Neurons in the cat's primary auditory cortex distinguished by their responses to tones and wide-spectrum noise

73

Hewing Research, 18 (1985) 73-86 Elsevier

HRR 00595

Neurons in the cat’s primary auditory cortex distinguished by their responses to tones and wide-spectrum noise D.P. Phillips, S.S. Orman, A.D. Musicant

and G.F. Wilson

Department of Neurophysiology and Waisman Center on Mental Retardation and Human Development, Unioersity of Wisconsin. Madison, WI 53706, U.S.A. (Received

18 December

1984; accepted

27 March

1985)

In the cortex of barbiturate-anesthetized cats, area AI was identified by its tonotopic organization, and single neurons in that field were examined with regard to the shapes of their spike count-versus-intensity functions, the organization of their frequency-intensity response areas, and their responses to wide-spectrum noise, using calibrated sealed stimulating systems. Neurons whose pure tone rate intensity functions were monotonic in shape displayed V-shaped response areas that were open-ended at high tone intensities. In contrast. cells displaying nonmonotonic tone intensity functions tended to have circumscribed response areas; these cells were responsive to tones over limited ranges of both frequency and intensity. Monotonic neurons almost always responded to wide-spectrum noise stimuli, while nonmonotonic neurons often did not. The mean minimum latent period of monotonic cells (14.0 ms) was significantly shorter than that for nonmonotonic neurons (19.1 ms). For those cells that responded to both tones and noise, minimum latent periods for the two stimuli were similar or identical. Monotonic neurons tended to be horizontally segregated from nonmonotonic neurons across AI’s middle cortical layers. The implications of these data for the nature of some neural mechanisms underlying the stimulus selectivity of cortical cells are discussed auditory

cortex,

single neuron,

tone, wide-spectrum

noise

Introduction In the barbiturate-anesthetized cat’s primary auditory cortex (area AI), single neurons display marked selectivity to the frequency of tonal stimuli at threshold levels of stimulation. This selectivity is manifested in the narrow V-shape of their threshold tuning curves that enable each neuron to be assigned a ‘characteristic frequency’ (CF) to which it is most sensitive [21,28]. If the spike discharge rate of a cortical cell is studied as a function of the intensity of a CF tone, the resulting rate intensity function generally can be classified as either ‘monotonic’ or ‘nonmonotonic’ in shape. In the former case, spike counts increase from threshold over a lo-40 dB range, usually tending towards a ceiling level of discharge rate that is retained over wide ranges of higher intensities. For nonmonotonic cells, spike counts increase from threshold, reach a single maximum typically within lo-40 dB of threshold, and then decline 0378-5955/85/$03.30

0 1985 Elsevier Science Publishers

[5,28,33]. Whether these two groups of neurons actually represent discrete classes of cells is, however, currently unclear. The consequences of intensity function shape for the organization of a cell’s ‘response area’ (the total frequency-intensity domain within which a pure tone must fall in order to elicit spike discharges) has not previously been studied in the cat’s AI. There has also been little evidence reported on the presence and/or nature of the inputs that a cortical neuron might receive from the frequency-intensity domains that lie outside the excitatory response area. A partial answer to the latter question might be found in a comparison of a neuron’s responses to stimuli of differing spectral bandwidths. In the present study, we explored the response areas of AI neurons with tones and wide-spectrum noise stimuli. We have obtained a number of lines of evidence suggesting that neurons with monotonic and nonmonotonic pure tone spike count functions might indeed be discrete

B.V. (Biomedical

Division)

74

classes of neurons. presented [32].

Preliminary

data have been

Successful acute experiments were conducted on 11 adult cats shown by preliminary otoscopic examination to have outer and middle ears free from infection. Surgical anesthesia was induced with sodium pentobarbital (35 mg/kg i.p.) and was supplemented with additional doses administered to maintain a state of areflexia. All cats were administered a single dose of dexamethasone sodium phosphate (0.3 mg/kg i.m.) as a prophylactic against cerebral edema. The trachea was cannulated and the pinnae were removed bilaterally, leaving a short stub of transected external auditory meatus for insertion of the stimulus delivery systems. The left temporal musculature was removed, and the skull overlying the left ectosylvian gyri was opened using a trephine. The dura mater was cut and reflected, and a lucite chamber was cemented around the exposure. The chamber was filled with warmed dimethylpolysiloxane oil and the cortex was photographed so that electrode penetration sites could be marked in relation to cerebrovascular landmarks on a 20 x or 40 x working print. Rectal temperature was maintained at 37.5”C using a thermostatically controlled blanket.

