An analysis of variability in somatosensory cortical neuron discharge.

OF NEUROPHYSIOLOGY Vol. 40, No. 3, May 1977. Printed

JOURNAL

in U.S.A.

An Analysis of Variability in Somatosensory Cortical Neuron Discharge B. L. WHITSEL,

R. C. SCHREINER,

Department of Physiology, University Chapel Hill, North Carolina 27514

SUMMARY

AND

AND

of

North

CONCLUSIONS

The spike trains of individual somatosensory cerebral cortical neurons were recorded with extracellular microelectrodes in the absence of general anesthesia. Macaque monkeys served as experimental subjects. 2. Estimates of neuronal variability were computed from the impulse activity contained within periods (time segments) of stationary activity evoked by constant-velocity tactile stimuli. 3. The variability (as estimated by the standard deviation of the interspike-interval distribution-SD) computed for weakly stationary time segments of stimulus-evoked discharge activity is shown to be independent of stimulus parameters. 4. For each of the 165 cortical neurons which were studied, the SD of discharge activity decreased as the mean rate of firing increased; and the relationship between SD and the mean interspike interval (MI) was of the form SD = ma MI + b (where m = slope and b = y intercept). 5. It is shown that in both area 3-l of SI and in SII/r the neuron population which represents the hairy skin of the proximal fore- or hindlimb is heterogeneous; i.e., in each area these neurons can be subdivided into three subpopulations on the basis of the y intercept of the linear relationship between SD and MI for stimulus-evoked discharge activity. Moreover, it is shown that neurons with different y intercept and slope parameters exhibit different discharge patterns as reflected by the shape of the hazard function. 6. Considered together, the observations are interpreted to indicate that the differences in the S D-MI relationship exhibited by the different subpopulations of somatosensory cortical neurons enable the somatosensory cortex to reflect subtle differences in tactile stimulation over a larger range of stimulus intensities than would be possible if all neurons exhibited similar S D-MI relationships. 1.

Received

for publication

April

19, 1976.

G. K. ESSICK Carolina,

INTRODUCTION

Averages of stimulus-locked alterations in somatosensory neuron discharge activity tend to be reproducible when appropriately formed; but comparison of the spike trains evoked by successive replications of the mechanical stimulus reveals a tendency for the detailed temporal structure of the evoked discharge to vary unpredictably from one stimulus trial to the next. Such variations are expected, since some degree of randomness in the timing of spike discharges has been observed in every type of sensory neuron which has been examined with the technique of single-unit recording. The prominent variability which is associated with central somatosensory neuron discharge persists even when extensive measures are taken to eliminate nonconstant external factors, which might induce fluctuations in the neuronal response (18). If the random element in central somatosensory neuron discharge is not introduced by a failure to control experimental variables but is intrinsic to the neuron or to the neural circuit in which it functions, then it is of value to obtain quantitative estimates of the variability associated with the discharge evoked by environmental stimuli. Such data provide insight into the information processing capabilities of the neural circuit under observation (3-6, 9, 13, 15). The experiments to be described in this paper may be divided into two categories on the basis of the experimental questions to which they were addressed. Once we had designed a method to obtain reproducible estimates of neuronal variability during stationary periods of somatosensory neuron discharge, an initial series of experiments was performed to determine if, and to what degree, the interspike-interval distribution for stimulus-evoked cortical somatosensory neuron discharge activity is influenced by the parameters of the mechanical stimulus delivered to the receptive field. A second group of experiments was motivated by the recent demonstration (15, 16) that, although variability in the timing of neuronal discharges limits the information capacity of a nerve cell, it also can 589

Downloaded from www.physiology.org/journal/jn by ${individualUser.givenNames} ${individualUser.surname} (129.186.138.035) on January 13, 2019

590

WHITSEL,

SCHREINER,

lead to an improved correspondence between a time-varying environmental stimulus and its representation in a single neuron’s spike train: i.e. ) intrinsic variability may “linearize” the neuronal response so that it encodes, without appreciable distortion, a limited range of timevarying mechanical stimuli. This latter view of variability as a mechanism for counteracting neural nonlinearities (e.g., phase locking, rectification, etc.), which severely distort the central representation of sensory events, and as a means for “tuning” central neurons to certain stimulus features, led us to study systematically the relationship between variability and mean firing frequency for different somatosensory cortical neurons. With this approach we sought: a) to determine if the neurons of SI and SII/r which receive their input from the hairy skin of the foreor hindlimb could be further differentiated on the basis of the level of variability associated with their discharge, and b) to obtain evidence which would clarify the role of neuronal variability in the coding of somatosensory stimuli. Preliminary descriptions of the findings obtained in this study have been reported elsewhere (14, 23, 26). METHODS

