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For each of the three owls used in this experiment, miniature microphones were used to digitally record the waveform evoked in each ear by an external speaker emitting broadband noise (sampling rate, 30 kHz). As described previously, HRTFs were computed from these waveforms, bandpass filtered between 2000 and 11,000 Hz, and stored as 255-point finite impulse response filters (Keller et al., 1998
The Journal of Neuroscience, January 1, 2002, 22(1):284-293
From Spectrum to Space: The Contribution of Level Difference Cues to Spatial Receptive Fields in the Barn Owl Inferior Colliculus
David R. Euston and Terry T. Takahashi Institute of Neuroscience, University of Oregon, Eugene, Oregon 97403
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
Space-specific neurons in the owl's inferior colliculus have spatial receptive fields (RFs) computed from interaural time (ITD) and level (ILD) differences. Because of the shape of the owl's head, these cues vary with frequency in a manner specific for each location. We sought to determine the contribution of ILD to spatial selectivity. We measured the normal spatial receptive fields of space-specific neurons using virtual sound sources (i.e., noises filtered to simulate external sound sources, presented using headphones). The virtual-source filters were then altered so that ITD was fixed while frequency-specific ILDs varied according to location in the usual manner. The resulting "ILD-alone" RF typically revealed a horizontal band of excitation that included the normal RF. Above and below, the neurons were inhibited. Interestingly, the maxima of ILD-alone RFs were generally outside the normal RF, suggesting that space-specific neurons are not optimally tuned to the ILD spectrum occurring at the normal RF location. Congruously, frequency-specific ILD tuning, assessed with tones, better matched the ILDs at the peak of the ILD-alone RF than those at the peak of the normal RF. The firing evoked from the normal RF may thus reflect the balance of excitatory and inhibitory inputs needed to appropriately restrict the receptive field.
Frequency-specific ILD tuning curves were combined with measured head-filtering characteristics to predict responses to the frequency-specific ILDs at each location. The predicted ILD-alone RFs, which are based on a simple sum of frequency-specific inputs, accounted for 56% of the variance in our measured ILD-alone RFs.
Key words: sound localization; binaural; spectral integration; interaural intensity difference; head-related transfer function; virtual auditory space
INTRODUCTION
The barn owl (Tyto alba) is a nocturnal predator specialized for sound localization. Its inferior colliculus (IC) contains auditory cells with tightly restricted spatial receptive fields (RFs), topographically arranged into a map (Knudsen and Konishi, 1978b
). Despite such specializations, the owl uses the same binaural cues that humans and other mammals rely on, namely, interaural time difference (ITD) and interaural level difference (ILD). In both owls and mammals, the auditory periphery also fractionates sounds into frequency bands. Consequently, the spatial selectivity observed in the owl's IC depends crucially on the integration of information across frequency (Wagner et al., 1987
The filtering characteristics of the ears and face, called the head-related transfer functions (HRTFs), generate a unique combination of frequency-specific ILD and ITD values for each location in space (Moiseff, 1989a
Whereas ITD processing has been well investigated, the contribution of ILD spectra to the spatial selectivity of an IC neuron is less well understood. Brainard et al. (1992)
The ILD selectivity manifest in our measured ILD-alone RFs derives from a frequency integration process (Brainard et al., 1992
We also examined the hypothesis that the frequency-specific ILD tuning of a space-specific neuron should match the ILD values occurring at the center its spatial RF (Knudsen, 1999
MATERIALS AND METHODS
Stimulus presentation in virtual auditory space
Stimuli were presented in virtual auditory space using a system developed for the owl in our laboratory. Details of the measurement and use of HRTFs required for this process have been given previously (Keller et al., 1998n and +n, in which n was a magnitude value appropriate for our stimulus presentation hardware. Noises were bandpass filtered between 2 and 11 kHz before any other filtering and deviated <1 dB from the mean amplitude across this frequency range.
