the signal for all the units in our sample. For a majority of units, thresholds improve with signal and masker separation, but for some units, the thresholds actually become worse. Generally, the changes in threshold seem to be a consequence of placing the signal and the masker at favorable and unfavorable azimuths, a result similar to the one shown by Caird et al. (1991).
To test the effect of signal and masker separation on the thresholds for all of our units, we determined the worst threshold for each unit and examined how this worst threshold relates to the signal and masker locations. Figure 5 shows the noise azimuth that gives rise to the worst threshold, the "worst-threshold noise azimuth," as a function of both signal azimuth (top) and the unit's best azimuth (bottom, defined as the azimuth with the relative IPD nearest to 0 within the physiological range). If separation improved thresholds, then the worst threshold should occur when the signal and noise are at the same azimuth, i.e., the worst-threshold noise azimuth and the signal azimuth should be the same. Contrary to this prediction, the correlation between a unit's worst-threshold noise azimuth and the signal azimuth is very low (0.15) and is not significant (P = 0.2, two-sided t-test, n = 68). Thus the worst thresholds do not necessarily occur when the signal and the masker are co-located. In contrast, the correlation between the worst-threshold azimuth and the best azimuth is much higher (0.57) and is highly significant (P < 0.001), indicating that strong excitation by the masker tends to produce poor masked thresholds. Consequently, the individual unit responses do not show a correlate of spatial release from masking, consistent with previous BMLD studies. However, as suggested by the previous results (e.g., Caird et al. 1991; Colburn 1973, 1977a,b; Jiang et al. 1997a,b), a neural correlate of spatial release from masking may still exist in the response of a population of ITD-sensitive neurons.
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To test the hypothesis that the population of low-frequency, ITD-sensitive units is sufficient for explaining spatial release from masking at low frequencies, we defined a population threshold based on the "lower-envelope principle" (Parker and Newsome 1998). Specifically, for each signal and noise configuration, the population threshold is the best single-unit threshold in our sample of ITD-sensitive units. The top row of Fig. 6 shows both the individual mean-rate thresholds for all the units in our sample (dot-dash lines) and the population thresholds (thick solid lines) as a function of noise azimuth for three signal azimuths (arrows). The bottom row shows the synchronized rate thresholds, which are discussed later. Unlike the single-unit thresholds, the population thresholds do show a correlate of spatial release from masking in that they generally improve when the signal and noise are separated. Clearly, the curves do not show perfect spatial release from masking: for example, the thresholds do not improve for the signal at 45° when the noise azimuth is >45°, and the improvement for the signal at 0° is not symmetric with respect to the midline. It is not obvious whether obtaining a larger sample of neurons would improve the correlate for the S45° condition, but as units in the opposite IC are expected to have mirror-imaged threshold curves, incorporating units from both ICs would almost certainly eliminate the asymmetry in population thresholds for S0° (see following text). Overall, it seems that the combination of all the unit responses, each with a different azimuth preference, allows for a correlate of spatial release from masking to emerge in the population response.
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; see figure caption for details). In the psychophysical experiments, which were designed to have stimuli and methods similar to those in the physiological experiments, the signal (a 40-Hz chirp train with chirps that were swept logarithmically from 250 to 12,000 Hz) was low-pass filtered at 1,500 Hz, and the noise was low-pass filtered at 2,000 Hz to restrict listening to the low-CF region. As in the physiological experiments, the signal was fixed and the noise level was varied to find the masked threshold. To take into account differences in head size between cats and humans, we compare thresholds for similar ITDs rather than similar azimuths (see DISCUSSION). For example, a 30° azimuth in humans corresponds to an ITD of 250 µs, close to the 290-µs ITD produced by a sound source at 90° in cat. Consequently, Fig. 7B shows thresholds as a function of noise ITD with S30° for the human thresholds and with S90° for the cat thresholds. Figure 7A shows thresholds for S0°, which corresponds to the near-zero ITD in both species. The thin solid line in Fig. 7A shows the psychophysical thresholds reflected about 0°, essentially completing the curve under the assumption that the listener would have symmetric thresholds. To simulate the effects of having two ICs, we also reflected all of the individual unit thresholds about 0° and then recomputed the population thresholds using both original and reflected thresholds, making the curve symmetric about 0°. Both the shapes and the SNR values for the population threshold curves and the human behavioral data are similar. (The plotted thresholds were not vertically shifted to adjust the match between the two data sets, but reflect the true measured values.) There are clear differences between the functions, however. The human behavioral thresholds are better than the neural thresholds when both the signal and noise are near the midline, the neural thresholds improve more rapidly with separation than the human psychophysical thresholds, and the neural thresholds have a fine structure where the thresholds "dip" and then rise. Despite the differences (which are at least in part the result of having a limited number of neurons in the population), the overall match seems reasonably close, suggesting that the low-frequency, ITD-sensitive units in the IC probably serve as a neural substrate for spatial release from masking and that spatial release from masking at low frequencies is processed similarly across species.
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Having identified a neural correlate of SRM in the population response of ITD-sensitive units, we now focus on whether the responses of these units to SRM stimuli can be predicted by a cross-correlator model similar to the one described by Colburn (1973, 1977a,b). The example unit in Fig. 3 shows that, depending on the stimulus configuration, the signal can be detected through either an increase (A, B) or a decrease (C) in rate over the noise-alone response. Furthermore, masking can arise from the noise either suppressing (B) or overwhelming (A, C) the signal response. To test whether this diversity of responses is qualitatively consistent with the cross-correlator model, we implemented a simple cross-correlator model with parameters that matched the CF and best ITD for the unit in Fig. 3 (see caption of Fig. 8 for implementation details). The model response is shown in Fig. 8 for comparison with the unit's rate response in row 4 of Fig. 3.
