Auditory Cortical Responses to the Interactive Effects of Interaural Intensity Disparities and Frequency

Julie R. Mendelson and Keith L. Grasse1

Department of Speech-Language Pathology, Faculty of Medicine, University of Toronto, Toronto, Ontario M5S 3H2 and , 1 Department of Psychology, Centre for Vision Research, Institute for Space & Terrestrial Science, York University, North York, Ontario M3J 1P3, Canada


    Abstract
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Under natural conditions, stimuli reaching the two ears contain multiple acoustic components. Rarely does a stimulus containing only one component (e.g. pure tone burst) exist outside the realm of the laboratory. For example, in sound localization the simultaneous presence of multiple cues (spectral content, level, phase, etc.) serves to increase the number of available cues and provide the listener with more information, thereby helping to reduce errors in locating the sound source. The present study was designed to explore the relationship between two acoustic parameters: stimulus frequency and interaural intensity disparities (IIDs). By varying both stimulus frequency and IIDs for each cell, we hoped to gain insight into how multiple cues are processed. To this end, we examined the responses of neurons in cat primary auditory cortex (AI) to determine if their sensitivity to IIDs changed as a function of stimulus frequency. IIDs ranging from +30 to –30 dB were presented at different frequencies (frequency was always the same in the two ears). We found that approximately half of the units examined exhibited responses to IIDs that varied as a function of stimulus frequency (i.e. displayed some form of IID x Freq dependency). The remaining units displayed IID responses that were not clearly related to stimulus frequency.


    Introduction
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Studies have shown that cortical cells are sensitive to a variety of stimulus parameters such as interaural intensity, temporal and frequency when they are examined individually (Kitzes et al., 1980Go; Phillips and Irvine, 1981Go, 1983Go; Reale and Kettner, 1986Go; Mendelson, 1992Go). Under natural conditions, these cues rarely arise in isolation. In fact, in sound localization if only one of the available cues is present, spatial ambiguity often occurs, causing the organism to mislocalize. This ambiguity stems from the fact that sounds arising from different locations may produce identical values of a given cue. Thus, the presence of additional cues (spectral, temporal, level, etc.) helps to disambiguate the location of the sound source.

The goal of the present study was to further our understanding of how the auditory cortex processes the interaction of multiple acoustic parameters. To date, relatively few studies have examined the interaction of two or more parameters (Kitzes et al., 1980Go; Takahashi et al., 1984Go; Semple and Kitzes, 1987Go; Wenstrup et al., 1988aGo,bGo; Fuzessery et al., 1990; Brainard et al., 1992Go; Irvine et al., 1996Go; Park et al., 1997Go). In general, these studies have shown that the response of some neurons to one parameter can be modulated by the simultaneous manipulation of a second parameter. For example, Irvine et al. (1995) studied the relationship between interaural intensity and temporal differences as would be predicted by the time–intensity trading phenomenon observed in psychophysical studies (Deatherage and Hirsh, 1959Go). They found that for the majority of units in the inferior colliculus, the response to interaural intensity disparities (IIDs) could not be predicted from the response to interaural temporal disparities (ITDs). The effect of sound pressure level (SPL) has also been shown to modulate the response of units in the inferior colliculus (Semple and Kitzes, 1987Go; Wenstrup et al., 1988aGo,bGo; Fuzessery et al., 1990) and auditory cortex (Irvine et al., 1996Go) to IIDs. Park et al., on the other hand, have shown that stimulus duration has no effect on the IID response of lateral superior olive (LSO) neurons (Park et al., 1997Go). Collectively, one point these studies clearly demonstrates is that the way in which neurons in the auditory system treat multiple parameters is by no means a simple matter.

Two other parameters that warrant examination because of their intimate relationship are the intensity and spectral components of the signal. Recent studies have shown that IID-azimuth functions display different patterns of non-monotonicity/monotonicity at different frequencies (Irvine, 1987Go; Martin and Webster, 1989Go; Rice et al., 1992Go). However, the way in which the interaction of these parameters is encoded by the auditory cortex is not well understood. Thus, in the present study we investigated the relationship between IIDs and stimulus frequency in an attempt to explore how the response of a cortical unit to one parameter can be modified by the manipulation of a second parameter.