Neurophysiology
All procedures were approved by the Institutional Animal Care and Use Committee of the University of Oregon. Our data are based on single-cell recordings from the IC of three owls. Techniques for neural recording were similar to those used previously in our laboratory and reported previously (Takahashi and Keller, 1992; Fredrick Haer Co., Bowdoinham, ME). Action potentials were recorded using the DataWave Technologies (Longmont, CO) Discovery data acquisition package, which stored the time of occurrence of each spike, as well as a 32-point representation of the waveform. The waveforms were sorted off-line according to shape using a commercial software product (Autocut; DataWave Technologies), allowing us to verify that all data came from the same cell over the course of several tests, and, in a few cases, allowing us to extract data from more than one cell at a single recording site. Space-specific neurons were selected based on the compactness of their spatial RFs. To quantify compactness, we developed a metric which we refer to as the "spatialization index:" for each measured RF, all locations that elicited spike rates below spontaneous rate were set to zero, and the entire set of responses was then scaled between zero and one. We then determined the location that elicited maximum firing (i.e., the RF peak). The spatialization index was defined as the sum of the responses at each location times the angular separation of that location from the peak. Angles were measured in double-polar coordinates (see above), and responses were taken every 5° in azimuth and elevation over the entire frontal hemifield. As illustrated in Figure 1A, this quantity was inversely correlated with the spatial specificity of a cell and accorded well with more traditional metrics for selecting space-specific neurons, including degree of ITD side-peak suppression (Takahashi and Konishi, 1986
The locations of recording sites were verified histologically, using an electrode coated with DiI (Molecular Probes, Eugene, OR) to provide a reference mark. The electrode was prepared by applying several coats of a saturated solution of DiI dissolved in ethyl alcohol. Only a single DiI penetration was made for each side of the brain, and all other penetrations were determined relative to the reference mark. Penetrations for recording sites were clustered in the external nucleus of the IC (ICx) and on the boundary between the ICx and its medial neighbor, the lateral shell of the central inferior collicular nucleus (ICc-ls). For every cell encountered, a series of three or four tests were run. We first measured the normal and ILD-alone spatial RFs of the cell, using evenly spaced (10° resolution in azimuth and elevation) virtual stimuli presented from 360 locations in the frontal hemisphere. (For the purposes of display, RFs were converted to 5° spacing using linear interpolation.) The term "normal" is used to denote an RF measured with ILD, ITD, and ABL varying as they do under free-field conditions. This differentiates it from an ILD-alone RF, in which only ILD varies. ITD tuning curves were also measured, using a broadband noise. ITDs typically covered the range of
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Figure 1. Spatialization indices used in screening cells. A, Examples of normal RFs from three cells and their associated spatialization index values (for definition, see Materials and Methods). Scale bar indicates firing rate scaled between 0 and 1. The diamond-shaped plots represent loci in the frontal hemisphere, from the owl's perspective, expressed in double-polar coordinates (Knudsen, 1982
Data analyses
Prediction of ILD-alone receptive fields. The ILD-frequency response surfaces, which demonstrate the tuning of a cell for ILD at different frequencies, were transformed into the spatial domain by combining responses linearly according to the HRTFs. The resulting "predicted ILD-alone RFs" could be compared directly with the measured ILD-alone RFs that were obtained with broadband noise. An overview of this process is presented here and is illustrated in Figure 2. Additional details are presented in the next paragraph. Figure 2A shows the ILD spectrum from a particular location (
Before summing, the data were conditioned as follows. ILD-frequency surfaces were transformed so that the frequency axis values matched those of the HRTFs (14.65 Hz spacing) and the ILD axis had spacing of 0.5 dB. Linear interpolation was used to generate the missing spike-rate values. The HRTF-derived ILD spectra associated with each location were also rounded to the nearest 0.5 dB. In addition, the spontaneous rate of the cell was subtracted from the ILD-frequency response surface, thus allowing for negative activity levels in both the ILD-frequency response surface and the predicted ILD-alone RF. For each location, these manipulations yielded an ILD spectrum that corresponded to a string of values on the ILD-frequency response surface. As described above (and in Fig. 2), the predicted response at a particular location was the average of this string of values. Although it is more consistent with known physiological mechanisms to assume that a linear neuron sums rather than averages the contribution of frequency-tuned inputs, we averaged the response across frequency because the range of frequencies tested varied