coding of tone-pulse amplitude by single neurons in auditory cortex of albino rats (rattus...

Hearing Research, 31 (1989) 269-280 Elsevier

269

HRR 01164

Coding of tone-pulse amplitude by single neurons in auditory cortex of albino rats (Rattus norvegicus)

D.P. Phillips ’ and J.B. Kelly 2 ’ Department of Psychology, Da/ho&e University, Halifax, Nova Scotia, Canada and ’ Department of Psychology, Carleton University, Ottawa, Ontario, Canada

(Received 13 June 1988; accepted 25 September 1988)

We examined the neural representation of tone pulse amplitude in the auditory cortex of anesthetized albino rats. Rate-level

functions for monaural, contraiateral CF tones were obtained from single neurons. Most of these functions were saturating and

monotonic in shape. The dynamic ranges of these functions were typically 5 to 35 dB in breadth, although the tail of this distribution

extended to beyond 60 dB. The neurons with the widest dynamic ranges were usually those with the lowest CF thresholds.

Nonmonotonic neurons were uncommon, and the nonmonotonicity was not as well developed as that seen in the cortex of cats and

monkeys. For two individual rats, data are presented for neurons tuned to the same tone frequency in a single cerebral hemisphere.

The CF thresholds of these neurons varied over a 50 dB range, and their collective dynamic ranges spanned at least 70 dB. These data

provide a minimum estimate of the dynamic range of the cortical code for tone pulse amplitude in the rat. They suggest that there may be no serious mismatch between the neural and behavioral amplitude dynamic ranges.

Rat; Auditory cortex; lntensity code; Dynamic range

In~~uction

The auditory system of the albino rat, Rat&s noruegicus, has received considerable attention. Behavioral experiments show the rat to have a broad behavioral audiogram, with maximum sensitivity in the frequency range from 8 to 40 kHz, where tone thresholds may be as low as 0 to 10 dB sound pressure level (Kelly and Masterton, 1977). Its frequency discrimination, whether indexed by critical ratio measurements (Goure- v-itch, 1965) or by frequency difference thresholds (Fay, 1974; Heffner et al., 1971) is poorer than that of cats, but only slightly poorer than that of other rodents (Heffner and Masterton, 1980). Its intensity difference threshold, for tone amplitude increments imposed on a 30 dB suprathreshold pedestal, is in the order of 1.5 to 4.5 dB (Kelly,

Correspondence to: Dr. D.P. Phillips, Department of Psy-

chology, Dalhousie University, Halifax, Nova Scotia, Canada

B3H 451.

1970). While the rat’s behavioral dynamic range has never been measured directly, its absolute sensitivity to tonal stimuli and its sensitivity to amplitude increments, suggest a general similarity to the human dynamic range. The albino rat’s spatial hearing has been examined (Heffner and Heffner, 1985; Kavanagh and Kelly, 1986; Kelly, 1980; Kelly and Glazier, 1978): minimum audible angles for midline sound localization are in the order of 9 to 13 degrees azimuth, which is comparable to the performance of other small mammals (Heffner and Heffner, 1984). In contrast, its localization performance in the lateral hemifields is very poor (Kavanagh and Kelly, 1986), at least as compared to cats (Jenkins and Masterton, 1982), ferrets (Kavanagh and Kelly, 1987), monkeys (Brown et al., 1982) and man (Mills, 1958). Whether this is unique to the albino rat or common to other rodents (or small mammals, cf. Heffner and Heffner, 1987) is un- known.

There is a parallel history of research on the anatomy and physiology of the rat’s central audi-

0378-5955/89/$03.50 0 1989 Elsevier Science Publishers B.V. (Biomedical Division)

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tory nervous system (eg., Molier, 1972; Rees and Moller, 1983; Coleman and Clerici, 1987; Beyerl,

1978; Winer and Larue, 1987). While some of these studies have been performed using other species in the Myomorph sub-order, they have

revealed that the general organization and physiology of the rat’s auditory nervous system follows a basic plan common to other mammals (cf. Aitkin

et al., 1984). Insofar as the auditory cortex is concerned, the rat shows patterns of reciprocity and topography in thalamocortical connectivity that are similar to those described for cats (cf. Andersen et al., 1980; Ryugo and Killackey, 1974; Vaughan, 1983; Winer and Larue, 1987).