Subjects and preparation Sixty-seven monkeys (Macaca mulatta), each weighing between 3 and 6 kg, provided the data which are described in this report. Fifty-two of these animals also provided data reported in earlier papers, addressed to different aspects of somatosensory cortical functional organization (24, 25). The subjects were anesthetized with halothane, the trachea was intubated with a soft catheter lubricated with local anesthetic, a small opening was made in the skull, a recording chamber was cemented over the opening with dental acrylic, and the surgical field on the head was infiltrated with local anesthetic (5% XyloCaine ointment). After incising and reflecting the dura overlying the cortical region of interest, general anesthesia was discontinued. Neuromuscular block was produced by intravenous administration of gallamine triethiodide, and the animal was ventilated with a positive-pressure respirator. End-tidal carbon dioxide was maintained between 3.5 and 4.5% by adjustment of the respiratory volume and/or rate; rectal temperature was maintained at 37°C. The recording chamber was filled with artificial cerebrospinal fluid and sealed with a glass plate in order to minimize cortical movements. The glass plate contained an 0 ring through which the microelectrode was advanced. A metal ring attached to the recording chamber supported the animal’s head, thus eliminating the need for ear bars. Previous publications (22,

AND

ESSICK

24) have described in detail the precautions which were routinely taken to minimize stress to the experimental subjects.

General

experimental

procedures

Extracellular recordings were obtained with glass-insulated tungsten microelectrodes; their preparation and characteristics have been described previously, as have our methods for determining receptive-field location, submodali ty class, and response properties of individual cerebral cortical neurons (19, 24). The impulses discharged by individual neurons were amplified and displayed in the conventional manner; better than 90% of the recorded units emitted discharges with characteristics similar to those recorded from the “regular’ ’ cortical neuron population described by Mountcastle et al. (8). At the end of the experiment, the animal was deeply anesthetized and perfused with 0.9010 saline and lOY6 neutral buffered formalin. The brain was embedded in celloidin, sectioned at 30 pm, and the sections were stained with cresyl fast violet and/or Mahon’s stain. Each microelectrode penetration was reconstructed, and the cytoarchitectonic areas encountered by the electrode were identified according to the criteria of Powell and Mountcastle (11; see also ref 2). Electrolytic lesions (created by passing DC currents of 2.5-5 PA for 2-5 s through the recording electrode), were made at points of interest during each microelectrode penetration.

Mechanical

stimulus

control

The mechanical stimulator employed consists of servomotors, gear trains, and controlling electronic circuitry enabling the delivery of constant-velocity tactile stimuli, which traverse a selected portion of a cutaneous receptive field at predetermined orientation and direction (24, 25). It was possible to deliver stimuli at any velocity between 10 and 300 mm/s. In all of the experiments reported in this paper, the stimulator was fitted to carry a fine camel’s hair brush, which did not produce significant distortion of the skin at any stimulus velocity. None of the brushes exerted a force greater than 10 g over the velocity range used in this study. It has been demonstrated that such brushing stimuli effectively activate not only those primary afferents which innervate the mechanoreceptors associated with hair follicles, but also the type I and type II afferents which innervate specialized endings in the hairy skin (25). In every experiment, successive stimulus presentations were separated by an interval of at least 2.5 s, during which the brush did not make contact with the skin. At this interval, the responses elicited from SI neurons by successive stimulus presentations are statistically independent (unpublished data); on the other hand, longer intertrial intervals (as long as


SQMATOSENSORY

STIMULUS

of occurrence of both neural discharges and stimulator events for each stimulus run (see Fig. 1). Each run usually consisted of 50 stimulus presentations, and during all runs successive presentations were delivered in opposite directions. In this display the events recorded during the stimulus presentations which moved in the same direction are plotted in serial order and are aligned with reference to the pulse generated at each stimulus onset. For the present study, it is only necessary that the segments of neural activity (time segments) chosen for analysis represent periods during which the neuron was discharging at a constant rate with constant variability. In order to be objective in our selection of time segments for analysis, we adopted a method of testing the activity within a segment for constancy of mean and variance, a condition called “weak stationarity . ” The method was designed to allow us to be reasonably confident that the segments of activity chosen for analysis satisfied these

20 s) were required in order to evoke reproducible responses from SIR neurons (24). The onset and completion of each stimulus presentation were signaled by pulses which enabled computation of the duration of each stimulus; stimulus velocity was computed by dividing the length of the stimulus path by the duration.

Data collection and analysis In the majority of the experiments of this project, a LINC-8 computer installation was used to monitor stimulus events and to measure and store interspike-interval data. The times of occurrence of both neural and stimulus events were measured with a resolution of 0.25 ms, and these measurements were stored on digital magnetic tape. The data tapes subsequently were translated into a format suitable for analysis on the PDP 1 l/20 installation in our present laboratory. The initial analysis performed on the stored data consisted of a graphical display of the times UNIT

LqAMINA

62-2-A

Stimulus P

>D

591

CODING

TL

AREA

l-3

Run ,P

D

-7

No - Stimulus P

>D

D

s

Y IO.