    Materials and Methods
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Recording Preparation

Nine adult cats with otoscopically clean ears were used in this study. The surgical procedures employed have been approved by the Canadian Council for Animal Care (CCAC) and comply with the stipulations regarding the care and use of experimental animals set out by the American Physiological Association. Initially, animals were tranquilized with an i.m. injection of ketamine hydrochloride (10 mg/kg) and acetylpromazine maleate (0.1 mg/kg) to allow for venous cannulation. Animals were then given sodium pentobarbital (30 mg/kg i.v.) followed by i.m. injections of atropine (1 mg) to reduce salivation, and dexa-methasone sodium phosphate (0.14 mg/kg) to help prevent brain edema. A surgical level of anesthesia was maintained throughout the experiment with a constant i.v. infusion of sodium pentobarbital (2 mg/kg/h) in lactated Ringer's solution. Body temperature was maintained at 37.5°C. The EEG and EKG were recorded continuously throughout the experiment to monitor any changes in the state of anesthesia. Anesthesia was considered adequate if the EEG was characterized by relatively large amplitude, low frequency activity coupled with intermittent spindles, and the EKG indicated a heart rate of ~170 beats/min.

Both pinnae were surgically reflected and the external meatuses exposed to allow for insertion of sound delivery tubes. The temporal muscle was transected on the left side and a craniotomy was performed over the middle of the ectosylvian gyrus. All wound margins and pressure points were generously infiltrated with a long-acting local anesthetic (bupivicaine hydrochloride 2.5%).

Stimulation and Recording

The animals were located in an electrically shielded sound-attenuating chamber (IAC). Glass-coated platinum–iridium microelectrodes (impedance 0.7–1.3 M{Omega}) were used to record extracellular single-unit responses. The microelectrode was aimed orthogonal to the surface of the primary auditory cortex and advanced remotely through the dura mater by a microdrive (Kopf). Neuronal activity was amplified, band-passed filtered (BAK A-1B Electronics amplifier), and monitored on an oscilloscope (Tektronix) and audiomonitor (Grass). Spike activity was separated from background noise with a level discriminator (BAK DS1 window discriminator). Stimulus and neuronal spike event times were collected and stored on-line by computer (Macintosh).

Pure tone burst stimuli of 110 ms (including 5 ms rise/decay time) were generated by a Wavetek function generator and were presented once every 700 ms. The tones were led through two independent, digitally controlled attenuators (Med Associates), to separate passive attenuators (Hewlett-Packard), and finally to a pair of calibrated STAX (SR 54) earphones encased in small chambers that were connected to sound delivery tubes sealed into the acoustic meatuses (Sokolich, US Patent 4,251,686; 1981). The speculae of the couplers fit snugly into the external meatuses to within 3 mm of the tympanic membrane. Calibration of the stimuli was carried out with a B&K 1/4 in. microphone (4136) and sound level meter (2231). The frequency response of the system was essentially flat up to 14.5 kHz and did not have major resonances deviating more than 6 or 7 dB from the average level. Above 16.0 kHz, the output rolled off at a rate of ~10 dB/octave.

Procedure

Single units were recorded along penetrations aimed orthogonal to the surface of the middle ectosylvian gyrus (AI) and were typically recorded between 500 and 1200 µm below the cortical surface, depths approximately corresponding to layers III, IV and V. Either at the beginning or at the end of an experiment the frequency representation of the cortical surface in the immediate vicinity was mapped to establish the relative location of recording sites within AI. In all cases, the frequency maps thus obtained were consistent with previous descriptions of the frequency representation in AI (Merzenich et al., 1975Go; Reale and Imig, 1980Go).