Microelectrode recording techniques have been

used to explore the physiological organization of the albino rat’s auditory cortex (Kelly and Sally, 1988; Sally and Kelly, 1988). These studies revealed that the rat’s cortex has a primary field (AI) which contains binaurally-influenced neurons that are narrowly tuned to the carrier frequency of a tone pulse. In AI, these neurons are spatially arrayed in tonotopic fashion, with neurons tuned

to high tone frequencies located rostrally and neurons of low characteristic frequency (CF) located caudally. Interestingly, rat AI neurons might be less narrowly tuned than those in cats, an observation that may be associated with the relatively poor frequency discrimination in rats. Surround- ing AI is cortical tissue of less obvious tonotopic organization, in which at least some neurons are broadly tuned to tonal frequency.

The study presented here examined the coding of tone-pulse amplitude by cortical neurons in the albino rat. It was conducted with two goals in mind. The first was to complement the extant data on this species’ acoustic sensory cortex, and in so doing, to provide the first parametric account of sound amplitude coding in the cortex of a lissen- cephalic mammal other than the bat (Suga and Manabe, 1982). There already exists a broad base of parametric data on the rate response of cat cortical cells. Detailed observations of rat cortical neurons under similar stimulus conditions will enable a direct comparison of the two species.

The second was to examine the fashion(s) in which the cortical neural code for tone level spans the behavioral dynamic range. One reason for this interest is that although the factors which shape a

given cortical neuron’s sensitivity to tone level have been studied in detail (eg., Brugge et al., 1969; Brugge and Merzenich, 1973; Phillips, 1985,

1987, 1988) the intensity dynamic ranges of single cat and monkey neurons are usually very narrow by comparison with the behavioral one (Stevens and Guirao, 1967); the manner in which neural

dynamic ranges contribute to the behavioral one is therefore unclear (see also Evans, 1981; Liberman, 1978). In cats (Phillips and Irvine, 1981) ferrets (Phillips et al., 1988) and albino rats (Sally and Kelly, 1988) data collapsed across individuals suggest that cortical cells tuned to the same tone frequency may have thresholds that vary over a 30-40 dB range. Only in primates (Brugge and Merzenich, 1973) and in bats (Suga and Manabe, 1982) however, has quantitative evidence been presented that the cortical representation of signal level in a single individual might span the behaviorally relevant range. A second goal of the present study, therefore, was to ascertain in what

manner(s) the rat’s cortical code for tone level relates to the behavioral dynamic range.

Methods

Successful acute experiments were performed on 12 adult male Wistar albino rats with clean outer ears and body weights in the range from 240-500 g. Surgical anesthesia was induced by intraperitoneal administration of 3 ml/kg Equi- thesin (aqueous solution containing 4.26% w/v chloral hydrate, 2.12% magnesium sulfate and 0.96% sodium pentobarbital). A state of areflexia was maintained by smaller doses of Equithesin (0.5 ml/kg) administered every 30 to 45 min for the duration of the experiment.

The rat was supported in a head-holder that left the skull and pinnae free from obstruction.

The tissue overlying the left lateral skull was reflected and a craniotomy was performed. The dura mater was left intact. The external auditory meatus of the albino rat is relatively unconvo- luted, and permits direct insertion of a snugly-fit- ting stimulus delivery system. The rat’s body tem- perature was maintained using a heating pad.