Run ,P

FIG. 1. Top: spike trains recorded from an SI neuron during 16 presentations of a constant-velocity tactile stimulus which moved along the medial edge of the foot from proximal to distal (P + D, on the left); and during 15 presentations which moved at the same velocity (22 mm/s) in the opposite direction (D +-- P, on the right). The spike train positioned nearest the time scale was obtained during delivery of the first stimulus presentation in that direction. The vertical arrow positioned along the time scale indicates the time of initial brush contact with the receptive field; the brush broke contact with the receptive field 350 ms prior to the end of each spike train. The intertrial interval was 3.5 s. Bottom: spike trains recorded at the same stimulator settings except that, in this case, the brush was not permitted to make contact with the receptive field. Such “no-stimulus” runs were performed routinely in order to estimate the contribution of spontaneous activity to responses recorded during the application of brushing stimuli.


592

WHITSEL,

SCHREINER,

criteria. Once an adequate number of stationary segments for an individual neuron was identified, the mean interval and its standard deviation were calculated for the activity within each segment. For each neuron a regression analysis was performed to determine the relationship between MI (as independent variable) and SD. We also found it useful to form interspike-interval (ISI) histograms of the intervals within a time segment, using a fixed 2-ms bin width. The initial step of the method to assess stationarity was to choose candidate segments (the segments varied between 100 and 650 ms in dusegment was 250 ms and ration; the “typical” contained 100-300 intervals) which were judged to contain spikes occurring at a constant rate (see Fig. 2A). The final step was to apply the “runs test” (1). In this test, the candidate segment is divided into 14 subsegments of equal duration, and these subsegments are then examined to determine if the average spike counts for the 14 subsegments are distributed randomly (in sequence) about the median of the averages and if the variances also are distributed randomly about their median. The null hypothesis is that there are neither too few nor too many runs in the data to cause suspicion that there are systematic changes in the mean or variance. A run consists of a sequence of average spike counts or of variances greater than or equal to the median or a sequence of averages or variances less than the median. A total of 4,977 time segments was chosen for

0’

MS

AND

ESSICK

examination by the runs test. Using a 5% rejection level for the null hypothesis, 227, or 4.6% of the total number of segments, were eliminated from any further consideration. We also examined the-data using a 15.6% rejection level, since a more conservative test would reduce the likelihood of accepting a false null hypothesis (type II error). An additional 519 segments, or 10.4% of the original total, failed to pass the runs test using the 15.6% rejection level. The impact of the more conservative test on the outcome of the regression analysis was determined for a randomly selected sample of 25 neurons. In no instance did elimination of points failing the runs test, using the 15.6% rejection level, significantly change either the y intercept or slope parameters of the regression equation relating SD and Ml, We conclude that, at worst, the segments failing the runs test at the 15.6% rejection level deviate only slightly from the condition of weak stationarity, and that no improvement in estimating the “true” S D-MI relationship results from use of the more conservative test. For all 165 neurons in the sample, the regression between SD and MI (using-segments which passed at the 5% level) was highly significant (P < O.OOl). In order to reveal the relationship between the time of occurrence of a spike and the timing of its immediate predecessor, the “hazard function’ ’ was calculated for the intervals within each segment and was displayed graphically. The hazard function is the conditional probability of the occurrence of an event at time t, given

32

FIG. 2. A: A time segment ofstimulus-woked cortical neuron discharge activity (the activity between 660 and 850 ms after the start of the stimulus period). The intertrial interval was 4.8 s. Stimulus velocity, $0 mm/s. B: PST histogram as a means to display separately the variability of the data in successive subdivisions of a single time segment. In the period between 600 and 850 ms the mean rate of firing did not change significantly. C: IS1 histogram for the segment identified inA and B. MI, 9.85 ms; SD, 4.81 ms; CV, 0.49; number of intervals in time segment, 577; bin width, 2 ms.