Upon isolation of a single unit, the characteristic frequency (CF), threshold and frequency tuning curve (at 10 dB above CF threshold) were determined. A deliberate effort was made to study cells with relatively high CFs in order to allow for a wide range of frequencies both higher and lower than CF to be tested. The upper frequency cutoff was restricted by the flat output of the stimulating system. Following this, the cell's binaural response type was determined by presenting the CF tone at equal intensity to both ears and comparing the response to the contralateral monaural CF response. Cells were classified as binaural facilitatory (EE), binaural inhibitory (EI) or as binaurally insensitive (EO). IIDs were then presented over a range of frequencies, though the frequency was always the same in both ears. Stimulus intensity was held constant in the contralateral ear at 10 dB above threshold while being systematically varied in the ipsilateral ear from –30 to +30 dB relative to the contralateral ear stimulus in 6 dB increments for a total of 11 IID conditions per frequency. The range of frequencies over which the IIDs were tested was determined by the breadth of the individual cell's excitatory tuning curve characteristics determined by stimulation of the contralateral ear. Frequencies at least 1.0 kHz higher and lower than CF were tested, and were typically presented in 500 Hz increments. In addition, the cell's contralateral monaural and ipsilateral monaural response to each frequency tested were recorded at 10 dB above CF threshold, as was its spontaneous activity during a stimulus-free 100 ms epoch. All IID by frequency (IID x Freq) stimulus conditions were controlled by computer and were presented in random order. Each stimulus condition was presented 40 times.

Data Analyses

Responses were initially quantified by counting the total number of spikes occurring in the first 200 ms after stimulus onset. This allowed for analysis of both ‘on’ and ‘off’ responses, when both types of response were present.

Since the aim of this study was to examine how IID and frequency interact, units were analyzed to determine if their response to IIDs was independent of frequency (i.e. where the response to IIDs was not modulated by stimulus frequency), or dependent upon stimulus frequency (i.e. where the response to IIDs varied as a function of frequency). To this end, the following analysis, used in studies in the visual system, was performed (Cynader et al., 1978Go). For each frequency the cell's monaural contralateral (Cmon) and ipsilateral (Imon) responses were summed. From this sum, the spontaneous activity (SA) recorded during a 100 ms epoch was subtracted: (Cmon + Imon) – SA. The resulting value was then subtracted from each of the IID conditions for a given frequency.

Neurons were then classified into one of two groups: cells exhibiting IID x Freq independence, where the response to IIDs was unaffected by the stimulus frequency, or IID x Freq dependence, whereby the response to IIDs varied as a function of frequency. This was determined by assessing whether the responses to frequencies lower (typically 500 Hz) than CF were within 25% of those higher (typically 500 Hz) than CF for a given IID condition (i.e. symmetric or asymmetric around CF). In other words, if the response at CF – 500 Hz for +24 (ipsi dB re contra) was 20 spikes/s and the response to CF + 500 Hz for +24 (ipsi dB re contra) was 22 spikes/s then the cell would be exhibiting IID x Freq independence. If, on the other hand, the response to CF – 500 Hz for +24 (ipsi dB re contra) was 20 spikes/s and the response to CF + 500 Hz for +24 (ipsi dB re contra) was 30 spikes/s, then the cell would be classified as IID x Freq dependent because the response to IIDs was different for the different frequency conditions.

In addition to whether a cell exhibited IID x Freq dependence or independence, most units (44/48) could be further classified based on their response profile to the IID conditions. This was based on the classification of units into monotonic versus nonmonotonic when stimulus intensity is increased (Phillips and Irvine, 1981Go). Units referred to as Type I units exhibited increased facilitation (or inhibition) in response to an increasing IID, i.e. a monotonic-type response. The second group of units, Type II units, were characterized by a nonsigmoidal response profile to IIDs in that the response peaked at a given IID condition and then decreased with increasing IIDs. In other words, Type II units responded to a ‘band-passed’ set of IID x Freq conditions. The third group of cells, Type III, exhibited a more complex response profile that included both a sigmoidal and band-passed component.


    Results
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
The results of this study are based on an examination of 65 cortical cells in AI. A complete data set (i.e. cells for which responses to all stimulus conditions were recorded) was obtained for 48 units of which 23 were classified as EE and 25 as EI when CF was presented at equal intensity to both ears. The average CF was 9.4 kHz and ranged from 5.0 to 14.0 kHz. There was an intentional sampling bias toward neurons with high CFs so that each unit could be tested with an adequate range of frequencies both below and above its CF. The majority of units (42/48) responded in a transient manner to the onset of the stimulus, while six of the units responded in a sustained fashion for the duration of the stimulus. The responses of these six sustained units to the IID x Freq conditions did not appear to differ in any other way from the other units in the study.