The stimulus delivery system for each ear incorporated a calibrated probe microphone assembly for in situ acoustic calibrations. These calibrations were obtained for both tones and noise in each ear cavity, in 100 Hz steps from 0.1 to 30.5 kHz, using an on-line LINC computer and a General Radio 1523-P4 wave analyzer. Measurement bandwidths (3 dB points) were 10 Hz for frequencies below 1.0 kHz and 100 Hz for higher frequencies. All intensities are expressed in dB sound pressure level (SPL: dB re 20 PPa). For the noise stimuli, spectrum levels i.e., the acoustic energy contained in 1.0 Hz-wide bands, were obtained by correcting the wave analyzer’s SPL measurements by 10 times the logarithm of the measurement bandwidth. The in situ noise spectrum showed a number of peaks and troughs, particularly at frequencies above 10.0 kHz, and these were sometimes in the order of f 12 dB. Stimulus repetition rates varied from 1 per 2 s to 2 per s according to the rate sensitivity of the neurons examined. The responses of single neurons were recorded extracellularly using glass pipettes filled with a saturated solution of fast green dye in 2 M NaCl. Electrode impedances were in the range from 1.8 to 4.8 MS2, measured at 1.0 kHz. Electrodes were advanced using a Davies microdrive fitted into a glass plate which hydraulically sealed the lucite chamber. Stimulus and response event times were digitized (10 ps resolution) and stored on-line by the LINC computer.

Stimulation

Experimental

Methods Animal preparation

and recording

Detailed descriptions of the stimulus generation and measurement systems have been provided in previous reports from this laboratory [2,5] and will be treated only summarily here. The cat was located in an electrically shielded sound-attenuating room. Continuous tones (generated by a General Radio 1309-A oscillator) and wide-spectrum (20 Hz-200 kHz) noise (Grason-Stadler 455-B) were shaped to 100 ms duration including 5 ms rise-decay times. The shaped stimuli were amplified and led to separate input channels of each of two digital attenuators. The stimuli were transduced by Beyer DT48S earphones which were connected by short lengths of polyethylene tubing to acoustic couplers whose speculae were sealed into the transected ear canals with Audalin ear impression compound.

protocol

Area AI was identified by the generation of partial maps of the distribution of single neuron and neuron cluster CFs across the crown of the middle ectosylvian gyrus (after [20,28]). When a single neuron was isolated, its CF was determined audiovisually, by determining the tone frequency requiring the lowest SPL for an excitatory response. Rate intensity functions were then obtained for CF tones, noise, and then for tones divergent from CF, and of frequencies selected to sample as much of the neuron’s response area as possible. These data were collected using intensity increments of 5 or 10 dB (depending on the range of intensities over which the neuron was responsive) and using linear increments in frequency, the size of which depended on the neuron’s breadth of

75

tuning. Spike count data were generally based on 50 trials at each frequency-intensity combination, and were gathered using stimuli presented monaurally to the contralateral ear; for those neurons whose discharges were significantly facilitated by binaural stimulation, data were obtained using binaural, equally intense stimulation of the two ears. Results This report is based on two bodies of experimental data. We begin by presenting quantitative data on the responses of 61 single neurons which were studied in detail for their responsiveness to both tones and noise. We shall then briefly describe the outcome of physiological mapping experiments concerned with the distribution of cell types distinguished in the first sections. The mapping experiments included observations of 83 single neurons and 329 unit clusters. Monotonic and nonmonotonic cell classes Spike count-versus-intensity functions for CF tones and/or response area data were obtained for 61 AI neurons. Intensity function data for 10 representative neurons studied with CF tones are presented in Fig. 1. In panel A are shown data for cells whose intensity functions conform to earlier descriptions of ‘monotonic’ profiles [5,28], while data for cells fitting the description of ‘nonmonotonic’ are shown in panel B. Each curve has been normalized to its maximum to facilitate comparison within and between the panels. Neurons with monotonic intensity functions displayed spike counts that increased from threshold over a lo-40 dB range, and which usually tended towards a saturated discharge level that was retained with further increments in stimulus level. In contrast, cells with nonmonotonic intensity functions (Fig. IB) showed spike counts that increased from threshold, reached a maximum, and then declined. Best SPLs (i.e., CF tone intensities associated with maximal discharge rates: after Brugge and Merzenich [6]) ranged from 15 to 80 dB. It is unlikely that the nonmonotonicity in these functions was attributable to activation of an inhibitory input from the non-stimulated ear by acoustic crossover for two reasons. First, spike count max-