SOMATOSENSORY

STIMULUS

CODING

593

activity, evoked from a given somatosensory cortical neuron, are highly reproducible only if the measureswere obtained from time segments of dischargeactivity with the samemean interval. If this condition is satisfied, however, the reproducibility of the interspike-interval histoWe estimated the hazard function by using a gram and its statistical parametersis remarkable smooth nonparametric density estimator, the even for segmentsof activity that were recorded difference quotient of the sample distribution at intervals as much as 6 h (Fig. 3). function (12), also called the “naive density esHaving identified an approach for obtaining timate” (17). reproducible estimatesof the underlying probaThe naive density estimate is bility distribution of interval lengths for stationary periods of neural activity evoked by conf,(t) = [ F,(t + h) - F,(t - h)]/2h stant-velocity stimuli, we set out to determine where F,(t) is the samplecumulative distribution the effects of changesin stimulusparameterson function for y1samples,and CL(t)the naive den- somatosensoryneuronal variability. A variety of sity estimate. The parameter h is dependent on stimulusparameterswere studied, including vethe samplesize and the underlying distribution locity, orientation and direction of brush motion, f(t). Optimal choice of h is and location of the stimuluswithin the receptive h = k . n-li5 field. The principal goal of these experiments was to determine if changesin stimulusproperwhere ties alter the random element in neuronal disk = (9/[2 Irn If”(t)l” dt]}1’5 charge in the absence of any change in firing frequency. In practice, the choice of k must be made with Effects of direction and orientation somejudgment about the nature of f(t). We have found that the following procedure for estimat- on variability Figure 4 compares interspike-interval histoing k results in a smoothdensity estimate, which follows the general trends of f(t) as revealed by grams obtained from the spike trains evoked by the histogram. An interval histogram is gener- constant-velocity stimuli which traversed the reated usinga 2-msbin width; from the histogram ceptive field of an SI neuron in different direcis obtained the ratio “peak” = (no. of intervals tions and at different orientations (seelegendto in modal bin)/(total no. of intervals) and k is then Fig. 4). The striking similarity of the IS1 distributions for periods of activity with comparacomputed as ble mean intervals, but evoked by the different k = -60 . peak + 26 stimuli, indicates that neither the changesin the The result of this procedure is that data with a direction nor the orientation of the tactile stimubroad range (and small peak) will have a larger lus led to detectable alterations in neuronal varivalue of the differentiating parameter h than will ability. data with a narrow range. The naive density Although most experiments of the present estimatehastwo advantagesof particular impor- study did not utilize the range of stimulusorientance for hazard estimation: a) it is more stable tations and directions illustrated in Fig. 4, all 165 than the histogram, especially when there are neuronsincluded in this study were studiedfor a limited data; and b) it is a more accurateestimate period of time sufficient to record the response of the actual density than is the histogram. to stimuli, which traversed the receptive field in For the characterization of the responsepat- opposite directions. None of the neuronsin our terns evoked by the moving tactile stimuli, we sampleexhibited alterations in the variability asplotted the data aspoststimulustime histograms sociated with a fixed level of discharge in re(PST histograms;see Fig. 2B). Each PST histo- sponseto changesin the direction and/or oriengram was constructed from the responsesto tation of the moving stimuli. only one direction of brush motion (additional Effects of stimulus location details are found in the legend to Fig. 2B). the occurrence of the preceding event at time zero. If f(t) is the interval density, and F(t) the cumulative distribution, then the hazard function H(t), is defined as

-m

on variability

RESULTS

The data basefor the present study consistsof records of the times of occurrence of the action potentials evoked from 165somatosensorycortical neurons during the application of constant-velocity tactile stimulation. Under the experimental conditions employed in this investigation, measuresof the variability of discharge

Figures 5 and 6 demonstratethat the variability associatedwith a fixed meanlevel of somatosensory cortical neuron dischargealso is unaffected by even radical changesin the location of the stimuluswithin the receptive field. The data illustrated in Figs. 5 and 6 were obtained in the course of a study of an SIG- neuron which possesseda continuous bilateral cutaneous re-


594

ST

HISTOGRAMS

Mk6.90

SD-4.82

SD=8.43 P8

Ml=13.82

1=13.61

S&)=8.21

2Ql 1 1

Mk7.62

SD=5.18

Ml=8*19

SD=5.7D

1352

41

/

MH2.23 2D

SDz6.38

Ml=1427 20’

SD=7.82

y 2 6

44

4.-.J--J--qL_l ir-r----r-----i-” 40 Q /

i

32

MS

FIG. 3. Left: PST histograms formed from the response to movement of a fine brush across the thigh from postaxial to preaxial (left histogram in each pair) and from preaxial to postaxial (right histogram in each pair) at a velocity of 80 mm/s. Each PST histogram (bin width, 100 ms) was formed from responses to 25 stimulus presentations. The brush was not in contact with the receptive field either before the onset or after termination of each stimulus presentation and, as a consequence, the first bins of each histogram reflect the neuron’s spontaneous activity. In this experiment, a run consisting of 25 stimuli in each irection was delivered at 10&n intervals (time after start of experiment is indicated to the right of each histogram pair). Bins of histograms which are coded identify the 200-ms segments selected for analysis of neuronal variability. Right: ISI histograms for the 10 segments identified on the left. Each row of IS1 histograms was obtained at the time indicated at the right. Bin width for all IS I histograms, 2 ms.

ceptive field. In these studies, dual mechanical stimulators were set up so that symmetrical portions of the bilateral receptive fields could be subjected to constant-velocity tactile stimuli possessing identical temporal and spatial characteristics (20). The electronic circuitry-employed to control the mechanical stimulators also permitted the ipsilateral and contralateral stimuli to be applied independently or simultaneously. Figure 5 displays the PST histograms formed from the responses elicited by stimulation, first, of the ipsilateral component (bottom histograms) and, IO min later, of the contralateral component (middle histograms) of the receptive field. The response elicited by simultaneous application of both the ipsilateral and contralateral stimuli (designated as the “bilateral” stimulus mode), is shown by the histograms at the top of the figure.