from cell to cell. For example, a cell whose ILD-frequency surface was measured between 4000 and 8000 Hz would have a total of 273 values along the frequency axis after interpolation, given the 14.6 Hz frequency spacing dictated by the HRTFs (see above). A different cell might have been tested at frequencies between 3000 and 10,000 Hz and would have 477 values along the frequency axis. Averaging therefore ensured that the predicting firing rates are comparable across cells. One of the important controls in this experiment was that the same ILD spectra were used to measure ILD-alone RFs and to transform pure-tone tuning into a predicted ILD-alone RF. This eliminates the possibility that errors in the measurement of the HRTFs could account for any discrepancies between predicted and measured ILD-alone RFs. The match between predicted and measured ILD-alone RFs was quantified by a correlation analysis. It should be noted that this analysis is insensitive to differences in overall responsiveness that may exist between the neuron and a linear predictive model. For instance, if the predicted responses were exactly twice as large as measured responses at all locations, the correlation coefficient would still be 1. Therefore, this method is sensitive only to differences in the shapes of the measured and predicted ILD-alone RFs. ILD curves. Pure-tone ILD tuning curves amount to cross-sections of ILD-frequency surfaces perpendicular to the frequency axis. We determined the best ILD for each such curve (i.e., the ILD that elicited maximum response), using cubic splines to interpolate missing data. Our interpolated curves covered ILD values ranging from
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Figure 2. Transformation of ILD-frequency response surfaces into predicted ILD-alone receptive fields. A, ILD spectrum for a single location (
RESULTS
Measured ILD-alone receptive fields
For 34 of the 47 cells in our sample, we measured both normal spatial RFs (i.e., with ITD, ABL, and ILD varying as they do with real external sound sources) and ILD-alone RFs (i.e., with ITD and ABL held fixed). The normal spatial RFs of six representative cells are shown in column 1 of Figure 3. These RFs, measured with noise sources in virtual auditory space, were similar in shape and size to those observed using free-field stimuli (Knudsen and Konishi, 1978a
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Figure 3. Contribution of ILD to spatial RFs. Rows A-F show the data from six single cells. The diamond-shaped plots represent loci in the frontal hemisphere, from the owl's perspective, expressed in double-polar coordinates (Knudsen, 1982
ILD-alone RFs are provided for each of our six representative cells in column 2 of Figure 3. Whereas normal spatial RFs were tightly restricted in both elevation and azimuth, ILD-alone RFs were amorphous and typically showed islands of response separate from the main excitatory region (Fig. 3F, column 2). Compared with normal RFs, ILD-alone RFs showed much greater firing at peripheral locations. This effect is attributable in part to ABL equalization. Under free-field conditions, the frequency-specific ABL at the eardrum falls off by as much as 40 dB as a stimulus of a constant amplitude is moved from the region directly in front of the bird toward the periphery (Keller et al., 1998
The ILD-alone RFs consistently showed a prominent horizontal band of activity that intersected the normal spatial RF. Based on the known role of ITD in restricting RFs in azimuth (Moiseff and Konishi, 1981
Although ILD generally appears to restrict firing in elevation, in some cases, it may also provide some degree of azimuthal restriction. For example, the ILD-alone RF shown in Figure 3C clearly shows that ILD limits firing for locations to the left of the normal RF.
The horizontal band seen in the ILD-alone RFs was usually flanked above and/or below by regions of minimal response. In 28 of 34 cells that had significant spontaneous firing rates, 24 had minimal responses significantly below spontaneous rate (p < 0.05). This evidence of active inhibition is remarkable in light of the fact that ITD was set to the preferred value of a cell at all locations. Nonoptimal ILD spectra are thus sufficient to completely inhibit a cell, even when that cell is driven at its best ITD.
For the majority of cells, the peak of the ILD-alone and normal RFs were not the same (Fig. 3D). This effect cannot be accounted for by the fact that ABL was equalized in ILD-alone tests. We estimated the reduction in firing that would have been seen in the ILD-alone RF had ABL varied as it does in normal space tests. This compensation was possible because we knew both the typical ABL response curve of our cells and how much ABL needed to be reduced at each location in the ILD-alone test. Even after this adjustment, the separation between maxima of ILD-alone and normal spatial RFs differed by as much as 53° (mean separation ± SD, 16 ± 12°).
ILD tuning characteristics of space-tuned IC cells
We measured the responses of our 47 space-specific IC cells to all combinations of equally spaced ILD and frequency values, creating an ILD-frequency response surface. A slice parallel to the ILD axis of this surface is an ILD tuning curve at a single frequency. The ILD-frequency response surfaces are shown in column 4 of Figure 3.