ima, and therefore the stimulus levels at which spike counts began to decline, were typically within lo-40 dB of threshold, while the acoustic separation of the ears in the sealed stimulating systems used in this study was generally better than 50 dB (as revealed by cochlear microphonic measurements in a separate series of experiments: Chan et al., unpublished). Second, the shape of a cell’s spike count function was independent of the cell’s binaural interactions; for at least some of these cells, simultaneous stimulation of the ipsilateral ear enhanced spike counts rather than suppressed them. To provide an indication as to whether monotonic and nonmonotonic neurons represented discrete cell classes or members drawn from a continuous distribution, we measured the amount by which their spike counts fell. from their respective maxima at the highest intensity with which the cells were tested (in practice, at least 50-60 dB above threshold). These data are shown in Fig. 2 which depicts in histogram form the distribution of neurons characterized by the extent to which their spike count functions ‘turned over’ at high stimulus levels. This distribution is bimodal, with the majority of cells displaying either less than 30% or more than 70% reductions in spike count at high stimulus intensities. For the purposes of the present study, an arbitrary criterion of 50% reduction in spike count was used to allocate cells to the monotonic or nonmonotonic cell classes (arrows in Fig. 2). It is possible that a small number of cells classified here as ‘monotonic’ would in fact have been assigned to the ‘nonmonotonic’ group had they been tested at sufficiently higher stimulus SPLs. However, given the strong bimodality of the distribution in Fig. 2, and the fact that monotonic cells were generally tested over quite wide intensity that there has been any ranges, it is unlikely serious contamination of the two cell groups. Response areas of monotonic and nonmonotonic neurons The shape of a neuron’s rate intensity function for CF tones typically also characterized the cell’s discharges for tone frequencies divergent from CF. The consequences of intensity function shape for the shape of a neuron’s response area were quite different in the two cases. Fig. 3 presents sche-

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Fig. 2. Distribution of 61 AI cells studied with CF tones, plotted as a function of the extent to which their spike count functions ‘turned over’, i.e., became nonmonotonic, at high stimulus levels. Arrows indicate criteria used to allocate cells to monotonic and nonmonotonic categories. Explanation in text.

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Fig. 1. Normalized spike count-intensity functions for 5 representative monotonic (A) and 5 nonmonotonic (B) cells. Data obtained using 50 presentations of CF tones delivered either monaurally to the contralateral ear, or to both ears simultaneously.

matic depictions of the excitatory response areas of three monotonic (A-C) and three nonmonotonic (D-F) cortical cells. The boundaries of these were defined as those areas response frequency-intensity conjunctions at which spike counts fell to less than 5% of the cells’ respective maximum discharge rates. The shaded and stippled areas, respectively, indicate the frequency-intensity domains within which spike counts were greater than, or less than, 50% of maximum response rate. The arrows in A-C indicate that the neurons continued to discharge spikes at intensities exceeding those for which spike count data were collected. Fig. 3 shows the defining characteristics of the response areas displayed by the, two classes of cells. The monotonic neurons each had V-shaped response areas that were open-ended at high intensities, and were reminiscent of those describing the tips of cochlear nerve fiber tuning curves [36] and elements in the ventral cochlear nucleus [S]. Al-

though we have not tested many cells with SPLs greater than 85-90 dB, they appear to lack the low-frequency, high-threshold ‘tails’ that characterize many high-CF cochlear nerve fibers [17]. Cells with nonmonotonic rate-intensity functions. on the other hand, displayed response areas that were often completely circumscribed. Inspection of the panels D-F in Fig. 3 reveals that these cells responded to tonal stimuli over only narrow ranges of hot/t frequency and intensity. In practice, the complete circumscription of these response areas was a necessary consequence of the fact that these cells’ rate intensity functions fell close to zero at high SPLs, regardless of the tone frequency with which the cells were tested. Responses to wide-spectrum noise of monotonic and nonmonotonic neurons

Most cells whose rate intensity functions for CF tones were monotonic in shape were responsive to wide-spectrum noise. Fig. 4 presents detailed data on one such neuron. The larger part of this illustration shows dot rasters representing the discharges of neuron 83-72M-3 throughout the larger part of its response area, studied with tones delivered to the contralateral ear alone. Each raster depicts spike discharge event times, for each of 50 trials, shown in relation to the time course of a tone whose frequency and intensity are specified by the column and row, respectively, in which the raster is located. Within each raster, N indicates the number of spikes contributing to the response and x indicates the mean latent period to first