In order to assess the effect on neuronal variability of stimulus location within the receptive field, segments with comparable mean intervals were selected from the spike trains recorded under each of the six stimulus conditions illustrated. The IS1 histograms generated from the data within these segments are displayed for comparison in Fig. 6. Although the three ISI histograms in the left column (40 ms > MI > 33 ms) and in the right column (61 ms 2 MI > 54 ms) were computed from discharge activity evoked by stimuli which either engaged different regions or different combinations of regions within the receptive field, the histograms with comparable mean intervals display marked similarities. All of the 36 SlVr neurons studied in this way displayed a fixed level of variability when they discharged at a particular frequency to tactile


SOMATOSENSORY

40

STIMULUS

RUN 1 MI= 6.44 cv= 0.82

CODlNG

RUN 2 r238 -

0 0

Fj 4Q

RUN 3

392

Ml=6.82 CV=Q.79

Q

MS

595

32

VEb=63

40

Q

32 RUN 4

MS

191

E i!s

3

MM SEC

FIG. 4. Bottom: representation of different stimulus conditions by arrows placed on the figurine. In run 1, stimuli moved from lateral to medial; in run 2, stimuli moved from proxima’i to istal at a medial location in the p9 receptive field; in run 3, stimuli moved from distal to proximal at a location c s-m >. lateral to the chord of run 2; in run 4, stimuli moved in the opposite direction of the stimuli of run 1, A.11 stimrxh were applied at 63 mm/s, Top: IS I Rin width of all JSI histograms computed from activity elicited by the four different stimr if.aq conditions. histograms, 2 ms.

stimuli, regardless of the direction of stimulus motion or the location of the stimulus in the receptive field. SI neurons also failed to display alterations in the variability associated with a fixed level of discharge when stimulus location within the receptive field was changed (see Fig. 4). It should be noted that for the SIiir neuron illustrated in Fig. 5, ipsilateral and contralateral stimuli were equally effective when tested separately, but simultaneous bilateral stimulation did not lead to neuron response significantly greater than that evoked by either stimulus presented alone. Although this behavior was observed frequently in our study of N/r neurons, other types of interaction between the ipsilateral and contralateral stimuli also were observed (e.g., summation. Lcilitation, and inhibition). These

The data displayed in Fig. 7 were obtained from a single SI neuron in an experiment in which the velocity of a tactile stimulus was varied, while stimulus orientation, direction, and location within the receptive field were held COIIstant. The PST histograms illustrated at the top for stimuli which of Fig, 7 were computed moved at 25 mm/s (top left histogram), at 64 mm/s (top center histogram). and at 130 mm/s (top right histogram). Once again, segments with comparable mean intervals were selected (one from the set of spike trains evoked at each stimu-


596

WHITSEL,

SCHREINER,

AND ESSICK

EXP:39-l-S-11 BIN WIDTHdOmsec

N TRA

0 lmm 2 SCALE

c

SKIN

IPSI

MUR

FIG. 5. Left: reconstructed microelectrode penetration of SII/r. Locations at which single neuron discharges or multiple neuron recordings (M URs) were obtained are indicated by lines (solid line, single unit; broken line, MUR) drawn at right angles to the electrode track. The cutaneous receptive field for the neuron or MUR recorded at each location is drawn on a figurine or pair of figurines (filled regions = receptive fields of single neurons; striped regions = receptive fields of MURs). Right: three pairs of PST histograms formed from the response of SII/r neuron C-4 to constant-velocity tactile stimulation. Stimuli were applied (velocity 35 mm/s) to symmetrical portions of the bilateral receptive field. Stimuli moved across the preaxial forelimbs in either of two directions; from proximal to distal (--+) and from distal to proximal (+). Twenty-five replications of each stimulus were employed; 4.5 s elapsed between successive stimulus presentations. The top pair of histograms shows the response to simultaneous stimulation of both the ipsilateral and contralateral parts of the receptive field (the Bilateral Stimulus mode); the center and bottom pair of histograms show the response of the neuron to unilateral stimulation (Contra, Contralateral stimulation only; IPSI, ipsilateral stimulation only). Neuron C-4 was located in lamina IV of area SII/r. The coded regions of histograms identify segments selected for analysis of neuronal variability.

lus velocity), and the IS1 histogram for each segment was computed and displayed for comparison. Figure 7 shows that the IS1 histograms for segments of activity evoked by stimuli of different velocities display striking similarities, as long as they possess similar mean intervals. Of the 37 SI and SII/r neurons in which the effect of variations in stimulus velocity on the timing of neural discharges was studied, none displayed systematic alterations in neuronal variability which were independent of changes in mean interval.