All of the units (n = 48) for which a complete data set was obtained were responsive to a subset of IID x Freq conditions. Approximately half of the units (n = 23) displayed some form of IID x Freq dependency. For 12 of these 23 units the response to IIDs did not increase with higher frequencies or decrease with lower frequencies but rather exhibited a variety of different response patterns to the stimuli.

Figure 1Go shows the response of a typical Type I unit encountered in the present study. This unit was characterized by a gradual increase in response with increasing IID until it reached asymptote at +18 dB (i.e. where the stimulus presented to the ipsilateral ear was 18 dB more intense than the stimulus presented to the contralateral ear; labelled ‘ipsi dB re contra’). Figure 1AGo shows the IID responses of the unit as a function of frequency. The monotonic nature of this unit's response can be seen most clearly by following its IID response at CF (14.0 kHz). In addition to reaching asymptote at +18 dB (ipsi dB re contra), this unit's response was facilitated over the IID range of +6 dB (ipsi dB re contra; 11 spikes/s) to +30 dB (ipsi dB re contra; 28 spikes/s). Figure 1BGo shows the response of the same unit, this time plotting response versus frequency for all IID conditions. This figure shows that the peak response always occurred at CF, revealing a relative independence to different IIDs. Because the response of this unit was similar for the IID conditions presented at frequencies both higher and lower than CF, this cell is described as exhibiting IID x Freq independence.



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Figure 1.  Example of a unit that was classified as IID x Freq independent. The IID response of the unit as a function of frequency (A) and the frequency response as a function of IID (B). This unit's response was facilitated over the IID range of +12 to +30 dB (ipsi dB re contra) with maximum facilitation occurring at CF (14.0 kHz). Facilitation was also present within 1.0 kHz of CF.

 
Figure 2Go illustrates another kind of Type 1 unit that exhibited IID x Freq dependence. The response of this cell was inhibited when the intensity of the ipsilateral stimulus was varied from –6 dB (ipsi dB re contra; –3 spikes/s) to +30 dB (ipsi dB re contra; –46 spikes/s). Deepest inhibition occurred at CF (11.5 kHz), but was also apparent within 1.0 kHz of CF. As can be seen, at 500 Hz above CF, the cell was inhibited between 0 dB (ipsi dB re contra; –5 spikes/s) and +30 dB (ipsi dB re contra; –16 spikes/s), while at 500 Hz below CF, inhibition occurred between –6 dB (ipsi dB re contra; –10 spikes/s) and +30 dB (ipsi dB re contra; –20 spikes/s). Thus, given that the IID responses to frequencies higher than CF were different from those recorded at frequencies lower than CF (i.e. they were asymmetrical), the response of this unit was characterized as IID x Freq dependent.



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Figure 2.  An example of a neuron that displayed IID x Freq dependence. The response of this cell was inhibited when the intensity of the ipsilateral stimulus was –12 to +30 dB (ipsi dB re contra) with deepest inhibition occurring at CF (11.5 kHz) as well as within 1.0 kHz of CF. As illustrated, this cell did not respond over the same IID range at all binaurally responsive frequencies. At 500 Hz above CF, the cell was inhibited between +6 and +30 dB (ipsi dB re contra) while at 500 Hz below CF, inhibition occurred between –12 and +30 dB (ipsi dB re contra).

 
Thirty-two of all units studied were classified as Type I. Of these Type I units, 17 exhibited IID x Freq independence while the remaining 15 units displayed IID x Freq dependence. While the nature of these IID x Freq dependent responses varied from unit to unit, six of these units responded to lower frequencies with smaller IIDs and to higher frequencies with larger IIDs.

An example of the second group of units (n = 7) Type II units (responding to a ‘band-passed’ set of IID x Freq conditions), is illustrated in Figure 3Go. For this unit (CF = 7.8 kHz), facilitation was evident between 0 dB (ipsi dB re contra; 10 spikes/s) and +30 dB (ipsi dB re contra; 16 spikes/s) with the most vigorous response occurring at +18 dB (ipsi dB re contra; 31 spikes/s) and decreasing for +24 dB (ipsi dB re contra; 19 spikes/s) and +30 dB (ipsi dB re contra; 16 spikes/s). The unit's response did not vary as a function of frequency at the smaller IIDs (e.g. 0–±12 dB ipsi dB re contra) but did so at larger IIDs (+18–30 dB ipsi dB re contra). Consequently, this unit was classified as IID x Freq dependent.