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, 1977a,b), introducing a signal at a favorable azimuth tends to cause a higher correlation and an increase in rate over that evoked by the noise alone (Fig. 8, A and B), whereas introducing a signal at an unfavorable azimuth tends to reduce the correlation and decrease the rate (Fig. 8C). Similarly, a masker at a favorable azimuth (Fig. 8, A and C) masks by increasing the correlation so that the signal can no longer be detected, whereas a masker at an unfavorable azimuth masks by decorrelating the inputs from the two ears and bringing the rate down to zero (Fig. 8B). Overall, the model responses shown in Fig. 8 are qualitatively similar to the neural responses shown in Fig. 3. Some differences can also be seen. For example, in Fig. 8A, the signal-plus-noise rate decreases slightly as the noise level increases, an effect that reflects adaptation by the continuous noise in the auditory nerve fiber model response. A similar effect is seen for the neural response in Fig. 3A, but is more pronounced than that in the model. Also, in Fig. 8C, the model predicts that the signal, which is at the worst azimuth, should produce no response, but the unit gives an onset response to the signal. Nevertheless, the cross-correlator model predicts that both how noise masks the signal and how the signal is detected depend systematically on the positions of the signal and the masker relative to the unit's best and worst azimuths, and the results for this neuron seem qualitatively consistent with the model. Effect of noise on signal response depends on noise azimuth
To test whether the units' behavior is quantitatively consistent with the cross-correlator model, we define two metrics: one that characterizes how the noise masks the signal response and one that characterizes the effect of the signal on the noise response. The first metric, the "masking type index" (MTI) quantifies whether the noise masker overwhelms or suppresses the signal response at threshold. The MTI is the difference between the signal-in-noise rate at threshold, R(S + NTh), and the approximate signal-alone rate, R(S), the signal response with the noise at the lowest level. This difference is then normalized by whichever of the two rates is larger
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To test the predictions of the cross-correlator model for our entire sample of units, we examine how the MTI depends on noise azimuth for all of the units. The model predicts that, for the masker at a favorable azimuth, the number of coincidences increases with noise level to produce excitatory masking (MTI >0). For the noise at unfavorable azimuths, we expect the noise to decorrelate the signal response and produce suppressive masking (MTI <0), providing the signal response is sufficiently strong. We plot the MTI as a function of both the noise azimuth (Fig. 9A) and the noise relative IPD (Fig. 9B) for all the units in our sample. We show results only for favorable signal azimuths (|s| <0.1) to see the effect of the masker on a strong signal response. When the noise is in the ipsilateral hemifield (negative azimuths), the masking is usually suppressive (MTI near –1). However, noise in the contralateral hemifield (positive azimuths) can mask through either excitation or suppression. This dependency of MTI on noise azimuth may arise from the fact that most of the units have their best azimuths on the contralateral side. To test this possibility, Fig. 9B replots the MTI as a function of noise relative IPD n, thereby normalizing for differences in best azimuths across units (see METHODS). By definition, favorable azimuths have relative IPDs near 0, whereas unfavorable azimuths have relative IPDs near –0.5. Figure 9B shows that the MTI across the population changes abruptly around n = –0.25: when the noise is at an unfavorable azimuth (n < –0.25), the masking is always suppressive, as expected; however, when the relative IPD of the noise is favorable (n > –0.25), the masking can be either excitatory or suppressive, despite the fact that the signal and masker are both at favorable azimuths. It seems that the masker can reduce the overall rate even when it is placed at a "favorable" azimuth, contrary to the predictions of a simple cross-correlator model. Because the signal and the masker have similar spectra, effects such as lateral (cross-frequency) inhibition or cochlear suppression are not likely to explain this result. Instead, this result suggests that additional processing beyond cross-correlation, probably some type of temporal processing, affects the relative responses to the signal and the noise in some units. In the DISCUSSION, we propose a likely candidate for such additional processing.
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The second metric used to compare the neural responses to the predictions of the cross-correlator model is the "signal effect index" (SEI), which characterizes the effect of the signal on the noise response. The SEI is again a normalized difference, this time between the S + N rate, R(S + NMax), and the N rate, R(NMax), at the noise level NMax where the signal causes the largest change in rate
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Figure 9 shows the SEI as a function of both signal azimuth (Fig. 9C) and the signal relative IPD (Fig. 9D). Only results for favorable noise azimuths (|n| <0.1) are shown so that the effect of adding the signal can be reliably evaluated. When the signal is near the midline or in the contralateral hemifield (positive azimuths), it is detected through an increase in rate in most cases (108 out of 122 thresholds in Fig. 9C; many of the points are plotted on top of each other, especially near 1). The median SEI (solid line) is near 1 in these cases. In contrast, when the signal is placed at –90°, it is usually detected through a decrease in rate (11 out of 14 cases). The SEIs never reach –1, but are usually near –0.5, indicating that the signal does not completely suppress the noise response. In Fig. 9D, the SEI is replotted against the signal relative IPD to normalize for cross-unit differences in best ITD and CF. Placing the signal at a favorable azimuth (s > –0.25) almost always increases the overall rate (106 out of 122 thresholds), as expected, but occasionally decreases the rate. Signals at unfavorable azimuths (s < –0.25) decrease the overall rate, as expected, in a majority of cases (nine out of 14), but can also increase the rate in some cases. When combined with the MTI results, these results suggest that, whereas the cross-correlator model gives useful predictions for many units, some additional processing is affecting the relative rates to the signal and the masker in a substantial fraction of the units.
Best thresholds occur for signal at best azimuth
For a majority of the units in our sample, +90° is near the best ITD, and –90° is near the worst ITD. For such units, placing the stimuli at these azimuths makes the neural inputs arrive as near to in phase or as near to out of phase as possible inside the physiological range. Therefore placing the signal and noise at +90° and –90° in various combinations is analogous to the well-studied N0S0, N0S, and NS0 conditions for units having their best ITD near 0 (0-ITD units). In the modeling studies by Colburn (1973, 1977a,b), the 0-ITD units were the most sensitive to changes in interaural correlation in the traditional BMLD conditions (N0S0 compared with N0S). Specifically, the in-phase conditions (N0, S0) for the 0-ITD units are similar to placing the stimulus at the best azimuth in this study because the inputs from the two ears would arrive in phase at the coincidence detector. The out-of-phase conditions (N, S) for a 0-ITD unit are similar to placing the stimulus at the worst azimuth for our units because the inputs would arrive nearly out of phase. The psychophysical thresholds for the N0S condition are better than the NS0 for a wide variety of signals, and both thresholds are better than those for the N0S0 condition (Durlach and Colburn 1978). Using a 500-Hz pure-tone signal, Jiang et al. (1997) found a correlate of this threshold hierarchy in the average thresholds of IC units. Furthermore, as predicted by the Colburn model, they showed that for the majority of units, the N0S neural thresholds were better when adding the signal decreased the overall response, indicating that the best thresholds occur when the signal decorrelates the noise response. If these results could be extended to our experiments, one would expect the best thresholds to occur when the signal is placed at the worst azimuth, usually near –90°, and the noise is placed at the best azimuth, usually near 90°, so that the signal is detected by decorrelating the noise response.