Relation between neuronal variability and mean interspike interval Satisfied that the parameters of the probability distribution of interspike-interval lengths mea-

sured in this study were independent of the stimulus conditions employed, we determined, for each cortical neuron sampled, the relationship between neuronal variability (as estimated by the SD) and the firing level (in terms of the MI) evoked by constant-velocity tactile stimulation. Figure 8 displays data (interspike-interval histograms and their statistical parameters) obtained from stationary periods of the response of two SI neurons to three different levels of peripheral drive. Although, for both neurons, the SD of the discharge activity becomes smaller as the MI is reduced, inspection of the interspikeinterval histogram and its parameters at the different levels of peripheral drive suggests that the function relating SD to MI for the two neurons


SOMATOSENSORY

Mb33.91

STIMULUS

SDs24.12

MW4.58 69

20

CODING

597

SDf38.49

30

20

I:

1

BILATERAL

IUI =36.52 20

SD=20.74 r59

MI=60.95 201

SD=33.42

CONTRALATERAL t 1

IN

F

33 c/) \1

2

MIt39.43 20m

SDt24.18

Mb56.28 I:

52

20

SD132.34

1

IPSILATERAL

MS

MS

FIG. 6. Interspike interval histograms for periods of activity selected in Fig. 5. Each histogram was computed from a segment of activity elicited by a different stimulus condition. The top pair of histograms were computed for activity elicited by bilateral stimulation; the center pair by contralateral stimulation; and the bottom pair by ipsilateral stimulation.

is substantially different. This difference becomes even more apparent on consideration of the change in relative variability (expressed as the coefficient of variation; CV = SD/MI) as mean firing level was altered; i.e., for the neuron in Fig. 8A, the CV increased as MI decreased, but for the neuron in Fig. 8B, the CV decreased as MI decreased. The quantitative relationship between SD and MI for stimulus-driven activity of three different SI neurons is illustrated in Fig. 9. For the three neurons of Fig. 9, as for all neurons studied, it was found that there was a highly significant (P < 0.001) linear relationship between MI and SD

(SD = moMI + b; where m = slope and b = y intercept), and that by employment of a uniform criterion for statistical significance (P < 0.01) of the y intercept values, each neuron could be assigned to one of three categories based on the sign of the y intercept. Of the sample neurons, 101 (61.2%) had a S D-MI relationship with a significant negative y intercept; 38 neurons (23 .O%) had a S D-MI relationship with a y intercept not differing significantly from zero; and 26 neurons ( 15.8%) had a S D-MI relationship with a significant positive y intercept. Although this classification system segregates somatosensory cortical neurons on


598

WHITSEL, EVENTS/SEC

5

50-

0+r ” cn+ P .I- 3 mc e ‘e;z -

AND

ESSICK

EVENTS/SEC

25MM/SEC

EVENTS/SEC

loo-

loo-

64 MM/SEC

130 MM/SEC

-

503 ’ 0

1 SEC

1 5.0

0

PERCENT

-

SCHREINER,

50-

1 SEC

I

I 5.0

I 0

PERCENT

207 -

r

I 5.0

PERCENT

20

MI = 20. 92 21 97

SD = 29.51 cv = 1.12

0

1 SEC

MS

250

0

MS

250

0

MS

7. Top: PST histograms illustrating the response of an SI neuron (unit 87-6-B, cyto area 3-l) to 25 presentations of a brushing stimulus applied at three different velocities. The intertrial interval was 2.8 s. The stimulus velocities were 25 mm/s (left histogram), 64 mm/s (center histogram), and 130 mm/s (right histogram). Bin width, 200 ms. The coded bins identify the segments selected for analysis of neuronal variability. Bottom: IS1 histograms for the three time segments identified at the top. Bin width, 2 ms. FIG.

the basis of statistical criteria, the neural population which we have sampled is more appropriately viewed as a continuum. In accord with this view, the y intercept values for the population ranged continuously between -23.0 and +7.5; the slope values ranged continuously between 0.2 and 1.8 (see Fig. lo), and the y intercept and slope values were negatively correlated (Y = -0.645; P < 0.001). There were no apparent differences in the incidence of the three categories of neurons between area 3-l of SI and SIR (Fig. lo), and statistical tests indicated that there was no significant relation between the parameters of the S D-MI relation and the location of a neuron within the central laminae (layers III, IV, and V) of cytoarchitectural area 3-l. Since very few neurons located in laminae I, II, or VI were studied, no summary statement about the variability in the timing of spike firing by the neurons in these layers is possible. Although the population of somatosensory cortical neurons sampled by our extracellular recording techniques exhibits a continuous range of slope and y intercept parameters for the linear relationship between SD and MI, an indication that the y intercept parameter may possess functional significance is gained by frac-

tionating the population into the three groups generated by employing the statistical criteria described above. That the neurons within a single group exhibit common behavior, and that the behavior of neurons belonging to different groups differs strikingly is made evident by plots of the relationship between the coefficient of variation and mean firing frequency. Figure 11 illustrates that a neuron whose SD-MI relationship has a y intercept value which does not differ significantly from zero has essentially the same CV at all levels of peripheral drive (top left plot). In contrast, a neuron whose SD-MI relationship has a significant negative y intercept displays a decreasing CV as firing frequency increases (top right plot); and a neuron whose SD-MI relationship has a significant positive y intercept displays an increasing CV as firing frequency increases (bottom plot). This relationship between magnitude of the y intercept value of the SD-MI relationship and the function relating CV to mean firing frequency reveals that, while all somatosensory cortical neurons discharge with less absolute variability as mean firing frequency is elevated (for all neurons the S D decreased as the MI decreased), somatosensory cortical neurons with significantly different


SOMATOSENSORY

A

STIMULUS

CODING

r331

?21 20

20

Mkll.18

Mk36.31 cv= 0.41

CV= 0.52

84

B 209

599

MI= 89.07 cv = 1.13

32

13’ 20:

Ml = 33.67

32

-54

20:

MI= 24.59 CV= 0.85

cv = 1.01 :

.