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Figure 3.  Example of a neuron that exhibited IID x Freq independence. This unit's (CF = 7.8 kHz) response was facilitated between 0 and +30 ipsi dB re contra with the most vigorous response occurring between +6 and +18 dB ipsi re contra.

 
Of the Type II neurons investigated, two were found to exhibit IID x Freq independence while the remaining five units showed frequency dependence. As with the Type I units, the nature of this frequency dependence was idiosyncratic. However, four of the five IID x Freq dependent units responded to larger IIDs when the ipsilateral ear was more intense than the contralateral ear for frequencies below CF and to progressively smaller IIDs as stimulus frequency was increased until it reached CF. For frequencies higher than CF, these units responded to IIDs when the amplitude of the contralateral ear stimulus was higher than that of the ipsilateral ear. For the remaining IID x Freq dependent unit, there was an increase in IID at higher frequencies and a decrease in IID at lower frequencies.

An example of the third group of cells (n = 5), Type III (exhibiting a combination of sigmoidal and band-passed components), is illustrated in Figure 4Go. As can be seen, the unit displayed an inhibitory monotonic response at 7.5 kHz (i.e. 1.0 kHz below the CF 8.5 kHz) over an intensity range from 0 dB (ipsi dB re contra; –20 spikes/s) to +30 dB (ipsi dB re contra; 51 spikes/s). At 500 Hz above CF, the unit exhibited a facilitatory nonmonotonic response profile from –12 dB (ipsi dB re contra; 17 spikes/s) to +18 dB (ipsi dB re contra; 15 spikes/s) with maximum facilitation occurring at +12 dB (ipsi dB re contra; 47 spikes/s). This unit exhibited one of the clearest examples of IID x Freq dependency encountered in this study.



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Figure 4.  Example of another type of unit that exhibited IID x Freq dependence. This neuron displayed an inhibitory monotonic response from +12 to +30 dB (ipsi dB re contra) with deepest inhibition occurring at 1.0 kHz below CF (8.5 kHz). At its CF, the unit exhibited a facilitatory nonmonotonic response profile from 0 to –18 dB (ipsi dB re contra).

 
Figure 5Go shows another example of a Type III unit. As illustrated, this cell was binaurally facilitated between the IID conditions of –6 dB (ipsi dB re contra; 15 spikes/s) and +12 dB (ipsi dB re contra; 24 spikes/s), with strongest facilitation occurring at +6 dB (ipsi dB re contra; 28 spikes/s). Maximum facilitation was observed at CF (6.7 kHz) but was still evident within 500 Hz of CF. A second, smaller region of facilitation occurred at CF between –18 dB (ipsi dB re contra; 9 spikes/s) and –30 dB (ipsi dB re contra; 12 spikes/s). The IID response of this unit did not appear to be dependent upon the frequency of the stimulus.



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Figure 5.  Another type of unit that exhibited IID x Freq independence. This cell was binaurally facilitated between the IID conditions of 0 and +18 dB (ipsi dB re contra) as well as weakly binaurally modulated at +24 to +30 dB (ipsi dB re contra). Greatest facilitation occurred at CF (6.2 kHz) but was still evident within 500 Hz of CF. A second smaller region of facilitation was evident at CF when the contralateral ear was 18–30 dB more intense than the stimulus in the ipsilateral ear.

 
Four of the five Type III neurons displayed IID x Freq dependency.

IID x Freq as a Function of Binaural Response Type

The distribution of units as a function of binaural response type (determined with CF presented at equal intensity to both ears) for each of the three types of IID x Freq responses is shown in Figure 6Go. Figure 6AGo shows the distribution of units that displayed IID x Freq dependence while Figure 6BGo shows it for the units that exhibited IID x Freq independence. As illustrated, the majority (26/32) of Type I units were EI (lined bars) with 14 exhibiting IID x Freq independence and 12 exhibiting IID x Freq dependence. In contrast, most (6/7) of the Type II neurons were EE (solid bars) with two of the six classified as IID x Freq independent. Finally, there were three EE (of which two were IID x Freq independent) and two EI Type III units (both of which were IID x Freq dependent).