Continuous tonal stimuli were shaped to 110 ms duration pulses, including 10 ms linear rise-fall times, and were presented at l/s. The shaped

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signals were passively attenuated, fed through a low output-impedance power amplifier, and led to the stimulus delivery system. Stimuli were trans- duced by a Pioneer SE-SOD driver in a shielded housing that opened into a small acoustic coupler whose speculum fitted snugly into the external auditory canal. This coupler contained a calibrated probe microphone assembly for in situ measurements of signal sound pressure level (SPL: dB re 20 PPa) close to the tympanum. The stimulus delivery systems were carefully fitted into both ears; the data that follow, however, were all obtained using monaural contralateral tones. Stimu- lus amplitudes were set in dB attenuation during the experiment, and were converted to dB SPL off-line.

The rat was located in a shielded, sound-at- tenuating room. An insulated tungsten microelectrode (1.4-2.4 Mf2 impedance at 1.0 kHz) was advanced perpendicularly through the dura mater using a micromanipulator, with the visual aid provided by a Zeiss operating microscope. The ex- tracellularly-recorded spike responses of single neurons were filtered (0.5 to 10.0 kHz), amplified (1000 x ), displayed on a stimulus-triggered oscilloscope, and continuously monitored on an audio amplifier. The responses of single neurons were distinguished using a Bak window discriminator; acceptance pulses were led to a Tracer Northern TN-1550 spike counter triggered by stimulus onset (bin width 500 ,LCS). This device collected the stimulus and response event times, and provided online peri-stimulus-time histograms (PSTHs) and spike count data. Acceptance pulses from the window ~sc~~nator also triggered the sweep of a second oscilloscope whose Y input was the analog-delayed spike. This configuration provided a continuous monitor of spike waveform.

When a single neuron was isolated, its threshold CF was determined audiovisually by lowering the tone pulse level until stimulus-d~ven responses were obtained at only a single tone frequency. A rate-level function was then obtained using CF tones. This involved obtaining PSTHs of the summed responses to 50 presentations of each of a number of CF tone levels. Typically, we first obtained PSTHs for tone level in 10 dB steps to define threshold and saturating tone amplitudes. We then tested the neuron with intervening tone

levels, so that the form of the rate-response curve could be defined with a resolution of 5 dB, and occasionally 2-3 dB. In most cases, we tested each neuron with some stimulus levels more than once so that we could ascertain response reliability. If responses were not reproducible to within about lo-20%. then the neuron was rejected from the sample. In other cases, rate-level curves were drawn through the means of the observations at each stimulus level. All the data that follow reflect responses to the onset of a tonal stimulus (usually within 40-50 ms of tone onset). This has the consequence that the rate-level functions we obtained might properly be regarded as depicting spike probability rather than spike rate. Response latencies were defined as the interval between tone onset and the peak bin of the resulting PSTH.

Results

Data base Spike-rate versus tone-level functions were ob-

tained from 110 cortical neurons. These neurons had CFs in the range 2.14 to 27.50 kHz, and all of them were within the region described as being auditory cortex in a previous study (Sally and Kelly, 1988). In 4 rats, data all came from neurons within AI as defined by its tonotopic organization; in two other rats, data came both neurons in tonotopic~ly-defined AI, and in cortical regions ventral and caudal to AI; in the remaining 6 rats, the allocation of neurons to one or other cortical field was not certain. Some of these latter neurons were apparently broadly tuned to tone frequency: their spike rates at any given stimulus attenuation tended to follow the peaks and troughs in the output of our stimulating system, although a single tone frequency to which such neurons were most sensitive could usually be determined. The rate response of these neurons was not readily dis- tinguishable from that of sha~ly-tuned cells, and we have not distinguished between them in what follows. The vast majority of units were recorded in the superficial and middle cortical depths.