-44 c

3a >

-

E -

K

c

0

nnn ,n,O 0

FIG. 8. Row A: Three IS1 histograms obtained from a single SI neuron when it was firing at different mean rates. Bin width, 2 ms. Row B: three IS1 histograms obtained from a single SI neuron when it was firing at different mean rates. Bin width, 2 ms.

INTERCEPT G 0 UNIT 96-3-G 301 SD= 0.781 (W0.489

125

INTERCEPT < 0 UNIT 69-2-B

1

G i- 53 O1

III

0

111-l

MI WEC) 55

I

30

0

II

I

I

Ml (MSEC)

II

1

125

INTERCEPT > 0 UNIT 69-6-B SD= 0.458 W+3.474

0

Ml (MSEC)

55

FIG. 9. Linear regression analysis of the relationship between SD and MI for three different SI neurons. Top: linear SD-MI relationship with ay intercept not significantly different from zero (on the left); linear SD-MI relationship with a significant negative y intercept (on the right). Bottom: linear SD-MI relationship with a significant positive y intercept. For each neuron illustrated, the linear regression between the variables SD and MI was highly significant (P < 0.001).


WHITSEL,

SCHREINER,

AND

ESSICK

INTERCEPT VERSUS SLOPE BRUSHING STIMULATION CUTANEOUS NEURONS S-I AND S-II/R

0 Q Q 0

A

0

0 A0

= S-I = S-II

Q A

I

-23.00

UNITS UNITS I

-17 A2

I

-12.83

I

-7.75

I

-2.67

I

2.72

1

7.50

INTERCEPT FIG. 10. As ‘sociation between the y intercept and slope parameters of the SD-MI somatosensory cortical neurons.

y intercept values differ in the extent to which the standard deviation of discharge activity is reduced by a fixed increment in firing frequency.

Analysis of discharge patterns hazard function

using

In an attempt to gain an understanding of the functional meaning of the different SD-MI relationships displayed by the different members of a cortical neuron population with similar receptive-field and submodality characteristics, we computed estimates of the hazard function for each neuron in our sample. Although the hazard function does not introduce any information not already contained in the IS1 distribution, it has been shown to be a useful means of revealing differences in the discharge patterns generated by different types of sensory neurons (3, 5, 10). A consistent relationship between the shape of the hazard function and the SD-MI relationship of a cortical neuron was identified. For each neuron whose SD-MI relationship had a positive y intercept, the hazard function increased monotonically; the maximum occurred

relationships

for 165

at intervals equal to or exceeding the mean interspike interval of the discharge activity (top plot, Fig. 12). In contrast, the hazard function for a neuron whose S D-MI relationship had a zero y intercept attained maximal firing probability at intervals less than the mean interspike interval, and this was maintained for intervals appreciably longer than the mean interval (center plot, Fig. 12). Finally, a neuron whose SD-MI relationship had a markedly negative y intercept also displayed an early elevation in firing probability but, in contrast to neurons with a zero y intercept, the early elevation was more pronounced, there was a later period of low firing probability, and a third period in which the firing probability again rose to levels approaching the early maximum (bottom plot, Fig. 12). In addition to our finding that the shape of the hazard function was similar for cortical neurons whose SD-MI relationships had comparable y intercept values, we consistently found that the basic shape of the hazard function for an individual neuron was retained at all levels of peripheral drive (Fig. 12). In fact, the hazard func-


SOMATOSENSORY

STIMULUS l-50-

UNIT 96-3-G

I

0.25-j 40

180 FREQ. (IMP/SEC) 1.001 UNIT 69-6-B

601

CODING UNIT 69-2-0

I

0

FREQ. i IMP/SEC)

6o

0.25I 10

1

I

I

I

FREQ. (IMP/SEC)

1

I

1

100

FIG. 11. Linear regression analysis of the relation between CV and mean fi ring frequency for the three SI neurons for which the SD-MI relations hips are plotted in Fig. 10. For neurons 69-2-B and 69-6-B there wa .s a statistically significant linear relationship between CV and firing frequency of the form CV = m’ ‘frequency + b’. Neuron 96-3-G, whose SD-MI relationship has a statistically zero y intercept, has a CV-frequency relationship whose slope is not significan tly different from zero. When change in time scale is considered, the parameter, m’, of this relationship equals they intercept of the S D- MI relations hip; parameter b' is the same as the slope of the S D-MI relationship.

tion for an individual neuron retained a similar appearance even when it was computed for periods of spontaneous activity.