77

right-hand column of rasters in Fig. 4. Noise intensity is expressed as the spectrum level at the cell’s CF. Inspection of these data reveals that the cell’s spike counts for noise stimuli were a predominantly monotonic function of noise intensity, and like its responses to tones, noise evoked responses had latent periods that were inversely related to SPL. The apparent difference in the absolute sensitivities of the unit to the two stimuli (the spectrum level of a threshold noise stimulus was more than 60 dB lower than the SPL of a threshold CF tone) presumably reflects the fact that the cell was responding to spectral energy in the noise summed across the bandwidth of its frequency tuning curve. particularly since there

spike, expressed in milliseconds, for the trials in which spikes were elicited. This neuron had its CF near 15.8 kHz, and its spike counts were a monotonic function of sound pressure level at each tone frequency with which the cell was tested. The cell’s thresholds were higher for frequencies divergent from CF, imparting a V-shape to the cell’s excitatory response area. Latent periods at any given SPL were shortest for responses to CF tones and, at any given frequency, latent periods were inversely related to SPL, declining towards asymptotic minima at high intensities. This neuron’s responses to wide-spectrum noise over a 60 dB range of SPLs are illustrated by the

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Fig. 3. Schematic depictions of the excitatory response areas of three monotonic (A-C) and three nonmonotonic (D-F) neurons. Shaded and stippled areas, respectively, indicate the frequency-intensity domains within which tonal stimuli evoked responses whose strengths were within, or less than, 50% of maximum response. Arrows in A-C indicate that the neurons continued to discharge spikes at intensities exceeding those for which spike count data were collected.

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Fig. 4. Response area data (left) and responses to noise (right) of neuron 82-72M-3, obtained using monaural contralateral stimuli. Dot rasters represent the timing of the cell’s spike discharges shown in relation to the stimulus duration, and are based on 50 repetitions of stimuli whose frequency and intensity are specified by the column and row in which the raster is located. Inset shows the spectrum of an unattenuated noise stimulus in the ear cavity to which the stimuli were presented. N indicates the number of spikes contributing to the raster, and x indicates the mean latent period to first spike. Description and explanation in text.

no peaks in the noise spectrum (inset) near this cell’s CF that could account for the discrepancy between the two thresholds. For Q/44 neurons responsive to both tones and noise, threshold noise stimuli had spectrum levels that were 10 to 40 dB lower than the cell’s lowest tone thresholds. Fig. 5 presents data for neuron 83-63M-13, whose spike counts were a strongly nonmonotonic function of SPL. This neuron’s CF was near 17.0 kHz, and its response area was particularly narrow, being only 1500 Hz wide at its broadest point. Every tone frequency with which this neuron was tested elicited spike disoharges that were a nonmonotonic function of SPL, falling to zero at were

high stimulus levels. As mentioned earlier, this had the consequence of generating a completely circumscribed response area. This neuron, and many like it (see below) was completely unresponsive to wide-spectrum noise stimuli of any sound pressure level (Fig. 5, right). The rasters for this cell’s noise responses cover a 40 dB range of SPLs, and it is clear that wide-spectrum noise was ineffective in eliciting spike discharges. The particularly relevant feature of this range of noise intensities is that it includes that where the spectrum level at the cell’s CF would otherwise have been optimal for eliciting spikes (20 dB) and that where the total acoustic energy in the bandwidth of the cell’s tuning curve would have been close to 20 dB (ie., where

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the spectrum level at CF would have been near - 5 dB). Not all nonmonotonic neurons totally failed to respond to noise; for most of these cells, wide-spectrum noise usually evoked weaker

responses that were noise level, and the noise spectrum levels Table I presents

a nonmonotonic function of cells responded only when were very low. summary data on the noise

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responsiveness of cells classified as monotonic or nonmonotonic on the basis of their responses to CF tones. Column 1 presents data for the monotonic cells in our sample. 32 of 34 monotonic cells were responsive to noise, and of these, over half had monotonic rate-intensity functions for noise. In contrast, the data in the right-hand column indicate that only 45% of cells with nonmonotonic spike count functions for CF tones were responsive to noise. Those elements of this group that did respond to noise almost always did so with a nonmonotonic dependence of spike count on noise intensity. The data in this table were analyzed with the chi-squared statistic to determine whether responsiveness to noise was significantly associated with tone intensity function shape. That analysis confirmed that the two response properties were significantly associated (chi-squared = 28.6. P < 0.001). Latent periods The mean latent period to first spike was an

inverse function of the intensity of a CF tone for both monotonic and nonmonotonic neurons. Typically, response latencies tended towards limiting minima when stimulus SPLs exceeded about 30 dB above CF threshold. Latency-versus-intensity functions, obtained using CF tones generally delivered to the contralateral ear alone are presented in Fig. 6A and B for monotonic and nonmonotonic cells, respectively. Data are presented for five cells in each panel, and it is apparent that cells in both groups displayed latent periods that declined towards asymptotic minima. For those nonmonotonic neurons responding to CF tones over only