We observed that ILD tuning curves for a single cell often changed with frequency. Frequency-dependent ILD changes were sometimes dramatic. For example, in Figure 3C, the best ILD changed from
In cells with spontaneous activity >20% of maximum firing rate, which accounted for 42% of our sample, inhibitory zones were prominent in the ILD-frequency response surfaces. As exemplified by the three cells in Figure 3A-C, it was common to see inhibition on both sides of the ridge defined by the best ILD at each frequency.
Mapping pure-tone ILD-response surfaces to ILD-alone RFs
We used ILD spectra derived from the HRTFs to transform pure-tone, ILD-frequency response surfaces into predicted ILD-alone spatial RFs. As detailed in Materials and Methods and Figure 2, the predicted response to the ILD spectrum from a particular location was taken to be the mean of the pure-tone responses to each of the frequency-specific ILDs occurring in that spectrum. It should be emphasized that this method of transforming pure-tone responses into ILD-alone RFs is based on the assumption that the contributions of each frequency band are independent and are combined linearly by the cell.
ILD-alone RFs predicted from pure-tone responses are shown in column 3 of Figure 3. Comparison of columns 2 and 3 shows that the predicted ILD-alone RFs were similar in overall shape to those measured directly, although, in some cases, predicted ILD-alone RFs were noticeably misaligned relative to the measured ILD-alone RFs. However, even in the worst cases of misalignment, such as the one shown in Figure 3F, the same general shape was recognizable. In many cases, RFs predicted from pure tones captured idiosyncratic auxiliary zones seen in the measured RF. In Figure 3C, for example, the predicted and measured ILD-alone RFs share a zone of excitation at higher elevations. However, in other cases, predicted ILD-alone RFs were much more extended (Fig. 3E).
The similarity between predicted and measured ILD-alone RFs was quantified by a correlation analysis. In all cases, R2 differed significantly from zero (p < 0.01). As shown in Figure 4, R2 values ranged from 0.20 to 0.83, with many cells having R2 values in the 0.7-0.8 range. The mean R2 was 0.56, i.e., the linear sum of pure-tone responses accounted for ~56% of the variance in the measured ILD-alone responses.
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Figure 4. Distribution of all R2 values for predicting broadband ILD-alone RF values from a linear sum of pure-tone responses.
Figure 5 plots the normalized predicted and measured responses for four of the cells shown in Figure 3. In some cells, such as those shown in the bottom half of Figure 5, we observed a downward bowing, which suggests that the predicted responses often overestimated firing rates at intermediate values. The effect is also apparent in plots of predicted ILD-alone RFs, which often were less restricted than measured ILD-alone RFs (Fig. 3, compare columns 2, 3). To quantify the degree of bowing, we fit a curve of the form y = xn individually to the predicted versus measured ILD-alone plot of each cell. For the group of cells whose variance about this line was in the lower quartile (n = 9 cells), the mean coefficient, n, was 1.85. This suggests that the normalized broadband response might be better approximated by the square of the normalized pure-tone responses.
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Figure 5. Normalized, measured ILD-alone firing rate plotted against the normalized predicted ILD-alone firing rate for four cells. The jagged black line shows the running average of the distribution, and the broad gray line shows the best-fitting curve of the form y = xn. The value of n (exp) is shown at the top left of each plot.
Are neurons tuned to ILD values at the normal RF peak?
The ILD-frequency response surfaces allow us to determine the best ILD of a cell at a given frequency. The set of all such preferred ILDs defines a ridge along the ILD-frequency surface. We compared this ridge with the ILD spectra occurring at the peak of the normal RF and at the peak of the measured ILD-alone RF. Comparisons were made only at frequencies at which the maximum firing rate of a given ILD curve was at least 20% of the maximum firing rate of the entire ILD-frequency surface and the SD in the estimate of the ILD peak (i.e., best ILD) was <10 dB (for details, see Materials and Methods).