Figure 10 shows the thresholds plotted against CF for 11 units in three animals for which we measured responses for the signal and the noise on opposite sides of the head. Each unit's response was tested with the signal near the best azimuth and the noise near the worst (S90, N –90, white squares) as well as the signal near the worst azimuth and the noise near the best (S–90, N90, black circles). The thresholds for the signal and masker co-located near the best azimuth at 90° (S90, N90, x's) are also shown for the same units. For all the units, the thresholds for the signal placed near the best azimuth, the condition most like NS0, are always at least as good as the thresholds for the signal placed near the worst azimuth, the condition most analogous to N0S (white squares are always lower than black circles). This relationship is the reverse of the one expected from previous psychophysical and physiological studies of BMLD with pure-tone signals. The S90, N–90 thresholds are always better than the co-located thresholds as expected from the BMLD psychophysical and physiological results (white squares are always lower than x's); however, the S–90, N90 thresholds, which might be expected to be the best thresholds overall, are not necessarily even as good as the co-located thresholds (x's are sometimes lower than black circles). It should be noted, however, that we have biased our results somewhat by selecting only neurons that gave a sustained response to the chirp at some azimuth; searching for units that showed a response to the signal by suppressing the noise response would be difficult at best. Nevertheless, in contrast to previous findings and model predictions, the best thresholds for these stimuli do not seem to occur when the signal decorrelates the noise response, but rather when the signal correlates the anticorrelated noise response.4
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,b) results. In contrast, at the same overall signal level, the SNR at threshold for the chirp train was better at +90° than at –90° (unfilled squares are lower than filled circles), consistent with the pattern seen in our units. However, because there are differences between the pure-tone and the chirp-train signals in the amount of signal energy that passes through the peripheral auditory filters, we varied the level of the chirp train to see whether the threshold hierarchy might depend on signal level. As the level of the chirp train was raised from 38 to 43 dB SPL, the threshold SNR for S90°, N–90° remained about the same (Fig. 11, white squares), but the threshold SNR for S–90°, N90° improved considerably (Fig. 11, black circles). At higher chirp levels, thresholds for the two configurations were similar. It seems that a low-level signal at –90° does not sufficiently suppress the noise response to be detected at any noise level, but the amount of suppression increases with signal level, so that the thresholds reach about the same values as when the signal is at 90° at higher levels. Thus although overall signal level can affect the threshold SNR, the fundamental result holds across levels: the chirp-train thresholds for the signal at favorable azimuths are always at least as good as thresholds at unfavorable ones. These differences in results for the tone and chirp train signals do not seem to be caused by differences in signal energy through the peripheral filters because we were unable to find a signal level where the S–90° thresholds were significantly better than the S90° thresholds. Instead, other differences in the spectral or temporal properties of the signals are likely to affect the threshold hierarchy for this unit. Mean-rate thresholds compared with synchronized-rate thresholds
To examine the role of spike timing in these unit responses, we evaluated the effects of using synchronized rate instead of mean rate to define both the individual unit thresholds and the population thresholds. The fourth row of Fig. 3 shows the synchronized rate for the S+N response as well as the N response as a function of noise level for our example unit. Because the synchronized rate is the mean rate multiplied by the vector strength at 40 Hz, it is equal to the mean rate (row 3) when the response is perfectly synchronized to the signal (all three conditions at low levels, column B at all levels). Otherwise, the synchronized rate is always less than the mean rate, an effect that can make the thresholds better or worse, as discussed in the following text. Row 5 in Fig. 3 shows the percentage of stimulus presentations for which the signal-plus-noise response is greater than the noise-alone response as a function of noise level for both the mean rate (dots) and the synchronized rate (x's). Threshold is the noise level where the signal can be detected for 75% of the stimulus presentations through either an increase or decrease in mean rate or synchronized rate (dotted 25 and 75% lines). The circles show the thresholds for each condition: the synchronized rate thresholds can be better than (column A), the same as (column B), or worse than (column C) the mean rate thresholds.
When both the signal and the noise are excitatory (Fig. 3, column A), the use of timing information can improve thresholds because introducing the signal causes the spike times to become synchronized to the signal repetition rate without a concomitant change in mean rate compared with the noise-alone condition. In contrast, when the noise suppresses the signal response (Fig. 3, column B), the percentage curves and the masked thresholds for the mean rate and synchronized rate are similar. Here, because every spike is phase locked to the signal envelope, spike timing provides no additional information over the rate. Finally, when the noise is excitatory and the signal is suppressive (Fig. 3, column C), the synchronized rate threshold is actually worse than the rate threshold because the signal acts to decrease the rate but increase the vector strength, reducing the overall change in synchronized rate when the signal is added to the noise. In the case of Fig. 3, column C, the consequence of reducing the difference was considerable: the percentage curve for the synchronized rate did not reach 25%, so that the 75% threshold had to be used. This 75% synchronized rate threshold was 18 dB worse than the 25% mean rate threshold.
Figure 12, A and B, shows both the synchronized-rate thresholds and the mean-rate thresholds as a function of noise azimuth for S+90° (A) and S–90° (B) for the unit in Fig. 3. Again, the synchronized-rate thresholds can be the same as, better than, or worse than the mean-rate thresholds, depending on whether the signal and the masker are excitatory or suppressive. However, at the signal and masker locations that give the best thresholds (those with the lowest SNR; in this case, S+90° and negative noise azimuths), the mean-rate and the synchronized-rate thresholds are essentially the same. This observation is typical for our sample of units because the best thresholds generally occur when the signal is excitatory and the masker is suppressive, the very case when there is little difference between the two threshold metrics.
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Consistent with the above results, Fig. 6 shows that the population thresholds based on the mean rate (top) and the synchronized rate (bottom) are very similar. Except for one spurious synchrony threshold for S45°, the shapes and SNR values for both thresholds are nearly identical. Again, this similarity arises because the best thresholds tend to occur when the noise suppresses the signal response; consequently, the additional information in the spike timing does not improve the population thresholds. Thus the population thresholds appear to be robust with respect to the choice of threshold metric for our highly modulated stimuli.