Shapes of rate-level functions The rate-level functions of rat cortical neurons

varied along a continuum from monotonic to nonmonotonic. By ‘monotonic’, we mean that the sign

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62 82 102

,W E

R. R12-9

f?(i

40.

x- /“ 14 34 54 74

100-F

,,.R12-2

Fig. 1. Rate-level functions obtained using CF tone pulses in eight neurons of specified ID numbers. Each curve is based on

responses to 50 presentations of each stimulus condition, and has been normalized to its own maximum. Neurons in panels A-D

classified as monotonic; those in panels E-H were classified as nonmonotonic. Unit CFs were R3-10, 6.16 kHz; Rg-4 22.04 kHz;

R12-3. 10.81 kHz: R6-4, 12.3 kHz; R12-9. 10.71, kHz: R12-2, 10.3 kHz; R9-8, 5.86 kHz; R3-7, 6.0 kHz. The maximum response rates of these neurons were all in the range from 39 to 117 spikes/M trials.

of the gradient of the rate-response curve did not Fig. 1, A-D shows rate-level functions for 4 reverse over the intensity range tested. By ‘nonmonotonic neurons. Each of the curves shows the

monotonic’. we mean that the gradient of the normalized spike rate of the identified neuron,

rate-response curve did change sign at supra- each point representing responses to 50 repetitions

threshold tone levels, imparting, in extreme cases, of the CF tone level specified by the abscissa.

a bell-shape to that curve. These neurons had in common that their rate-re-

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sponse gradients did not change sign, but they varied widely in the widths of their dynamic ranges, and therefore in the slopes of their normalized functions. In some neurons (eg., R6-4: Fig. lD), spike rate increased from threshold over the entire intensity range tested, with little or no tendency toward firing rate saturation. Other neurons (eg., R3-10: Fig. 1A) showed a more limited dynamic range with firing rates remaining at saturated levels for higher CF tone pulse amplitudes. These were not mutually exclusive trends: many neurons displayed intermediate tendencies toward firing rate saturation over comparable ranges of suprathreshold tone levels (Fig. 1, B-C).

Nonmonotonic rate-level functions obtained from other rat cortical cells displayed similar het- erogeneity. Fig. 1, E-H, shows data for 4 neurons that illustrate this point. In some neurons, it was clearly the case that spike rates were maximal for a narrow range of tone levels, but that the decline in response rate at supramaximal stimulus levels was at best modest, and did not systematically progress with further increments in tone amplitude (eg., units R12-9 and R12-2 in Fig. I, E-F). Since tone intensities were tested in random order, it is unlikely that the maximal responses reflected a transient period of heightened sensitivity or excitability. Moreover, across the neural population, there appeared to be widely varying degrees of nonmonotonicity (Fig. 1, F-H). Rarely, some neurons showed a marked ‘tuning’ to tone level (cf. Phillips, 1988). Neuron R3-7 (Fig. 1H) was the most narrowly amplitude-tuned neuron in our sample. More commonly, declines in response rate at supram~mal stimulus levels were intermediate in strength.

One means of quantifying the nonmonotonicity of rate-level functions is to measure their width (in dB) at 50% of maximum response rate (Phillips and Orman, 1984; Phillips, 1988). This measure was inapprop~ate for rat cortical neurons because so few cells showed 50% respbnse reduction at high stimulus levels. An alternative is to measure the percent reduction in spike rate at the highest tone level with which a neuron is tested (Phillips and Orman, 1984; Phillips et al., 1985). This is a measure that can be applied to all neurons, and which has previously been used to separate monotonic from nonmonotonic cortical neurons. It

RAT N=81

EAT ~=61

20 PERCEdNOT

80 TL%NO”ER

100

Fig. 2. Distribution of percent turnover scores for rat (stippled histogram) and cat {soiid outline) cortical neurons. Abscissa defined as the extent to which spike rate declined from maximum at the highest SPL tested. N indicates sample size. Cat

data are redrawn from Phillips et al. (1985).

should be recognized that this measure is con- founded by the fact that since all neurons are not tested at the same suprathreshold levels, they have not had equivalent opportunity to express a nonmonotonic rate-response. In the rat data to follow, percent turnover scores were measured at a mean stimulus level 45.1 dB (S.D. = 14.9 dB) above threshold, and this level is slightly less than that used in a previous study in the cat (Phillips et al., 1985).