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Fig. 6. Latency-intensity functions for 5 representative monotonic (A) and 5 nonmonotonic (B) cells studied with CF tones. Note that both groups of neurons display responses whose latent periods decline towards asymptotic minima, and that this property is common to even thou nonmonotonic cells that responded over only narrow ranges of intensities (solid symbols in B).

narrow ranges of SPLs (e.g.. the cells whose responses are indicated by the solid stars and circles in Fig. 6). latent periods typically also tended towards limiting minima, despite the abbreviated range of SPLs over which the cells responded. These data were obtained for 61 cells. Minimum latent periods for each of these neurons are shown, separately for monotonic and nonmonotonic groups, in Fig. 7. For monotonic cells (solid outline) minimum latent periods for CF tones were generally in the range from 11 to 17 ms, while nonmonotonic cells (stippled histogram) usually had longer latent periods. The mean (+ S.D.) minimum latent period for monotonic cells (14.0 f 2.9 ms) was shorter than that for nonmonotonic cells (19.1 f 4.0 ms) by over 5 ms. and this difference was statistically significant (r = 5.76, P -c 0.01). Both the difference between the means, and its statistical significance, were retained at all levels above threshold. Thus, at 10 dB

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above threshold, the mean latent periods for monotonic and nonmonotonic cells were respectively 19.9 and 27.4 ms (t = 5.1, P < O.Ol), and 30 dB above threshold, the means for the two groups were 15.2 and 20.6 ms (t = 5.5, P < 0.01). The slopes of the latency functions, measured between 10 and 30 dB above threshold, were, however, not significantly different, being 0.24 ms/dB for monotonic cells and 0.34 ms/dB for nonmonotonic cells (t = 1.8, P > 0.05). These data indicate that the shapes of the latency functions for the two groups were similar, with the exception that nonmonotonic cells had latent periods between 5 and 7 ms longer than those for monotonic cells at all stimulus levels. For the majority (41/44) of neurons responsive to both tones and noise, minimum latencies for noise were within l-4 ms of those for tones. The respective mean minimum latent periods for CF tones and noise bursts were 15.4 and 15.8 ms, and these were not significantly different (2 = 0.4, P b 0.05). Segregation of monotonic and nonmonotonic neurons within AI In 4 experiments, we studied the topographic

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distribution of monotonic and nonmonotonic neurons in the middle cortical depths by employing relatively dense (up to 40 penetrations/mm2) micromapping procedures. In each penetration, single units or unit clusters were examined audiovisually at 2-4 loci disposed in depth between 500 and 1200 pm beneath the pial surface. The neural activity at those locations was classified into one of three groups. A cortical locus was classified as ‘nonmonotonic’ if neural response strength was significantly reduced at high SPLs. The second classification was ‘monotonic’, and was used when there was little or no apparent difference in the vigour of responses elicited by CF tones at high SPLs (80-90 dB) and that of responses to tones 30-40 dB less intense. The final category was termed ‘mixed’, and was used when different cells at a single recording site, or different sites within a single penetration revealed activity of both the above kinds. In some cases, discriminations were based on spike count data. In the case of distinctions based on unit cluster responses, there is some danger that the presence of nonmonotonic cells may have been masked by that of monotonic cells. While this phenomenon may have resulted in

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Fig. 8. Schematic illustration showing the distribution of three categories of neural activity across a small region of Al (left) shown in relation to this cat’s cortical fissural pattern (right). Dashed lines indicate the orientation of iso-frequency contours, i.e.. lines of cells with similar, indicated CFs (expressed in kHz). Symbols and other details described in text.