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DISCUSSION |
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; Colburn 1973, 1977a,b; Jiang et al. 1997a,b), we show that individual units' masked thresholds depend primarily on the unit's azimuth preference. Because of the variety of azimuth preferences in our population, the best thresholds in the population as a whole show neural correlates of spatial release from masking. Unit responses compared with predictions of the cross-correlator model
Although many of the neuron responses were qualitatively consistent with a cross-correlator model of ITD processing, many others appeared to be influenced by additional processing beyond cross-correlation. In general, the deviations from the predictions of the cross-correlator model were consistent with an excess responsiveness to the chirp signal compared with the noise masker. Specifically, the signal could be detected by an increase in rate over the noise-alone response, even when it was placed at an unfavorable azimuth where the cross-correlation model predicts fewer coincidences and therefore a lower rate (Fig. 9D). Similarly, the masking could be suppressive even when the noise was placed at a favorable azimuth where it would be expected to mask by creating an overabundance of coincidences (Fig. 9B). This difference in responsiveness between the signal and the noise must reflect some physical difference between the two stimuli. Although the two stimuli have similar broadband spectral envelopes, they differ in their temporal characteristics: the chirp signal has a pronounced 40-Hz amplitude modulation (AM), whereas the noise masker has a flat envelope (although some envelope modulation would be introduced by peripheral auditory filtering). One striking property of IC units is that many have band-pass, rate-based modulation transfer functions (rMTFs), i.e., they fire maximally for a particular modulation frequency (for a thorough review see Joris et al. 2004). This property of IC neurons contrasts with those in lower auditory nuclei, which tend to have flat rMTFs. This transformation is not restricted to high-frequency units: a recent study by Sterbing et al. (2003) shows that low-frequency ITD-sensitive units can have higher or lower rates depending on the modulation frequency of the stimulus envelope. Because the 40-Hz modulation frequency of the chirp train lies within the pass band of many IC units, the band-pass sensitivity of IC unit's rMTFs may account for their greater responsiveness to the modulated chirp signal compared with the noise.
Elsewhere (Lane 2003; Lane et al. 2003, 2004), we show that the IC units' sensitivities to temporal envelope can quantitatively account for the differences between the data and the predictions of the cross-correlator model. Briefly, we first implemented a cross-correlator model, but adjusted the responses to account for the nonmonotonic rate level functions seen in the ICC. We found that this model accounts for the noise-alone response quite well. However, when the (spectrally similar) signal is added, the response is not well predicted for many of the units, indicating that additional processing beyond cross-correlation is required. When processing that provides sensitivity to different modulations rates was added to the model, the model responses for both the noise-alone and the signal-plus-noise conditions matched the data well.
The departures from the traditional cross-correlation model of ITD processing are important because they may provide clues as to the functional role of the IC. Previous work shows that ITD-sensitive units in the IC do not necessarily act as the cross-correlator model predicts (e.g., Fitzpatrick et al. 2002; Joris 2003; Palmer et al. 1999); nevertheless, the literature tends to emphasize the consistency of these units' responses with cross-correlation predictions. One potential reason that evidence for additional processing is not more prevalent in the literature is the use of stimuli with flat envelopes and constant ITDs. Using stimuli with modulated envelopes is important because most natural sounds including animal vocalizations contain pronounced modulations (Singh and Theunissen 2003), but only a few studies (e.g., Sterbing et al. 2003) exist documenting interactions between ITD sensitivity and AM sensitivity. The importance of envelope modulations for ITD processing in the IC is consistent with evidence for the importance of temporal processing in the IC for human speech (Delgutte et al. 1996), musical sounds (McKinney et al. 2000), and bat echolocation sounds (Covey and Casseday 1999). One of the few well-documented departures from cross-correlation occurs in response to stimuli with time-varying ITD, and the processing involved appears to arise in the IC itself (McAlpine and Palmer 2002a; Spitzer and Semple 1998). Computational models (Borisyuk et al. 2002; Cai et al. 1998a,b) suggest that temporal processing, such as adaptation or interaction between excitation and inhibition, can explain the time-varying ITD results. As shown by Nelson and Carney (2004), similar processing may also give rise to sensitivity to AM.
Individual unit responses and thresholds
As expected from experimental and theoretical results for BMLD stimuli (Caird et al. 1991; Colburn 1973, 1997a,b; Jiang et al. 1997a,b), the worst individual thresholds tend to occur when the noise produces a strong excitatory response. Furthermore, the best thresholds tend to occur when the addition of the chirp signal increases the overall rate, in contrast to the results of Jiang et al. (1997a,b) for pure-tone signals. However, the two sets of results are not necessarily contradictory. When tested in one unit, we found the same threshold hierarchy as that of Jiang et al. (1997a,b) and Palmer et al. (2000) for a pure-tone signal, although when the chirp-train signal was used, the thresholds were again better when the signal was placed at a favorable azimuth. By measuring the thresholds for the chirp train at different signal levels, we showed that the differences in threshold hierarchy for the two signals do not seem to be caused by differences in the amount of energy passing through the peripheral filters, but more likely result from differences in the signals' temporal or spectral characteristics. Additional data comparing responses to pure-tone and broadband signals in noise are needed to fully resolve the issue.
Population thresholds
As expected from previous modeling and physiological studies of the BMLD (Caird et al. 1991; Colburn 1973, 1977a,b; Jiang et al. 1997a,b), the population of ITD-sensitive units shows a correlate of SRM even though the responses of individual units primarily reflect their azimuth preference. Because the units have a variety of preferred azimuths, the population thresholds generally improve with separation between signal and masker, and both the actual threshold values and the amounts of masking release are similar for the human behavioral data and the cat neural population data. Overall, the population of low-frequency, ITD-sensitive units in both ICs seems likely to provide a neural substrate for spatial release from masking, and these results suggest that spatial release from masking at low frequencies may be processed similarly across species.
In this study, we recorded from units in the left IC. Because these units tend to have the best thresholds when the signal is on the right (contralateral) side, our results suggest that the chirp signal is likely to be detected by the IC contralateral to the signal location. Presumably units in the opposite (right) IC would detect signals on the left hemifield, providing a mirror image of our population threshold curves. For a signal directly in front, both ICs would be able to detect the signal. These results suggest that a patient with a lesion in one IC would have difficulty detecting broadband signals in noise when the signal is on the side contralateral to the lesion. In contrast, the previous results with pure-tone signals (Jiang et al. 1997) predict that the patient's deficit for a pure-tone signal would occur when the signal is ipsilateral to the lesion.