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Figure 6.  Distribution of the three types of units as a function of binaural response type and (A) IID x Freq dependence and (B) IID x Freq independence. The majority of units were classified as Type I. EE units are depicted by solid bars while EI units are represented by lined bars.

 
IID x Freq Independence versus Dependence

All of the units for which a complete data set was obtained (n = 48) were responsive to a subset of IID x Freq conditions. Approximately half of the units (n = 23) displayed some form of IID x Freq dependency. In addition, the types of responses described above (e.g. monotonic, nonmonotonic response profile to IID x Freq stimulus conditions) appeared to be distributed approximately equally between the IID x Freq independent and IID x Freq dependent groups.

IID x Freq and Monotonicity

When units were presented with IIDs at CF, 33% exhibited a nonmonotonic response function. In general, if a unit responded to IIDs at CF in a monotonic fashion, then it typically responded monotonically to IIDs at non-CF frequencies. The same observation was made for cells that responded nonmonotonically to IIDs at CF.

Additional Observations: On–Off Responses

Nine units in the present study responded to both the onset and the offset of the stimulus. For some units, the off response was evoked over the same frequency range in which the on response was elicited. For other units, off responses were elicited for frequencies approximately 1.0 kHz higher or lower than those evoking on responses. The particular characteristics of off responses varied from neuron to neuron.

Another aspect of the unit's response that was examined was a comparison of the frequency range of the cell's monaural tuning curve (assessed at 10 dB above CF threshold) with the binaural frequency response range when tested with IIDs. In other words, did the cell respond to the same range of frequencies under contralateral monaural and binaural stimulus conditions. For 40 of the units, binaural IID responses were elicited over the same frequency range in which responses were obtained under monaural stimulus conditions.


    Discussion
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
The primary goal of the present study was to explore the functional relationship between changes in stimulus frequency and IID in the responses of auditory cortical neurons. To this end, approximately half of the units examined exhibited responses to IIDs that varied as a function of stimulus frequency (i.e. displayed some form of IID x Freq dependency). The remaining units displayed IID responses that were, for the most part, invariant to stimulus frequency.

Comparison with Other Studies

Monotonicity

In the present study ~66% of the units exhibited monotonic response profiles when tested with IIDs at CF. This is consistent with what has previously been reported (Phillips and Irvine, 1981Go; Schreiner et al., 1992Go). In addition, the fact that monotonicity appeared to be independent of CF has also been observed by other investigators (Phillips and Irvine, 1981Go; Schreiner et al., 1992Go).

Effect of Frequency on ITD/IPD

A number of studies have examined the effect of varying stimulus frequency on ITDs. Early studies showed that when the ITD sensitivity of auditory neurons was assessed qualitatively, the ITDs appeared to be independent of stimulus frequency (Rose et al., 1966; Brugge and Merzenich, 1973Go; Benson and Teas, 1976Go). However, more recent investigations employing quantitative techniques have yielded mixed results. For example, while Yin and Kuwada found that the characteristic interaural phase disparity (IPD) for some units in the cat inferior colliculus varied as a function of frequency (Yin and Kuwada, 1983Go), Spitzer and Semple found that the peak of the ITD function of units in the gerbil superior olive typically did not vary with frequency (Spitzer and Semple, 1995Go). Despite the apparently conflicting nature of these results, it does appear that non-CF frequencies can engender different peak ITD responses for a given unit. Consistent with this, the results of the present study show that non-CF frequencies can also affect binaural cortical responses since the IID functions often differed markedly from one unit to another.

Effect of Intensity on ITD/IPD

The effect of varying stimulus intensity on ITD responses has also been examined. Yin and Kuwada found that the characteristic ITD of inferior colliculus units could change as a function of stimulus intensity (Yin and Kuwada, 1983Go, 1984Go). However, Park et al. showed that responses to IIDs were invariant with absolute intensity (Park et al., 1997Go).