some contamination of our data, we generally could discriminate spikes from two or more cells at a single locus on the basis of spike amplitude and latency. Moreover, it is clearly the case that nonmonotonic cells cannot mask the simultaneous presence of monotonic cells: any neuron cluster that fails to respond to CF tones at high SPLs while responding to lower intensity CF signals. must contain only nonmonotonic elements. A schematic illustration of a partial map of Al in cat 83-129M is shown in Fig. 8. In this experiment, one small section of AI, about 2.5 mm long and l-2 mm wide, whose location in relation to the cortical fissural pattern is shown on the right, was studied in detail. The symbols indicate the nature of the activity recorded at those locations. Open stars indicate the presence of only nonmonotonic cells, filled circles indicate the presence of only monotonic neurons, and the circles enclosing stars indicate the presence of both kinds of cells. The dashed lines indicate the orientation of ‘isofrequency contours’, i.e., lines of cells with similar, indicated CFs (expressed in kHz). In this cat, there was a relatively large zone in AI, extending from the lO- to the CkHz isofrequency contours, that contained exclusively nonmonotonic neurons. Be-

yond this region of nonmonotonic units, but in the same isofrequency domains, both the other kinds of activity were observed. In this cortex, and in the other three that were studied less extensively, it was our impression that the ‘mixed’ zone usually was located between those regions occupied by exclusively monotonic or exclusively nonmonotonic cells. Because our observations are restricted to the middle cortical depths, it is unclear whether this observation reflects non-vertical penetration angles, or that the ‘seeing distance’ of the electrode was sufficiently great that recording sites at the boundary between two zones might have recorded from both of them. Nevertheless, the data in Fig. 8 present evidence for a topographic segregation of the two cell classes, and therefore a further validation of the distinction between them. Diseusaion Relation to previotu auditory cortex

single unit studies of the cat

This study has presented evidence concerning the properties of single neural elements in two classes of cells distinguished on the a priori ground of the shapes of their rate intensity functions for

83

CF tones. Our experiments have revealed that the shape of the rate intensity function seen for CF tones is typically preserved throughout a cell’s response area, and that the two cell classes were further distinguished by their responses to widespectrum noise, by their response latencies, and by their horizontal segregation across AI’s middle cortical layers. Both monotonic and nonmonotonic cells have been previously described for the cat’s auditory cortex [5,7,9,28,30,33], although relatively little attention has been paid to the consequences of intensity function shape for the organization of a cortical cell’s response area [5,33]. Cortical neurons that displayed monotonic rate intensity functions for CF tones usually did so throughout their frequency response areas which consequently took on a V-shaped, open-ended conformation whose outline was reminiscent of the tips of cochlear nerve fiber tuning curves [17]. In contrast, nonmonotonic cells tended to have circumscribed excitatory response areas because their spike counts often fell to zero at high SPLs. In this regard, the cortical neurons whose frequency-amplitude selectivity most closely resembled that of cochlear nerve fibers [36.37] were those whose latent periods were the shortest. On these grounds, it is tempting to speculate that the afferent pathway to the nonmonotonic cortical cells, i.e., the cortical cells showing the most extensive transformations of cochlear nerve fiber response areas, contains a greater number of synapses. This suggestion must currently remain speculative because response latency data obtained from extracellularly recorded spikes do not address the possibility that the prolonged latent periods of nonmonotonic cells reflect a release from a preceding inhibitory input as well as the onset of an excitatory one. Goldstein and his colleagues [10,35] and Nomoto [26] have previously reported the presence in the auditory cortex of cells that responded to tones and noise, and of other cells that responded to tones but not to noise. This study has extended the observations of these earlier authors by providing evidence on the other properties of cells in these two groups. This evidence enables some tentative conclusions to be drawn regarding the neural mechanisms that might be responsible for the differences between the cell groups. Any nonmono-

tonicity in a cortical cell’s pure tone rate intensity function must be produced by central neural inhibitory processes since cochlear nerve fibers display exclusively monotonic rate intensity functions [17,36,37]. (Young and Brownell [40] and Voigt and Young [39] have provided detailed evidence on how this may have been achieved in the decerebrate cat’s dorsal cochlear nucleus.) The highintensity domains lying outside a nonmonotonic cortical cell’s response area are, therefore, probably occupied by inhibitory frequency-intensity domains. The present study revealed that many such neurons fail to respond to wide-spectrum noise of any SPL. This finding suggests that even low intensity noise stimuli were simultaneously activating excitatory and inhibitory inputs to the cells, since the cells would otherwise have responded to those spectral elements of the noise known from pure tone studies to provide an excitatory input. It seems likely, then, that the highand/or low-frequency borders of these cells’ response areas are also flanked by inhibitory domains. Whether the afferent neurons responsible for the high-intensity inhibition at CF are the same elements that confer the more sensitive inhibitory influences at frequencies divergent from CF is currently unclear. At least some cells with monotonic rate intensity functions at CF failed to respond to wide-spectrum noise (Table I). This finding is compatible with, although not definitive of, there being separate sources for the inhibition seen at high SPLs at CF, and for the inhibition that we suggest flanks the excitatory response areas of nonmonotonic cells at lower SPLs. Irrespective of the source(s) of these inhibitory inputs, however, the presence of these flanking inhibitory domains is consistent with the early observations of Evans and Whitfield [9] and Goldstein et al. [IO] in the auditory cortex of the awake cat. Both of those groups have provided qualitative descriptions of cortical neurons whose excitatory response areas were flanked on the high-intensity and/or lowand/or high-frequency sides by frequency-intensity domains associated with a suppression of ongoing spontaneous discharges. Implications for stimulus processing in, and functional organization oj the cat’s primary auditory cortex