Figure 6 shows, for six representative cells, the ILD values occurring at the peaks of the ILD-alone and normal RFs superimposed on the ILD-frequency response surface of that cell. We begin by describing the comparison between the ILD spectrum occurring at the peak of the normal RF (solid green lines) with the best ILD values (filled circles). As is apparent in Figure 6, A and B, the best ILD ridge often followed the ILD spectrum from the peak of the normal RF reasonably well below 7000 Hz, although there were noticeable misalignments in some cells (Fig. 6C). At higher frequencies, the best ILD ridge often followed the general trend of the peak-location ILD spectrum but not the details. For example, if the peak-location ILD was right-louder, the ILD tuning of the cell also favored right-louder ILDs. When the ILD tuning and the peak-location ILD of a cell disagreed, the cell was usually tuned to larger absolute values (Fig. 6E,F). For example, in the cell shown in Figure 6F, the 7000 Hz ILD at the peak location was +5 dB, whereas the preferred ILD of the cell at that frequency was approximately +20 dB. When the peak-location ILD was negative, the best ILD ridge was shifted toward more extreme negative values. In a minority of cases (6 of 46), there was no apparent relationship between the best ILD ridge and the peak-location ILD values (Fig. 6D).
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Figure 6. Correspondence between pure-tone ILD tuning curves and ILD spectra. ILD-frequency response surfaces from six cells are shown as colored surface plots. Scale bars indicate firing rate, scaled between 0 and 1 for all cells. The peaks of the ILD tuning curves are indicated by filled circles or open squares, in which circles indicate estimates of peak location with low variance and high overall firing, and squares indicate less reliable estimates (for details, see Results). The horizontal bars show the SD of the location of the ILD peak. The solid green line indicates the ILD spectrum occurring at the peak of the normal RF, and the dashed red line traces the ILD spectrum occurring at the peak of the ILD-alone RF.
The differences between the frequency-specific best ILDs and the ILD spectra from the peak of the normal RF were generally >5 dB. This discrepancy is unlikely to be attributable to the use of virtual space techniques. First, the same earphones and earphone-equalizing filters were used to present both the spatial and nonspatial stimuli. Any factor that shifted ILD values in the spatial ILD spectra would have shifted pure-tone ILD values by the same amount. Second, the virtual space technique is quite reliable. The largest source of errors arises from the placement of the earphones on each experiment. In an exploratory study, we measured the earphone-ear canal transfer function for 20 repeated insertions of the earphones. The SD of these measurements, averaged across all frequencies, was 1.2 dB. Therefore, even if earphone-placement errors played a role in the mismatch between neuronal tuning and acoustical cues, they could not account for it exclusively.
The patterns illustrated in Figure 6 were even more apparent in the population data shown in Figure 7A. For frequencies between 5000 and 7000 Hz, the ILD tuning tended to correspond with the ILD values at the peak of the normal RF, although there was a fair amount of scatter in the data. At lower frequencies, the data are inconclusive because the scatter in the data are large relative to the range of peak-location ILDs. The failure of the pure-tone tuning to track ILD spectra above 7000 Hz is also apparent. As shown in Table 1, the slopes of the regression lines in both the 5000-7000 and 7000-9000 Hz ranges are slightly greater than one, showing that the ILD tuning of a cell tended to be more extreme than the peak-location ILD values. This trend is especially apparent in the outlying data points in the 5000-7000 Hz plot.
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Figure 7. Best ILD plotted against peak-location ILD for all cells. A, Pure-tone ILD tuning plotted against those ILDs occurring at the peak of the normal RF. B, Pure-tone ILD tuning plotted against those ILDs occurring at the peak of the ILD-alone RF. In both A and B, data have been broken down by frequency ranges as indicated by the title of each plot. The black line shows the best-fitting regression line. Regression coefficients are given in Table 1.
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Table 1. Results (broken down by frequency range) of a regression of pure-tone best ILDs against acoustic ILDs occurring at the peak of the normal and ILD-alone RFs (Fig. 7) As explained above, the peak of the measured ILD-alone RF was not always the same as the peak of the normal RF. In other words, cells were sometimes driven more strongly by the ILD spectrum from a location other than their normal RF peak. In light of this finding, we compared our pure-tone ILD tuning with the ILD spectra occurring at the peak of the measured ILD-alone RF (Fig. 6, dashed red lines). As exemplified by Figure 6, B and C, the ILD tuning of the cell often matched better with the ILD spectrum occurring at the peak of the ILD-alone RF than it did with the spectrum from the peak of the normal RF. As we found when comparing ILD tuning against the ILD values from the peak of the normal RF, the ILD tuning and peak ILD spectra corresponded below 7000 Hz and diverged at higher frequencies. The population data show clearly that the ILD spectrum at the ILD-alone RF peak was a better predictor of the best ILD of the cell than was the spectrum from the normal RF peak. This can be seen by comparing the 5000-7000 Hz plots of Figure 7, A and B. This difference is also apparent in the values of the Student's t test statistic shown in Table 1.