Irvine et al. examined the relationship between ITD and IID to determine if the neural sensitivity to IIDs could be predicted from the ITD sensitivity profile (Irvine et al., 1995Go). The basis for their study was initially derived from Jeffress's latency hypothesis which postulates that there should be a predictable interaction between these two sound localization cues (Jeffress, 1948Go). Surprisingly, Irvine et al. found that the responses to IIDs could be predicted from ITD response profiles for ~10% of the cortical neurons investigated (Irvine et al., 1995Go). These results serve to further underline the complex way in which sound localization is accomplished by the auditory system.

Effect of Average Binaural Intensity or Stimulus Duration on IID

The effect of average binaural intensity has been shown to influence the IID response of cells in the inferior colliculus (Semple and Kitzes, 1987Go; Wenstrup et al., 1988aGo,bGo; Fuzessery et al., 1990) as well as the cortex (Irvine et al., 1996Go). In general, these investigators found that the binaural intensity level can influence a unit's response to IIDs. Park et al. examined the effect of stimulus duration on IID responses in the bat LSO; they found that increasing stimulus duration increased spike count, but did not affect IID (Park et al., 1997Go).

Collectively, these studies and the results of the present study support the suggestion that multiple cues interact in complex ways that, at present at least, are poorly understood.

Possible Implication of IID x Freq Responses for Sound Localization

The response of all units in the present study was influenced by IIDs independent of whether or not that response was invariant with stimulus frequency. This supports the notion that units in AI may provide varying degrees of spatial information about the location of a sound source. For example, researchers have suggested that those cortical cells displaying a monotonic response to IIDs could be associated with processing contralateral sound source azimuths (Phillips and Irvine, 1981Go). In addition, the slope of the function could provide some form of information about the medial border of the receptive field (Wise and Irvine, 1985Go; Wenstrup et al., 1988; Irvine and Gago, 1990Go). Units exhibiting a nonmonotonic response function could in turn provide information about sound sources located in the central region of the sound field (Phillips and Irvine, 1981Go; Fuzessery and Pollack, 1985Go). Thus, in the present study, IID x Freq independent units may provide general spatial information about azimuthal sound sources located in the contralateral hemifield or in the central region.

While IID x Freq independent units may provide some degree of general azimuthal spatial information about a sound source (through their response to IIDs), it is possible that IID x Freq dependent units may provide more specific information about a sound source's location. After all, precise sound localization is typically based upon the presence of multiple cues. For example, it is known that in localizing azimuthal sound sources, the sound shadowing properties of the head cause higher-frequency components to be attenuated more than lower-frequency components in the ear furthest from the sound source. This implies that the IID produced by a sound source in one location will have different values for different frequencies. In both cats and humans the IID caused by head shadowing can be as small as 3 or 4 dB for frequencies <4.0 kHz and as large as 20–30 dB for higher frequencies at more extreme eccentricities (Feddersen et al., 1957Go; Moore and Irvine, 1979Go; Middlebrooks et al., 1989Go). One implication of this observation is that a cell selective for a given IID (or range of IIDs) at a given frequency (or range of frequencies) is capable of providing the auditory system with fairly specific spatial information about a given sound source. In the present study, the IID response of almost 50% of the units was modulated in some manner by the stimulus frequency. This suggests that perhaps up to half the cortical units may be providing the organism with spatial information in addition to whether or not the sound source is located in a hemifield or central region.

However, accurate sound localization is most likely achieved by the presence of more than two cues. It is certainly the case that under natural conditions there is typically an array of acoustical cues available which allow the organism to derive an accurate representation of sound source location. In accord with this multiple-cue requirement, Brainard et al. reported that the value of IID for a single frequency is not sufficient to specify the exact location of a sound source because, given other conditions, several locations may share similar IIDs (Brainard et al., 1992Go). For example, an IID of 6 dB for a 4 kHz tone may arise from anywhere on a conical surface extended in space. In their study, Brainard et al. found that the multiple receptive fields of units in the optic tectum of the barn owl observed when one cue was presented at a time resolved into a single, spatially circumscribed receptive field when these same cells were stimulated with multiple cues presented simultaneously (Brainard et al., 1992Go). Finally, it has been suggested that the location of sound sources are not encoded by single neurons, but rather by a population of neurons (so-called ‘ensemble coding’) (Clarey et al., 1994Go; Irvine et al., 1996Go).