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The nonmonotonic relation between a cortical cell’s spike counts and the intensity of an acoustic signal typically has been interpreted in the context of a neural code for stimulus intensity [6,28,33,38]. Recently, attention has focussed not so much on the precision with which a neuron’s spike counts might specify a stimulus in the intensity domain, but rather on the ensemble of cortical elements activated by any specified stimulus ([33,38]; see also [24]). Thus, since nonmonotonic cells differ in the signal intensities to which they respond most vigorously, the population of cells activated by any given stimulus depends on the sound pressure level of that stimulus. Our data, and those of Greenwood and his colleagues [11,12] in the anesthetized cat’s cochlear nucleus, suggest that associated with selectivity to signal intensity may be sensitivity to spectral bandwidth. Greenwood et al. described the responses of cells with circumscribed response areas, of the kind reported here, to noise signals of constant spectrum level but varying spectral bandwidth. They found that maximal responses to noise stimuli were obtained when the spectrum of band-passed noise was centered near CF and did not significantly exceed the width of the cells’ excitatory response areas. These observations suggest that the greater the noise bandwidth, the more likely was the noise spectrum to intrude into flanking inhibitory domains. These cells responded poorly, if at all, to wide-spectrum noise. We have not studied cortical cells with band-passed noise, but the correspondence of unit properties that exists between the cells described in this report and those in the studies of Greenwood and Goldberg [ll] suggests that the neural mechanisms conferring those properties might be similar at the two central loci. Whether this correspondence reflects the preservation in the cortex of cochlear nucleus cell properties, or multiple sites of convergence of excitatory and inhibitory inputs along the auditory neuraxis, remains to be determined. These considerations reveal two ramifications of the present data for the ‘population’ hypothesis of stimulus representation in the auditory cortex. The first is that the spectral bandwidth of an acoustic signal should be added to the other stimulus parameters known independently to influence the discharges of feline AI neurons, and

therefore the ensemble of neural elements activated by a given stimulus (viz., frequency [21,283, intensity [5,28], laterality [5,19,29,30] and location [22]). The second is that a single neural mechanism, in this instance, the acquisition of flanking inhibitory domains around the excitatory response area, might impart selectivity to more than one parameter of acoustic stimulation. Reale, Imig and Sinex [34] were first to provide evidence on a corollary of intensity selectivity among AI cells, namely that there might be a topographic segregation of monotonic and nonmonotonic cell types. Those authors were primarily concerned with the intensity selectivity of neurons in the various ‘binaural suppression columns’ [14,15]. They found that nonmonotonic cells were more common in the ventral suppression column than in the more dorsal ones. The present study confirmed the apparent horizontal segregation of monotonic and nonmonotonic cells in AI. Although we did not collect quantitative data on the binaural interactions of the cells in the two classes, we routinely examined the binaural sensitivity of the neurons so that we could study them quantitatively using stimuli of their preferred lateralities. It was our impression, based on these recorded observations, that the zones of Al occupied by monotonic and nonmonotonic neurons did not respect the boundaries of binaural columns. This observation is consistent with previous incidental findings that both monotonic and nonmonotonic cells may exhibit most or all of the binaural interactions thus far described for cortical cells [5,29,31,33]. From the purely intuitive standpoint, nonaligned cortical maps for stimulus features would appear to be necessary for normal hearing, since the detection of any one stimulus feature (e.g., intensity) would otherwise only be possible if that feature was in conjunction with another (e.g., left ear). General anesthesia the results

and the functional

significance

of

One qualification of the present data might stem from the fact that the data were gathered from cells in barbiturate-anesthetized cats. An attendant difficulty with this preparation is that the supragranular layers are often inactive, so that extracellular recording methods are unable to