Anesthesia
One difficulty with our study is that neurophysiological results from anesthetized cats are compared with human behavioral data. Barbiturate anesthesia alters the responses of units in the inferior colliculus, and its effects are often described as being attributed to increased GABAergic inhibition (e.g., Kuwada et al. 1989). Our anesthetic is a mixture of Dial (a barbiturate) and urethane, which acts on different receptor channels (Hara and Harris 2002), so the overall effects are difficult to predict. For example, inhibition is known to shape rate-level functions (Sivaramakrishnan et al. 2004), which could in turn affect how the signal-plus-noise response and the noise-alone response depend on level. The best thresholds occurred for units with monotonic rate-level functions (not shown), the most common type of neuron in awake preparations. As a result, the best thresholds and therefore the population response may be robust to the effects of anesthesia. Additionally, barbiturate anesthesia has been shown to affect the ITD sensitivity of individual units in the IC (Kuwada et al. 1989). However, the rate-ITD functions in the unanesthetized rabbit show the same general types of responses as those in the anesthetized cat. Now that we have studied these units intensively in an anesthetized preparation, future work could test, in a targeted way, whether the results shown here hold in awake preparations.
Species differences
Another potential issue that arises in comparing human behavioral thresholds with cat neural population thresholds is the differences between the two species. For one, the two species have different head sizes. To some extent, we took this factor into account by comparing thresholds for similar ITDs instead of similar azimuths. Previous studies (Hancock and Delgutte 2004; Joris et al. 2004; McAlpine et al. 2001) have shown that the distribution of best ITDs is similar in cats and in guinea pigs despite the difference in head size between the two species. If humans were similar to cats and guinea pigs in this respect, differences in head size might not pose a serious problem to our results. Additionally, Shera et al. (2002) suggest that human cochlear filters may be more than twice as sharp as those of cats. Sharper frequency tuning is expected to make the random envelope modulations for the noise more pronounced, possibly reducing the difference in responsiveness we observed between the signal and the noise. Models, such as the one developed in Lane (2003), could be used to address the effects of the species differences.
Neural metrics for signal detection
In an effort to evaluate the possible contribution of spike timing to detection thresholds, we compared masked thresholds based on mean discharge rate versus synchronized rate. The synchronized-rate thresholds of individual units could be lower than, the same as, or higher than the corresponding mean-rate thresholds. It is possible that different methods of incorporating spike timing, such as comparing the responses to a signal template, might change the results for individual unit thresholds. For example, at a higher signal level, detection methods based on spike timing may become essential because the rate responses may be saturated in a majority of units. On the other hand, in the case when the excitatory signal response is masked by suppression, the individual thresholds are unlikely to change regardless of the threshold metric used as long as every spike is phase locked to the stimulus envelope. This observation is especially important because these thresholds are usually the most sensitive and would likely remain so regardless of the threshold metric. Consequently, the population thresholds are robust to the choice of detection metric because the best thresholds across the population tend to occur when the noise suppresses the phase-locked signal response.
Population thresholds and pooling of information across neurons
We used the most sensitive unit in our population as our estimate of the population threshold, following the "lower envelope principle" reviewed by Parker and Newsome (1998). The most sensitive neurons in a neural population often predict psychophysical performance in sensory neurophysiology; examples include Mountcastle et al. (1972) in somatosensation, Bradley et al. (1987) in vision, and Delgutte (1990) in audition. An alternative to the lower envelope principle is to assume that the central processor can pool the individual neuron responses across the population. Provided the information is processed in a meaningful way, the performance based on pooled responses must be at least as good as the lower envelope performance because the central processor has access to the most sensitive neuron as well as all the others. In some cases, the performance improvements achieved by optimal pooling can be quite significant: for example, the pooled performance based on auditory nerve fiber responses for intensity discrimination predicts the "near miss" to Weber's law for pure tones better than the lower envelope performance (for a review see Delgutte 1996). Nevertheless, pooling the unit responses was not required in this study to achieve thresholds comparable in both magnitude and shape to human behavioral thresholds, indicating that, if pooling occurs, it is not necessary to improve detection.
The population thresholds for the BMLD conditions with pure-tone stimuli (Jiang et al. 1997a; Palmer et al. 2000) were reported as averages across the population of units, rather than according to the lower envelope principle. Nevertheless, consistent with our results, only a few neurons responded to the tone signal at low signal levels, corroborating the idea that a few neurons dictate performance at threshold. Results from other studies of ITD-sensitive neurons in the IC indicate that understanding how the information is combined across neurons is not straightforward. Consistent with the lower envelope hypothesis, Shackleton et al. (2003) showed that human ITD acuity for broadband noise near the midline can be accounted for by the performance of the most sensitive neurons in the guinea pig IC. However, Shackleton and Palmer (2004) showed that changes in interaural correlation, a task closely related to ITD discrimination as well as the SRM and BMLD, do not seem to be predicted by individual unit thresholds. Additionally, Hancock and Delgutte (2004) found that pooling responses across IC neurons with different CFs seems necessary to account for ITD acuity away from the midline. To further address this issue, computational studies, such as the one in Lane (2003), allow us to quantitatively assess which neurons determine performance in the SRM task and how performance may be selectively affected by removal of some of these neurons.
In summary, we have shown that the masked thresholds of individual ITD-sensitive units in the cat inferior colliculus are sensitive to the positions of the signal and masker in space. Both the manner in which the noise masks the signal response (suppressive or excitatory masking) and the way that the signal is detected (through an increase or decrease in rate) change systematically with signal and masker azimuth, as predicted by a cross-correlator model. However, additional processing beyond cross-correlation appears to influence the units' response, perhaps because of sensitivity to the stimulus temporal envelope. Individual unit threshold curves depend on the locations of the signal and masker relative to the neurons' preferred azimuths. Because of the variety of azimuth preferences in the population, the neural population threshold, which is based on the most sensitive unit at each signal and masker configuration, shows a correlate of spatial release from masking. The population threshold curves were similar in both shape and magnitude to human behavioral thresholds, indicating that ITD-sensitive IC units may provide a neural substrate for low-frequency human spatial release from masking. These neurophysiological results, combined with computational studies described elsewhere (Lane 2003; Lane et al. 2003, 2004), take us several steps closer to a quantitative understanding of the neural mechanisms of spatial release from masking and of listening in complex environments in general.