In general, it appears that many neurons in AI are capable of providing some type of information about the azimuthal location of a sound source. However, for IID x Freq independent units in particular, this information appears to be of fairly limited resolution, providing the organism with such acuity information as ‘the sound is originating from the contralateral hemifield’ or ‘it is originating from the frontal field’, etc. This interpretation has been supported by a number of free-field studies in which it has been shown that many neurons respond to sounds positioned over a relatively wide region of space (Middlebrooks and Pettigrew, 1981Go).

Thus, on a theoretical level, these two types of cortical units, the IID x Freq independent and IID x Freq dependent units, may be thought to lie along a spatial encoding continuum within which units displaying IID x Freq independence that provide more general spatial information are found at one end, while IID x Freq dependent units might lie more toward the other end of this continuum, closer to cells that provide very precise spatial information.

Despite the above argument that cortical cells may provide varying degrees of azimuthal spatial information to the organism, one should not necessarily conclude that these units do not provide specific spatial information. It is possible that cortical units are better suited, for example, localizing sounds along another spatial dimension such as an oblique axis. It is also possible that these units may provide more specific spatial information when presented with a different combination of parameters such as IID and ITD, for example. In other words, those units whose response to IIDs appear to be independent of frequency might have their IID responses modulated by another stimulus parameter.

Functional Implications of the Role of AI

Given that approximately half of the units in the present study exhibited IID x Freq independence suggests that AI may be involved in a first-order — or at least relatively early — analysis of sound location cues in that it provides what appears to be a fairly imprecise representation of sound source location. This suggestion, in turn, leads to the speculation that precise azimuthal encoding of sound source location may be performed in either another extraprimary cortical field or through some complex interaction between different cortical fields. Roullier et al. and Mendelson et al. have proposed a hypothetical scheme whereby an initial stage of auditory processing is allocated to AI, which is thought of as being involved in a more general form of signal encoding processing and/or stimulus representation than subsequent cortical fields (Roullier et al., 1991Go; Mendelson et al., 1997Go).

Another possible functional role of AI may be to perform some form of acoustical analysis such as signal detection. Partial support for this notion can be derived from free-field studies in which many neurons appear to respond to sounds emanating from a wide, as opposed to a restricted, region of space (Imig et al., 1990Go; Middlebrooks and Pettigrew, 1981Go; Rajan et al., 1990aGo,bGo; Clarey et al., 1994Go). In this more limited capacity, AI cells need only provide the animal with information about the general location of sound sources, thereby leaving AI free to analyze several other aspects in parallel. In other words, AI units may act more like a ‘range-finder’, leaving the task of highresolution localization for another cortical stage. However, lesion studies suggest that AI's role in sound localization is more than just mere signal detection (Jenkins and Masterton, 1982Go; Jenkins and Merzenich, 1984Go; Heffner and Heffner, 1990Go). Yet if the role of AI is at all similar to that of the primary visual cortex, then the effects of AI lesions can be potentially misleading in that the lesion may interrupt the general flow of signals, some of which are further elaborated by subsequent cortical regions into highly specific functions such as high-resolution sound localization.

A closely related alternative view of the function of AI is that it is primarily involved in the spectral analysis of sounds rather than in sound location per se. For example, AI may perform a first-order spectral analysis of sound stimuli, which would be useful for processing such stimuli as species-specific communication signals or characteristic sounds generated by prey. The results of this first-order spectral analysis could be conveyed to other cortical fields where, as suggested above, it might be refined further in a manner useful for more specific functions such as sound localization and/or acoustic pattern recognition. Additional research is necessary before either the role of AI or the role of higher auditory cortical areas can be comprehended within a single theoretical framework.


    Notes
 
This research was supported by the Natural Sciences and Engineering Research Council of Canada (WFA0123096, EQ0121437, EQ0156825).

Address correspondence to J.R. Mendelson, Department of Speech-Language Pathology, Faculty of Medicine, University of Toronto, Tanz Neuroscience Building, 6 Queen's Park Crescent West, Toronto, Ontario, Canada M5S 3H2. Email: j.mendelson{at}utoronto.ca.


    References
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 
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