These data are shown in Fig. 2. The stippled histogram shows the proportions of rat cortical neurons showing various percent turnover scores (i.e., the extent to which spike rate declined from maximum). It is clear from inspection of these data that most rat cortical cells showed less than 20% turnover. For comparison, data obtained from a previous study of cat cortical cells are shown by the solid outline. Whereas in rats the dist~bution of turnover scores has a single peak at low scores, and a broad tail, the cat data are more bimodally distributed, with most cells showing less than 30% or more than 70% turnover. The two frequency distributions were compared using the cl&squared statistic, and were found to be signific~tly different (x2 = 36.66, df= 9, P -C O.Ol), suggesting that the paucity of rat neurons with high turnover scores was not due to chance.

Intensity dynamic ranges

The rate-level functions of 81 rat cortical cells reached saturation or clearly defined maxima over the tone amplitude ranges used, and were ex-

274

11)25 1 r-l RAT N=81 Ii 3 t I1 q CAT ~=70

LL 15 0 II I

izi 25 2 p-L , (

d;NAMIC30 RANG? (dB) 70

Fig. 3. Distribution of dynamic ranges measured from rate-level

functions of rat (stippled histogram) and cat (solid outline)

cortical neurons. Dynamic range defined as the SPL range over

which spike rates increased from 10 to 90% of maximum. N

indicates sample size. Cat data are redrawn from Phillips and

Hall (1986).

amined with sufficient resolution that we could measure their dynamic range widths. In keeping

with previous studies (Tsuchitani, 1977; Phillips and Irvine, 1981; Semple and Kitzes, 1987) we measured the 80% dynamic ranges of these cells, i.e., the tone level range over which spike rates (corrected for spontaneous level) increased from 10% to 90% of maximum response rate. These data are shown as the stippled histogram in Fig. 3. Most neurons had dynamic ranges between 10 and 30 dB. There exists, however, a broad tail to this distribution, which extends to over 60 dB; it was common to both monotonic and nonmonotonic neurons.

Data of this form have previously been presented for cat cortical neurons studied with (5 ms rise-time) CF tones (Phillips and Hall, 1986). For the purpose of comparison, the distribution of dynamic range widths for a sample of 70 cat AI neurons is shown as the unfilled histogram in Fig. 3. It is apparent that the cat data histogram is more peaked, and located over narrower dynamic range widths than is that for the albino rat. A &i-squared analysis revealed that the two frequency distributions were significantly different (x2 = 30.69, df = 12, P < 0.01).

We were interested to ascertain whether the width of a cortical neuron’s dynamic range was correlated with other physiological properties in the same neurons. This question was prompted by the finding in the cat’s auditory nerve that fibers of lowest threshold have relatively narrower in-

tensity dynamic ranges than the fiber population with higher thresholds (cf. Sachs and Abbas, 1974; Liberman, 1978). In the albino rat’s auditory cortex, this relationship was apparently reversed. Fig. 4A presents a scatter plot in which each neuron’s dynamic range width is plotted as a

function of its threshold at the test frequency (CF). Data are provided for all neurons with measurable dynamic ranges.

Inspection of Fig. 4A reveals that the neurons with the broadest dynamic ranges (greater than 40 dB) had low thresholds (- 11 to 29 dB SPL). In contrast, of neurons with higher thresholds (greater

than 40 dB SPL), only one had a dynamic range in excess of 30 dB. It might reasonably be argued

IA. N=81

60 t I .

I

1601

Fig. 4. (A) Relationship between dynamic range and CF tone

threshold. Each data point represents a neuron’s 80% dynamic range plotted as a function of that neuron’s CF threshold.