85

visualize the complete range of cortical afferents, since these often show a laminar distribution [15,16]. Accordingly, whether the zones of AI found in this study to contain predominantly monotonic or nonmonotonic neural elements actually represent physiological columns that span all cortical strata is not clear. Nevertheless, if these properties were restricted solely to those cells in the middle cortical layers (in direct receipt of thalamic afferents), then the present data, and those of Reale et al. [34] would point to a topographic segregation of monotonic and nonmonotonic inputs to Al. A second qualification of the present data that derives from the use of general anesthesia concerns the extent to which the stimulus selectivity of cells in the anesthetized animal accurately represents that of neurons in the awake animal. There is abundant evidence that the discharge rates and discharge patterns of auditory cortex neurons may vary with behavioral state in both cats and monkeys [3,6,18,23], but there is little or no evidence that directly addresses the question of stimulus selectivity. Most of the cell types distinguished by frequency-intensity selectivity and binaural interactions in the anesthetized cat have also been described in the awake cat and monkey [6,9,13,19,27]. Moreover, those studies that have addressed the effects of behavioral performance on the discharges of auditory cortex cells have often described effects on spike discharge rate or temporal pattern, but have provided less evidence for effects on stimulus selectivity [3,4,23,25]. On the other hand, not all of the physiological cell types described for the awake cat’s auditory cortex have been seen in the anesthetized cat (cf. [l,lO,ZS]). Taken together, these data suggest that general anesthesia might silence some cells, and that this effect is probably laminar dependent. These data also suggest that the stimulus selectivity of the cells that one does see in the anesthetized cat’s cortex is probably a reliable indication of that which would be seen in the awake animal, even if their discharge patterns are not. Acknowledgements We express our special thanks to Dr. J.F. Brugge for encouraging us in this research in his laboratory. Dr. C.-M. Huang took part in some of the

We thank Drs. J.F. Brugge, M.S. experiments. Cynader, C.D. Geisler, J.E. Hind, D. Oertel and T.C.T. Yin for their incisive comments on previous presentations of this work. We thank Ellen Burleigh and Shirley Hunsaker for their artistic and photographic work. This work was supported by NSF Grant BNS7912939 and NIH Grants HD03353, NS12732 and NS07026. D.P.P. was supported by NIH International Research Fellowship F05 TW03102. References M.H. Jr. (1970): Functional 1 Abeles, M. and Goldstein, organization in cat primary auditory cortex: columnar organization and organization according to depth. J. Neurophysiol. 33, 172-187. D.J. and Brugge, J.F. (1970): 2 Aitkin, L.M., Anderson, Tonotopic organization and discharge characteristics of single neurons in nuclei of the lateral lemniscus of the cat. J. Neurophysiol. 33, 421-440. 3 Beaton, R. and Miller, J.F. (1975): Single cell activity in the auditory cortex of the unanesthetized, behaving monkey: correlation with stimulus controlled behavior. Brain Res. 100, 543-562. 4 Benson, D.A., Hienz, R.D. and Goldstein, M.H. Jr. (1981): Single unit activity in the auditory cortex of monkeys actively localizing sound sources: spatial tuning and behavioral dependency. Brain Res. 219, 2499267. 5 Brugge, J.F., Dubrovsky, N.A., Aitkin. L.M. and Anderson. D.J. (1969): Sensitivity of single neurons in the auditory cortex of cat to binaural stimulation: effects of varying interaural time and intensity. J. Neurophysiol. 32. 1005-1024. M.M. (1973): Responses of 6 Brugge, J.F. and Merzenich, neurons in auditory cortex of the macaque monkey to monaural and binaural stimulation. J. Neurophysiol. 36. 1138-1158. 7 Erulkar, S.D., Rose, J.E. and Davies, P.W. (1956): Single unit activity in the auditory cortex of the cat. Bull. Johns Hopkins Hosp. 99, 55-86. 8 Evans, E.F. and Nelson, P.G. (1973): The responses of single neurones in the cochlear nucleus of the cat as a function of their location and anaesthetic state. Exp. Brain Res. 17, 402-427. 9 Evans, E.F. and Whitfield, I.C. (1964): Classification of unit responses in the auditory cortex of the unanaesthetized and unrestrained cat. J. Physiol. (London) 171. 476-493. 10 Goldstein, M.H. Jr., Hall. J.L. and Butterfield, B.O. (1968): Single unit activity in the primary auditory cortex of unanesthetized cats. J. Acoust. Sot. Am. 43, 444-455. D.D. and Goldberg, J.M. (1970): Response of 11 Greenwood, neurons in the cochlear nucleus to variations in noise bandwidth and to tone-noise combinations. J. Acoust. Sot. Am. 47, 1022-1040.

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