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ACKNOWLEDGMENTS |
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Present address of C. C. Lane: Rice University, Electrical and Computer Engineering Dept., MS 380, PO Box 1892, Houston, TX 77251–1892. E-mail: court@rice.edu.
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FOOTNOTES |
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1 In some experiments, for the noise-delay function, the noise burst was shorter in the left ear (20 ms instead of 200 ms) than in the right ear because of a programming error. In this case, the rate window for the ITD-sensitivity analysis was adjusted to match the length of the shorter noise burst. A Gabor function could still be fit for all but one unit, which was discarded from the database. 
2 This somewhat unusual technique was selected because the bootstrap threshold distributions were sometimes bimodal, making their median an unreliable measure of central tendency; in contrast, the percentage-correct curves had well-behaved, unimodal distributions.
3 To obtain the noise-alone response to changing ITD only, the noise waveform was filtered through the first principle component of the HRTFs (essentially a version of the original HRTFs that has been smoothed in the frequency domain) to remove ILD and spectral cues, and the ITD was varied. At each azimuth, the ITD was adjusted to match the delay yielding the maximum of the interaural cross-correlation of the smoothed HRTFs for each azimuth (see Litovsky and Delgutte 2002 for details).
4 To test directly whether thresholds improve when the signal is detected by a decrease in rate, as in Jiang et al (1997a,b), we looked at the thresholds for units where the signal was detected by a decrease in rate (showed a negative SEI) at some signal azimuths and by an increase in rate (positive SEI) at others (11 units, not all the same as in Fig. 10). Because we measured thresholds for many stimulus conditions, each of these conditions showed a range of thresholds. We therefore compared the best threshold for a given unit when the signal increased the rate to the best threshold for that unit when the signal decreased the rate. In contrast to the results of Jiang et al. (1997a,b), for all the units, the best threshold when the signal increased the rate was at least as good as the best threshold when the signal decreased the rate (not shown).
Address for reprint requests and other correspondence: C. C. Lane, ECE Department, Rice University, MS 380, P.O. Box 1892, Houston, TX 77251-1892 (E-mail: court{at}rice.edu)
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REFERENCES |
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Batra R, Kuwada S, and Fitzpatrick DC. Sensitivity to interaural temporal disparities of low- and high-frequency neurons in the superior olivary complex. II. Coincidence detection. J Neurophysiol 78: 1237–1247, 1997.
Batra R and Yin TC. Cross correlation by neurons of the medial superior olive: a reexamination. J Assoc Res Otolaryngol 5: 238–252, 2004.[CrossRef][Web of Science][Medline]
Borisyuk A, Semple MN, and Rinzel J. Adaptation and inhibition underlie responses to time-varying interaural phase cues in a model of inferior colliculus neurons. J Neurophysiol 88: 2134–2146, 2002.
Bradley A, Skottun BC, Ohzawa I, Sclar G, and Freeman RD. Visual orientation and spatial frequency discrimination: a comparison of single neurons and behavior. J Neurophysiol 57: 755–772, 1987.
Brown TJ. Characterization of Acoustic Head-Related Transfer Functions for Nearby Sources (MS thesis). Cambridge, MA: Massachusetts Institute of Technology, 2000.
Cai H, Carney LH, and Colburn HS. A model for binaural response properties of inferior colliculus neurons. I. A model with interaural time difference-sensitive excitatory and inhibitory inputs. J Acoust Soc Am 103: 475–493, 1998a.[CrossRef][Web of Science][Medline]
Cai H, Carney LH, and Colburn HS. A model for binaural response properties of inferior colliculus neurons. II. A model with interaural time difference-sensitive excitatory and inhibitory inputs and an adaptation mechanism. J Acoust Soc Am 103: 494–506, 1998b.[CrossRef][Web of Science][Medline]
Caird DM, Palmer AR, and Rees A. Binaural masking level difference effects in single units of the guinea pig inferior colliculus. Hear Res 57: 91–106, 1991.[CrossRef][Web of Science][Medline]
Chan JC, Yin TC, and Musicant AD. Effects of interaural time delays of noise stimuli on low-frequency cells in the cat's inferior colliculus. II. Responses to band-pass filtered noises. J Neurophysiol 58: 543–561, 1987.
Colburn HS. Theory of binaural interaction based on auditory-nerve data. I. General strategy and preliminary results on interaural discrimination. J Acoust Soc Am 54: 1458–1470, 1973.[CrossRef][Web of Science][Medline]
Colburn HS. Theory of binaural interaction based on auditory-nerve data. II. Detection of tones in noise. J Acoust Soc Am 61: 525–533, 1977a.[CrossRef][Web of Science][Medline]
Colburn HS. Theory of binaural interaction based on auditory-nerve data. II. Detection of tones in noise (Supplementary Material). AIP document no. PAPS and JASMA-610525-98, 1977b.
Colburn HS. Computational models of binaural processing. In: Auditory Computation, edited by Hawkins HL, McMullen TA, Popper AN, and Fay RR. New York: Springer-Verlag, 1996.
Colburn HS and Durlach NI.Models of binaural interaction. In: Handbook of Perception, edited by Carterette EC and Friedman MP. New York: Academic Press, 1978.
Covey E and Casseday JH. Timing in the auditory system of the bat. Annu Rev Physiol 61: 457–476, 1999.[CrossRef][Web of Science][Medline]
Delgutte B. Physiological mechanisms of psychophysical masking: observations from auditory-nerve fibers. J Acoust Soc Am 87: 791–809, 1990.[CrossRef][Web of Science][Medline]
Delgutte B. Physiological models for basic auditory percepts. In: Auditory Computation, edited by Hawkins HL, McMullen TA, Popper AN, and Fay RR. New York: Springer-Verlag, 1996.
Delgutte B, Hammond BM, and Cariani PA. Neural coding of the temporal envelope of speech: relation to modulation transfer functions. In: Psychophysical and Physiological Advances in Hearing, edited by Palmer AR, Reese A, Summerfield AQ, and Meddis R. London: Whurr, 1998.
Durlach NI and Colburn HS. Binaural phenomena. In: Handbook of Perception, edited by Carterette EC and Friedman MP. New York: Academic Press, 1978.