Note the tendency for the data to converge towards narrow

dynamic ranges for high-threshold neurons. N indicates sample

size. (B) Relationship between dynamic range and CF. Data

are for the same neurons as those in (A).

275

that some neurons of high threshold possessed broad dynamic ranges which extended beyond the output of our stimulating system, and were therefore not measurable. In 5 neurons with thresholds greater than 40 dB SPL, this was indeed the case, although this problem extended to 4 neurons with significantly lower threshold SPLs. The converse argument, however, cannot be made. That is, neurons with low tone thresholds might also have been expected to express their level sensitivity in narrow dynamic ranges. In practice, they did so only infrequently. This finding, taken in conjunc- tion with the observation that so many high- threshold units showed narrow dynamic ranges, leads us to conclude that the inverse relations~p between threshold and dynamic range width is real. Note that to the extent that the present estimates of dynamic range are biased, the error is

\ RI2 -3

RAT N=45

II [7 CAT N=64

Fig. 5. (A) Latency-intensity functions for 6 cortical neurons. Latent period defined as interval between stimulus onset and the peak bin of the resulting PST histogram. (B) Distribution of minimum latent periods for rat (stippled histogram) and cat (solid outline) cortical neurons. N indicates sample size. Cat

data redrawn from Phillips et al., (1985).

in the direction of narrowness. The contrast with the cat cortical data (Fig. 3) is, therefore, all the more striking.

Our data on the relation between dynamic range and neural CF are less clear. Fig. 4B shows for each neuron, the dynamic range width plotted as a function of CF. Neurons with dynamic ranges less than about 35 dB in breadth were found in all sectors of the frequency representation in the rat’s AI. Interestingly, however, the 8 neurons with the broadest dynamic ranges (greater than about 40 dB) each had CFs above 10 kHz, frequencies for which the rat shows maximum behavioral sensitivity (Kelly and Masterton, 1977). Thus, the largest neural dynamic range might occur for tone frequencies for which the rat has the greatest behavioral dynamic range.

The shapes of latency-intensity functions of rat cortical neurons are shown in Fig. 5A. In this illustration, tone level has been expressed in relative dB for purposes of clarity. Note that the latent periods were long at low stimulus levels, and declined towards limiting minima at high tone amplitudes. Interestingly, the minimum latent periods of rat cortical neurons were frequently rather short, and often under 10 ms. Minimum latent periods were obtained for 45 neurons, and the distribution of these is shown as the stippled histogram in Fig. 5B. Most of these neurons had minimum latencies in the range from 7 to 13 ms.

Again for purposes of comparison, minimum latency data for cat AI neurons (from Phillips et al., 1985) are shown on the same axes as the unfilled histogram. The data for the cat are more broadly distributed and are centered over longer ~n~urn latencies than are those for the rat. Not unexpectedly, a &i-squared analysis showed that these two distributions were significantly different

(x2 = 63.04, df = 21, P < 0.01). We analyzed our data for correlations between

latent period and two other response measures, and found no evidence of a relation in either case. First, there was no obvious association between ~n~urn latency and CF threshold. Most rat cortical cells had minimum latencies in the range from 7 to 13 ms, regardless of their absolute sensitivity. Second, there was no apparent relation

216

between minimum latency and dynamic range width. We also examined latent period as a function of neural CF, but our sampling of the frequency representation of AI for latent periods

was insufficiently systematic to warrant presen- tation here.

Single case analysis

A number of previous studies have shown that in data collapsed across individuals (Phillips and Irvine, 1981; Phillips et al., 1988; Sally and Kelly,

1988), and within some individuals (Sally and Kelly, 1988), neurons of the same CF may have CF thresholds that span a range of over 40 dB. The further question is to what extent the ampli-

tude dynamic ranges of neurons tuned to the same CF in a single individual span the behaviorally discriminable range.