DISCUSSION
Virtual auditory stimuli have been developed for a variety of species, including humans (Wightman and Kistler, 1989a
The contribution of ILD to spatial tuning
The ILD-alone RFs, measured with ITD fixed, had complex shapes, often with multiple islands of response. However, they consistently had a horizontal band of excitation that traversed the normal RF. Above and/or below this horizontal band, we observed areas with no discharge, which, in cells with an appreciable spontaneous rate, could be shown to be inhibitory.
Because the ILD gradient at frequencies below 4000 Hz has a strong horizontal component, these lower frequencies might allow a cell to restrict its RF in azimuth. As a result, we might have found that ILD-alone RFs were as restricted in elevation and azimuth as the normal RFs are. Instead, our results indicate that restriction of the normal RF requires both ILD and ITD.
The role of ITD as an azimuthal cue is well established (Moiseff and Konishi, 1981
One of the key hypotheses that we set out to examine held that a space-specific neuron is tuned optimally to the ILD spectrum that occurs at the peak of its normal RF (Knudsen, 1999
That the ILD tuning of a cell is actually optimal for a location other than the normal RF peak is plausibly explained within a developmental framework. It has been suggested that auditory spatial RFs in the IC develop via a form of "supervised learning" wherein visual signals guide the establishment of the inputs to the space map that are appropriate for an auditory RF at a particular location (Knudsen and Brainard, 1991
Correspondence between ILD tuning and spatial tuning
Whether an IC neuron fires more at the peak of the normal or ILD-alone RF, the mechanisms of this selectivity have not been established definitively. It has been generally assumed that space-specific neurons are tuned, on a frequency-by-frequency basis, for the ILD values occurring at their optimal location (usually assumed to be the normal RF peak). Brainard et al. (1992)
We therefore examined frequency-specific ILD tuning in greater detail. We compared ILD tuning with the ILD values occurring at both the ILD-alone and normal RFs peaks. Although the pure-tone ILD tuning of a cell was coarsely related to the ILD values occurring at the normal RF peak, the match was better for the ILD spectrum associated with the ILD-alone RF peak.
Even when compared with the spectrum at the ILD-alone RF peak, however, the pure-tone ILD tuning of a cell often deviated from the HRTF-derived ILD values: best ILDs measured with noise tended to have larger absolute values than those ILD values at the ILD-alone RF peak. Interestingly, a similar trend is apparent in the data of Gold and Knudsen (2000
Frequency integration by space-specific neurons
To assess the nature of the frequency integration process occurring in the IC, we compared the ILD-alone RF of a cell, charted using broadband stimuli, with the ILD-alone RF predicted from frequency-specific ILD tuning curves, measured with tones. By probing with pure tones, we were, in essence, inferring the ILD tuning of each frequency-specific input to the neuron. If the cell summed these inputs, its responses to broadband ILD spectra (i.e., the ILD-alone stimuli) should be completely predicted by summing its responses to tones at various ILDs. This linear model, in fact, accounts for ~56% of the variance in the measured ILD-alone RFs.
Why does the linear model fail to do better? A possibility is that tones do not provide a complete picture of the inputs to a space-specific neuron. Space-specific neurons often respond phasically to tones, while their response to broadband stimuli is tonic and more robust (Knudsen and Konishi, 1978a
Differences between tone and noise responses may also account, indirectly, for the shift in ILD tuning curves to greater absolute values when measured with tones. The poor response of space-specific neurons to tones necessitated that the power spectral density of tones be greater than that of noises. In our study, noises and tones were played at approximately equal overall amplitude. However, because tones have all of their spectral energy concentrated in a narrow band, neurons in earlier processing stages, which are narrowly tuned to frequency, might have been pushed near the limits of their dynamic range by the tonal stimuli. Consequently, the processing of ILD might have been affected. It is known that ILD response curves in the nucleus ventralis laterale, pars posterior (VLVp), the first site of binaural convergence in the ILD pathway, shift with increasing ABL (Manley et al., 1988
The limitations above are intrinsic to any study that uses narrowband stimuli to probe the frequency-specific responses of broadband neurons. Additional progress may require the development of techniques, akin to reverse correlation (deBoer and Kuyper, 1968
FOOTNOTES Received May 29, 2001; revised Oct. 3, 2001; accepted Oct. 5, 2001.