Efron B and Tibshirani RJ. An Introduction to the Bootstrap. New York: Chapman & Hall, 1993.
Fitzpatrick DC, Kuwada S, and Batra R. Transformations in processing interaural time differences between the superior olivary complex and inferior colliculus: beyond the Jeffress model. Hear Res 168: 79–89, 2002.[CrossRef][Web of Science][Medline]
Gilkey RH and Good MD. Effects of frequency on free-field masking. Hum Factors 37: 835–843, 1995.[Medline]
Goldberg JM and Brown PB. Response of binaural neurons of dog superior olivary complex to dichotic tonal stimuli: some physiological mechanisms of sound localization. J Neurophysiol 32: 613–636, 1969.
Good MD, Gilkey RH, and Ball JM. The relation between detection in noise and localization in noise in the free field. In: Binaural and Spatial Hearing in Real and Virtual Environments, edited by Gilkey RH and Anderson TR. Mahwah, NJ: Erlbaum, 1997.
Green DM and Swets JA. Signal Detection Theory and Psychophysics. New York: Krieger, 1974.
Hancock KE and Delgutte B. A physiologically based model of interaural time difference discrimination. J Neurosci 24: 7110–7117, 2004.
Hara K and Harris RA. The anesthetic mechanism of urethane: the effects on neurotransmitter-gated ion channels. Anesth Analg 94: 313–318, 2002.
Irvine DRF. Physiology of the auditory brainstem. In: The Mammalian Auditory Pathway: Neurophysiology, edited by Popper AN and Fay RR. New York: Springer-Verlag, 1992.
Jeffress L. A place theory of sound localization. J Comp Physiol Psych 41: 35–39, 1948.[Medline]
Jiang D, McAlpine D, and Palmer AR. Detectability index measures of binaural masking level difference across populations of inferior colliculus neurons. J Neurosci 17: 9331–9339, 1997a.
Jiang D, McAlpine D, and Palmer AR. Responses of neurons in the inferior colliculus to binaural masking level difference stimuli measured by rate-versus-level functions. J Neurophysiol 77: 3085–3106, 1997b.
Joris PX. Interaural time sensitivity dominated by cochlea-induced envelope patterns. J Neurosci 23: 6345–6350, 2003.
Joris PX, Heijden M, Louage D, van de Sande B, and van Kerckhoven C. Dependence of binaural and cochlear "best delays" on characteristic frequency. In: Auditory Signal Processing: Physiology, Psychoacoustics, and Models, edited by Pressnitzer D, de Cheveigne A, McAdams S, and Collet L. New York: Springer-Verlag, 2004.
Kiang NY and Moxon EC. Tails of tuning curves of auditory-nerve fibers. J Acoust Soc Am 55: 620–630, 1974.[CrossRef][Web of Science][Medline]
Kim DO and Molnar CE. A population study of cochlear nerve fibers: comparison of spatial distributions of average-rate and phase-locking measures of responses to single tones. J Neurophysiol 42: 16–30, 1979.
Kopco N, Lane CC, and Shinn-Cunningham BG. Spatial unmasking of chirp trains in a simulated acoustic environment: behavioral results and model predictions. Assoc Res Otolaryngol Abstr 541, 2003.
Krishna BS and Semple MN. Auditory temporal processing: responses to sinusoidally amplitude-modulated tones in the inferior colliculus. J Neurophysiol 84: 255–273, 2000.
Kuwada S, Batra R, and Stanford TR. Monaural and binaural response properties of neurons in the inferior colliculus of the rabbit: effects of sodium pentobarbital. J Neurophysiol 61: 269–282, 1989.
Kuwada S and Yin TC. Binaural interaction in low-frequency neurons in inferior colliculus of the cat. I. Effects of long interaural delays, intensity, and repetition rate on interaural delay function. J Neurophysiol 50: 981–999, 1983.
Lane CC. Signal Detection in the Auditory Midbrain: Neural Correlates and Mechanisms of Spatial Release from Masking (PhD dissertation). Cambridge, MA: Harvard–MIT Division of Health Sciences and Technology, 2003.
Lane CC, Delgutte B, and Colburn HS. A population of ITD sensitive units in the cat inferior colliculus shows correlates of spatial release from masking. Assoc Res Otolaryngol Abstr 960, 2003.
Lane CC, Kopco N, Delgutte B, Shinn-Cunningham BG, and Colburn HS. A cat's cocktail party: psychophysical, neurophysiological and computational studies of spatial release from masking. In: Auditory Signal Processing: Physiology, Psychoacoustics, and Models, edited by Pressnitzer D, de Cheveigne A, McAdams S, and Collet L. New York: Springer-Verlag, 2004.
Langner G and Schreiner CE. Periodicity coding in the inferior colliculus of the cat. I. Neuronal mechanisms. J Neurophysiol 60: 1799–1822, 1988.
Litovsky RY and Delgutte B. Neural correlates of the precedence effect in the inferior colliculus: effect of localization cues. J Neurophysiol 87: 976–994, 2002.
Litovsky RY, Fligor BJ, and Tramo MJ. Functional role of the human inferior colliculus in binaural hearing. Hear Res 165: 177–188, 2002.[CrossRef][Web of Science][Medline]
Litovsky RY, Lane CC, Atencio CA, and Delgutte B. Physiological measures of the precedence effect and spatial release from masking in the cat inferior colliculus. In: Physiological and Psychophysical Bases of Auditory Function, edited by Breebart DJ, Houtsma AJM, Kohlrausch A, Prijs VF, and Schoonhoven R. Maastricht, The Netherlands: Shaker Publishing BV, 2000.
Loftus WC, Bishop DC, Saint Marie RL, and Oliver DL. Organization of binaural excitatory and inhibitory inputs to the inferior colliculus from the superior olive. J Comp Neurol 472: 330–344, 2004.[CrossRef][Web of Science][Medline]
McAlpine D, Jiang D, and Palmer AR. Binaural masking level differences in the inferior colliculus of the guinea pig. J Acoust Soc Am 100: 490–503, 1996.[CrossRef][Web of Science][Medline]
McAlpine D, Jiang D, and Palmer AR. A neural code for low-frequency sound localization in mammals. Nat Neurosci 4: 396–401, 2001.[CrossRef][Web of Science][Medline]
McAlpine D and Palmer AR. Blocking GABAergic inhibition increases sensitivity to sound motion cues in the inferior colliculus. J Neurosci 22: 1443–1453, 2002a.