We addressed this question in three of the rats in this study, by obtaining rate-level function for neurons tuned to similar CFs in a single cerebral hemisphere. Data for two of these rats are shown in Fig. 6. In both cases, for purposes of clarity, only the ascending portions of the rate-level functions are shown. Panel A shows the normalized rate-response curves of 6 neurons in rat No. 12, all of which had CFs between 10.0 and 10.81 kHz. The CF thresholds of these cells varied over a 20-25 dB range. The dynamic portions of the

functions, however, extended across a 65-70 dB range. In this instance, most of the ‘population’ dynamic range was encompassed by 4 of the 6 individual functions, since each of these was rather broad.

A second case is shown in Fig. 6B. In this instance, all of the neurons were most sensitive to tone frequencies in the range from 6.0 to 6.21 kHz. The rat is less sensitive to frequencies in this range, and the unit thresholds are correspondingly higher than those in Fig. 6A. Nevertheless, the thresholds of these cells spanned a 50 dB range, and the dynamic portions of their rate-response curves spanned about 65 dB. In this instance, most of the units had relatively narrow dynamic ranges, and the fact that together they span a wide intensity range reflects the spread of the individual unit thresholds. Note that in both families of curves, the right-most functions had not reached saturation over the tone amplitude ranges tested.

TONE40 AMPLI?JDE k?: SPL 1 100

Fig. 6. (A) Normalized rate-level functions for 6 neurons in the

10.0 kHz region of left auditory cortex in rat 12. (B) Normal-

ized rate-level functions for 7 neurons in the 6 kHz region of

left auditory cortex in rat 13. Description in text.

This suggests that the population dynamic range may have been broader than was indicated by the functions obtained.

Discussion

Comparison with the cat

This study has provided the first quantitative account of tone-pulse amplitude coding in the auditory cortex of a rodent. As in the cat (Brugge et al., 1969; Phillips, 1987, 1988; Phillips et al., 1985) neural elements in the albino rat’s acoustic sensory cortex may display either a monotonic or a nonmonotonic rate response for tone pulse stimuli (Fig. 1). The amplitude dynamic ranges of rat cortical cells were often in the range from 10 to 30 dB in breadth, which is comparable to that seen in cats. However, the rat data are biased towards broader dynamic ranges within those limits, and the tail of the distribution is broader than that seen in cats (Fig. 3).

To the extent that it was possible to index nonmonotonicity, the rat’s auditory cortex con-

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tained relatively few nonmonotonic cells (Fig. 2). Although a number of neurons showed modest declines in response rate at high stimulus levels, increasingly intense tones often failed to bring about progressively larger reductions in spike output (Fig. 1, E-G). In both anesthetized cats (Phil- lips and Orman, 1984; Phillips et al., 1985) and awake monkeys (Brugge and Merzenich, 1873; Pfingst and O’Connor, 1981), cortical cells with a nonmonotonic rate response often show close to a 100% reduction in spike rate at high tone-pulse levels. This was ~common in the rat, so that any apparent ‘tuning’ to tone level was much less marked than that seen in other species.

The reasons for this are unclear. The ascending limbs of rat cortical rate-level functions, which presumably are shaped largely by the rate-level functions of their excitatory afferents, often had broader dynamic ranges than those seen in cat cortical neurons. There are, therefore, grounds to suspect that the inhibitory inputs shaping the de- scending limbs of these functions might have had equally gradual rate responses. This set of cir- cumstances might result in a neuron’s being very broadly ‘tuned’ to tone pulse level, with shallow rising and falling slopes of the rate-level function. We have such neurons in our sample. This account, however, would not explain the ‘partial’ non- monoto~city seen in other neurons, ie., the tendency for a neuron’s rate-response to level out at intermediate rates for supramaximal stimulus levels (Fig. 1, E-G). Since such behavior is uncommon in the cat and primate, its existence in the rat raises the possibility that the organization of in~bito~ inputs to some rat cortical cells may be less well developed than those suspected of shaping the response areas of feline cortical cells (cf. Phillips 1987, 1988).