This work was supported by the National Institutes of Health (National Institute on Deafness and Other Communication Disorders Grant DC03925 and National Institute of General Medical Sciences Grant T32 GM07257), the National Science Foundation (Grant LIS CMS9720334), and the James S. McDonnell and Pew Memorial Trusts via a grant to the Center for the Cognitive Neuroscience of Attention, University of Oregon. We thank Drs. Kip Keller and Klaus Hartung for their assistance with virtual auditory space techniques. Drs. Shawn Lockery, Richard Marrocco, Michael Posner, and Michael Spezio provided helpful discussions.
Correspondence should be addressed to David R. Euston at his present address: Arizona Research Laboratories Division of Neural Systems, Memory and Aging, Life Sciences North Building, Room 384, University of Arizona, Tucson, AZ 85724-5115. E-mail: euston{at}emailarizona.edu.
REFERENCES
- Adolphs R (1993) Bilateral inhibition generates neuronal responses tuned to interaural level differences in the auditory brainstem of the barn owl. J Neurosci 13:3647-3668[Abstract].
- Brainard MS, Knudsen EI (1993) Experience-dependent plasticity in the inferior colliculus: a site for visual calibration of the neural representation of auditory space in the barn owl. J Neurosci 13:4589-4608[Abstract].
- Brainard MS, Knudsen EI (1995) Dynamics of visually guided auditory plasticity in the optic tectum of the barn owl. J Neurophysiol 73:595-614
[Abstract/Free Full Text] .- Brainard MS, Knudsen EI (1998) Sensitive periods for visual calibration of the auditory space map in the barn owl optic tectum. J Neurosci 18:3929-3942
[Abstract/Free Full Text] .- Brainard MS, Knudsen EI, Esterly SD (1992) Neural derivation of sound source location: resolution of spatial ambiguities in binaural cues. J Acoust Soc Am 91:1015-1027[Web of Science][Medline].
- Brugge JF, Reale RA, Hind JE, Chan JC, Musicant AD, Poon PW (1994) Simulation of free-field sound sources and its application to studies of cortical mechanisms of sound localization in the cat. Hear Res 73:67-84[Web of Science][Medline].
- deBoer E, Kuyper P (1968) Triggered correlation. IEEE Trans Biomed Eng 15:169-179[Medline].
- Delgutte B, Joris PX, Litovsky RY, Yin TCT (1995) Relative importance of different acoustic cues to the directional sensitivity of inferior-colliculus neurons. In: Advances in hearing research (Manley GA, Klump GM, Koppl C, Fastl H, Oeckinghaus H, eds), pp 288-299. Singapore: World Scientific.
- Delgutte B, Joris PX, Litovsky RY, Yin TC (1999) Receptive fields and binaural interactions for virtual-space stimuli in the cat inferior colliculus. J Neurophysiol 81:2833-2851
[Abstract/Free Full Text] .- Eggermont JJ, Johannesma PM, Aertsen AM (1983) Reverse-correlation methods in auditory research. Q Rev Biophys 16:341-414[Web of Science][Medline].
- Egnor SER (2000) The role of spectral cues in sound localization by the barn owl. In: PhD thesis California Institute of Technology.
- Fujita I, Konishi M (1991) The role of GABAergic inhibition in processing of interaural time difference in the owl's auditory system. J Neurosci 11:722-739[Abstract].
- Gold JI, Knudsen EI (2000) Abnormal auditory experience induces frequency-specific adjustments in unit tuning for binaural localization cues in the optic tectum of juvenile owls. J Neurosci 20:862-877
[Abstract/Free Full Text] .- Hartung K, Sterbing SJ (1997) Generation of virtual sound sources for the electrophysiological characterization of auditory spatial tuning in the guinea pig. In: Acoustical signal processing in the central auditory system (Syka J, ed), pp 407-412. New York: Plenum.
- Keller CH, Hartung K, Takahashi TT (1998) Head-related transfer functions of the barn owl: measurement and neural responses. Hear Res 118:13-34[Web of Science][Medline].
- Knudsen EI (1982) Auditory and visual maps of space in the optic tectum of the owl. J Neurosci 2:1177-1194[Abstract].
- Knudsen EI (1994) Supervised learning in the brain. J Neurosci 14:3985-3997[Web of Science][Medline].