McAlpine D and Palmer AR. Binaural bandwidths of inferior colliculus neurones measured using interaurally-delayed noise. Assoc Res Otolaryngol Abstr 155, 2002b.
McKinney MF, Tramo MJ, and Delgutte B. Neural correlates of the dissonance of musical intervals in the inferior colliculus. In: Physiological and Psychophysical Bases of Auditory Function, edited by Breebart DJ, Houtsma AJM, Kohlrausch A, Prijs VF, and Schoonhoven R. Maastricht, The Netherlands: Shaker Publishing BV, 2000.
Merzenich MM and Reid MD. Representation of the cochlea within the inferior colliculus of the cat. Brain Res 77: 397–415, 1974.[CrossRef][Web of Science][Medline]
Morse PM and Ingard KU. Theoretical Acoustics. New York: McGraw-Hill, 1968.
Mountcastle VB, LaMotte RH, and Carli G. Detection thresholds for stimuli in humans and monkeys: comparison with threshold events in mechanoreceptive afferent nerve fibers innervating the monkey hand. J Neurophysiol 35: 122–136, 1972.
Musicant AD, Chan JC, and Hind JE. Direction-dependent spectral properties of cat external ear: new data and cross-species comparisons. J Acoust Soc Am 87: 757–781, 1990.[CrossRef][Web of Science][Medline]
Nelson PC and Carney LH. A phenomenological model of peripheral and central neural responses to amplitude-modulated tones. J Acoust Soc Am 116: 2173–2186, 2004.[CrossRef][Web of Science][Medline]
Oliver DL, Beckius GE, Bishop DC, and Kuwada S. Simultaneous anterograde labeling of axonal layers from lateral superior olive and dorsal cochlear nucleus in the inferior colliculus of cat. J Comp Neurol 382: 215–229, 1997.[CrossRef][Web of Science][Medline]
Oliver DL and Morest DK. The central nucleus of the inferior colliculus in the cat. J Comp Neurol 222: 237–264, 1984.[CrossRef][Web of Science][Medline]
Palmer AR, Jiang D, and McAlpine D. Desynchronizing responses to correlated noise: a mechanism for binaural masking level differences at the inferior colliculus. J Neurophysiol 81: 722–734, 1999.
Palmer AR, Jiang D, and McAlpine D. Neural responses in the inferior colliculus to binaural masking level differences created by inverting the noise in one ear. J Neurophysiol 84: 844–852, 2000.
Parker AJ and Newsome WT. Sense and the single neuron: probing the physiology of perception. Annu Rev Neurosci 21: 227–277, 1998.[CrossRef][Web of Science][Medline]
Rosowski JJ, Carney LH, and Peake WT. The radiation impedance of the external ear of cat: measurements and applications. J Acoust Soc Am 84: 1695–1708, 1988.[CrossRef][Web of Science][Medline]
Saberi K, Dostal L, Sadralodabai T, Bull V, and Perrott DR. Free-field release from masking. J Acoust Soc Am 90: 1355–1370, 1991.[CrossRef][Web of Science][Medline]
Shackleton TM, Skottun BC, Arnott RH, and Palmer AR. Interaural time difference discrimination thresholds for single neurons in the inferior colliculus of guinea pigs. J Neurosci 23: 716–724, 2003.
Shackleton TR and Palmer DM. Sensitivity to changes in interaural time difference and interaural correlation in the inferior colliculus. In: Auditory Signal Processing: Physiology, Psychoacoustics, and Models, edited by Pressnitzer D, de Cheveigne A, McAdams S, and Collet L. New York: Springer-Verlag, 2004.
Shera CA, Guinan JJ Jr, and Oxenham AJ. Revised estimates of human cochlear tuning from otoacoustic and behavioral measurements. Proc Natl Acad Sci USA 99: 3318–3323, 2002.
Singh NC and Theunissen FE. Modulation spectra of natural sounds and ethological theories of auditory processing. J Acoust Soc Am 114: 3394–3411, 2003.[CrossRef][Web of Science][Medline]
Sivaramakrishnan S, Sterbing-D'Angelo SJ, Filipovic B, D'Angelo WR, Oliver DL, and Kuwada S. GABA(A) synapses shape neuronal responses to sound intensity in the inferior colliculus. J Neurosci 24: 5031–5043, 2004.
Spitzer MW and Semple MN. Responses of inferior colliculus neurons to time-varying interaural phase disparity: effects of shifting the locus of virtual motion. J Neurophysiol 69: 1245–1263, 1993.
Spitzer MW and Semple MN. Transformation of binaural response properties in the ascending auditory pathway: influence of time-varying interaural phase disparity. J Neurophysiol 80: 3062–3076, 1998.
Sterbing SJ, D'Angelo WR, Ostapoff EM, and Kuwada S. Effects of amplitude modulation on the coding of interaural time differences of low-frequency sounds in the inferior colliculus. I. Response properties. J Neurophysiol 64: 2827–2836, 2003.
Yang L, Pollak GD, and Resler C. GABAergic circuits sharpen tuning curves and modify response properties in the mustache bat inferior colliculus. J Neurophysiol 68: 1760–1774, 1992.
Yin TC and Chan JC. Interaural time sensitivity in medial superior olive of cat. J Neurophysiol 64: 465–488, 1990.
Yin TC and Kuwada S. Binaural interaction in low-frequency neurons in inferior colliculus of the cat. III. Effects of changing frequency. J Neurophysiol 50: 1020–1042, 1983.
Zhang X, Heinz MG, Bruce IC, and Carney LH. A phenomenological model for the responses of auditory-nerve fibers: I. Nonlinear tuning with compression and suppression. J Acoust Soc Am 109: 648–670, 2001.[CrossRef][Web of Science][Medline]
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Hz, about 45 dB SPL over entire bandwidth). For each masker azimuth, the masker level was adjusted using a 3-down, 1-up procedure to estimate the signal-to-noise ratio (SNR) yielding 79.4% correct detection performance. A: signal at 0°. Thin solid line shows the thresholds reflected about 0°, essentially completing the curve assuming that the listener has symmetric thresholds. Individual unit thresholds were also reflected about 0° to simulate the effect of having units from both ICs, and the population response was recomputed from the original and reflected data, making the response symmetric and different from 