The minimum response latencies of rat cortical neurons were generally shorter than those seen in cats (Fig. 5B). This probably reflects the smaller absolute size of the rat brain. Minimum latent periods were unrelated to both absolute sensitivity and dynamic range width.

Interestingly, however, dynamic range width showed some relation to CF threshold (Fig. 4A). In particular, the neurons with the broadest dynamic ranges typically had the lowest CF thresholds. While the paucity of broad dynamic

ranges in units with high thresholds might in part be attributed to the limits of our sound delivery system, the relative scarcity of low-threshold units with narrow dynamic ranges cannot be attributed to methodological problems (see above). In the cat’s auditory nerve, it is usually the high-threshold fiber population that displays sloping saturation and broader dynamic ranges (cf., Sachs and Ab- bas, 1974; Liberman, 1978). A scrutiny of our data on cat cortical cells (Phillips and Hall, unpublished) found no evidence of any relation between these two properties. This suggests that the afferent pathway to the cat’s AI is sufficiently convergent to obscure the relation present in cochlear output. The case with the albino rat remains to be determined.

Cortical representation of signal level Earlier studies in cats (Phillips and Irvine, 1981),

ferrets (Phillips et al., 1988) and rats (Sally and Kelly, 1988) have provided evidence that cortical neurons of similar CF may have absolute sensitivi- ties that span at least a 40 dB range. When such data have been collected from single indi~duals, which eliminates any confounding with individual differences in peripheral sensitivity, this range is approximately the same as, or slightly smaller than, the group dynamic range (Reale et al., 1987; Sally and Kelly, 1988). Now, in a cortical field populated largely by neurons with a monotonic rate response, any model of intensity ‘representation’ based on the number of active elements must have a dynamic range not exceeding the spread of unit thresholds at the test frequency, ie., less than about 40 dB. In cortical fields containing nonmonotonic neurons, the neural correlate of stimulus intensity likely resides as much in the identity of the discharging elements as in their number (Phillips, 1987; Phillips and Orman, 1984; Suga and Manabe, 1982). This follows from the apparent ‘tuning’ of those neurons to signal amplitude; in such cortical regions, the range of preferred amplitudes may be very broad indeed (Brugge and Merzenich, 1973; Suga and Manabe, 1982; Phillips and Orman, 1984), and may span the behavioral range.

In the albino rat’s AI, nonmonotonic neurons were relatively scarce. The present study, however, provides evidence for two other neural manifesta-

278

tions of level sensitivity that are pertinent to this issue. The first is that some neurons have very broad dynamic ranges (Fig. 5. 1, 3, 6A). This finding is compatible with the view that the firing rates of the relevant neurons contribute signifi-

cantly to the neural code for stimulus level (cf.

Phillips, 1987). The second is that even for neurons with narrower dynamic ranges, the rate re-

sponse of populations of cells with similar CFs

and different thresholds may span more than 70 dB (Fig. 6B). In at least some of our sample, high

threshold neurons had amplitude dynamic ranges that extended beyond the output of our stimulat-

ing system. The 70 dB range mentioned above, then, must be regarded as a minimum estimate of that represented in the cortex. This leads us to believe that the rat’s auditory cortex, even if relatively few of its neurons have a nonmonotonic rate response, also may contain a neural representation of tone level that has a dynamic range as wide as the behavioral one. This discussion is not intended to discount the inherent ambiguity of any single neuron’s spike rate, nor is it intended to suggest that intensity discrimination relies on

specifically cortical processes (cf. Swisher, 1976). Our findings, however, do indicate that resident in

the cortex is a neural representation of tone amplitude that likely spans the behavioral dynamic range.

Acknowledgements

We thank Susan Sherwood for participating in some of the experiments, and S.E. Hall for helpful comments on the manuscript. The research was supported by NSERC Grants 7654 to JBK and U0442 to DPP.

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coding of tone-pulse amplitude by single neurons in auditory cortex of albino rats (rattus...

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