- Knudsen EI (1999) Mechanisms of experience-dependent plasticity in the auditory localization pathway of the barn owl. J Comp Physiol [A] 185:305-321[Web of Science][Medline].
- Knudsen EI, Brainard MS (1991) Visual instruction of the neural map of auditory space in the developing optic tectum. Science 253:85-87
[Abstract/Free Full Text] .- Knudsen EI, Brainard MS (1995) Creating a unified representation of visual and auditory space in the brain. Annu Rev Neurosci 18:19-43[Web of Science][Medline].
- Knudsen EI, Konishi M (1978a) Space and frequency are represented separately in auditory midbrain of the owl. J Neurophysiol 41:870-884
[Abstract/Free Full Text] .- Knudsen EI, Konishi M (1978b) A neural map of auditory space in the owl. Science 200:795-797
[Abstract/Free Full Text] .- Manley GA, Koppl C, Konishi M (1988) A neural map of interaural intensity differences in the brain stem of the barn owl. J Neurosci 8:2665-2676[Abstract].
- Moiseff A (1989a) Binaural disparity cues available to the barn owl for sound localization. J Comp Physiol [A] 164:629-636[Medline].
- Moiseff A (1989b) Bi-coordinate sound localization by the barn owl. J Comp Physiol [A] 164:637-644[Medline].
- Moiseff A, Konishi M (1981) Neuronal and behavioral sensitivity to binaural time differences in the owl. J Neurosci 1:40-48[Abstract].
- Mori K (1997) Across-frequency nonlinear inhibition by GABA in processing of interaural time difference. Hear Res 111:22-30[Web of Science][Medline].
- Nelken I, Bar Yosef O, Young ED (1997) Response of Field AES neurons to virtual-space stimuli. In: Psychophysics and physiological advances in hearing (Palmer AR, Rees A, Summerfield AQ, Meddis R, eds), pp 504-512. London: Whurr.
- Olsen JF, Knudsen EI, Esterly SD (1989) Neural maps of interaural time and intensity differences in the optic tectum of the barn owl. J Neurosci 9:2591-2605[Abstract].
- Pena JL, Konishi M (2001) Auditory spatial receptive fields created by multiplication. Science 292:249-252
[Abstract/Free Full Text] .- Poon PW, Brugge JF (1993) Virtual-space receptive fields of single auditory nerve fibers. J Neurophysiol 70:667-676
[Abstract/Free Full Text] .- Reale RA, Brugge JF (2000) Directional sensitivity of neurons in the primary auditory (AI) cortex of the cat to successive sounds ordered in time and space. J Neurophysiol 84:435-450
[Abstract/Free Full Text] .- Rice JJ, Young ED, Spirou GA (1995) Auditory-nerve encoding of pinna-based spectral cues: rate representation of high-frequency stimuli. J Acoust Soc Am 97:1764-1776[Web of Science][Medline].
- Spezio ML, Keller CH, Marrocco RT, Takahashi TT (2000) Head-related transfer functions of the rhesus monkey. Hear Res 144:73-88[Web of Science][Medline].
- Takahashi T, Konishi M (1986) Selectivity for interaural time difference in the owl's midbrain. J Neurosci 6:3413-3422[Abstract].
- Takahashi TT, Keller CH (1992) Simulated motion enhances neuronal selectivity for a sound localization cue in background noise. J Neurosci 12:4381-4390[Abstract].
- Wagner H, Takahashi T, Konishi M (1987) Representation of interaural time difference in the central nucleus of the barn owl's inferior colliculus. J Neurosci 7:3105-3116[Abstract].
- Wightman FL, Kistler DJ (1989a) Headphone simulation of free-field listening. I. Stimulus synthesis. J Acoust Soc Am 85:858-867[Web of Science][Medline].
- Wightman FL, Kistler DJ (1989b) Headphone simulation of free-field listening. II. Psychophysical validation. J Acoust Soc Am 85:868-878[Web of Science][Medline].
- Wightman FL, Kistler DJ (1997) Monaural sound localization revisited. J Acoust Soc Am 101:1050-1063[Web of Science][Medline].
- Zheng W, Knudsen EI (1999) Functional selection of adaptive auditory space map by GABAA-mediated inhibition. Science 284:962-965
[Abstract/Free Full Text] .
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