Comparison of variability of spontaneous and stimulus-driven discharge activity Measures of the variability of spontaneous somatosensory cortical neuron activity were obtained in order to determine if the variability in the timing of spike discharges evoked from SI cortical neurons by peripheral stimulation differs from the variability observed in the absence of intentional stimulation. By substituting the MI for the spontaneous activity of a neuron into the regression equation relating S D and MI for driven activity (for that same neuron) and then solving for SD, we obtained an estimate of the SD for spontaneous activity. This prediction of the SD for spontaneous activity was then compared with the actual SD computed directly from the it spontaneous activity. Using this approach, became obvious that the predicted SD for cortical neuron spontaneous activity corresponded closely to the actual value only when the SD-MI relationship of the neuron had a significant negative y intercept and large slope. In addition, it was a consistent finding that the prediction of the SD for spontaneous activity of an SI neuron, whose SD-MI relationship had either zero or positive y intercept and small slope, underesti-

mated that actually measured. The conclusion is, therefore, that the variability in the timing of spike discharge for SI neurons which exhibit S D-MI relationships with negative y intercepts and large slopes does not change in the transition from stimulus driven to spontaneous activity; whereas, those neurons with SD-MI relationships with positive y intercepts and small slopes do display significantly different variability under the two conditions. Figure 13 shows that this relation is systematic, i.e., as the slope value of the S D-MI relationship for driven activity increases (and thus, as the y intercept value becomes more negative-see Fig. lo), the predicted value of the SD for spontaneous activity obtained from the SD-MI relationship and its actual value come into closer agreement. DISCUSSION

Comparison of variability in somatosensory cortical and thalamic neuron discharge The first systematic accounts of variability in the discharge activity evoked from central somatosensory neurons by controlled stimulation of peripheral mechanoreceptors were published in the early 1960’s (7, 18). In these studies, it was determined that the standard deviation (S D) of a population of interspike intervals collected dur-


602

WHITSEL, 0.2

SD=0.415

SCHREINER,

AND ESSICK

(MI) + 1.633 75.40/SEC

1 1

0.25

fy

1 SD=O.880

E 2Fr

45.55,SEC

(MI) - 0.386

cn ;

175.40/SEC j/ y106.28/SEC

$

yv

FIG. 13. Agreement (expressed in percent) between the prediction of the SD for spontaneous activity and the actual value of the SD as a function of the slope parameter of the neuron’s SD-MI relationship. Data from 118 SI neurons are illustrated. 100% indicates that the predicted and the actual values of the SD for spontaneous activity were identical; a value less than 100% indicates that the predicted SD underestimated the actual SD for spontaneous activity.

60.88/SEC

= 0El----0.1 1 SD=1.276

SLOPE

50 (MI) - 6.467

82.95/SEC 53.70/SEC

l/SEC 02.24 0

MS

50

FIG. 12. Hazard function computed at three different levels of peripheral drive for each of three different S I neurons. See text for details. Each small arrow designates the value of the hazard estimate at the mean interspike interval.

ing a stationary period of thalamic (nucleus ventroposterolateralis) somatosensory neuron discharge was, for both cutaneous and joint neurons, a constant fraction of the mean interspike interval (MI). In contrast to the population of cortical neurons sampled in the present study, the relationship between SD and MI of stimulus-driven activity differed only slightly from one thalamic neuron to the next; i.e., for all thalamic neurons, the relationship was S D = m*MI (mean value of m was 0.63; range was 0.47 - 0.84). This relationship was suggested to be a critical factor in the determination of sofor matosensorv discrimination thresholds,

when the SD of discharge activity is reduced as firing frequency increases, there is marked improvement in the capacity to discriminate between neural responses at the upper end of the response continuum, relative to that which is possible when SD remains constant at all levels of peripheral drive ( 18). The constant linear relationship between SD and MI would thus offset, at least partially, the negatively accelerating power law relation between thalamic neuron response and stimulus intensity. In the present study we have observed that the SD of stationary periods of discharge activity elicited from cutaneous neurons in either SI or SIR is a linearly increasing function of the MI, just as it is for the thalamic neuron population studied by Mountcastle and his co-workers (7, 10, 18). Accordingly, the relationship between SD and MI, which was determined for each of the cortical neurons sampled in this study, also would enable improved discrimination by acting to offset, at least partially, the prominent tendency for the cortical neuron stimulus-response relation to saturate at the upper end of the stimulus continuum. On the other hand, examination of the relationship between SD and MI exhibited by different cortical neurons reveals major differences between cortical neurons and the thalamic neurons described in the previous studies. In the first place, the y intercept value of the S D-MI relationship for individual cortical neurons may be significantly nonzero, whereas for thalamic neurons it is always zero (18); and second, the y intercept and slope values for different cortical neurons may


Y STIMULUS

differ markedly, whereas these values differ insignificantly from one thalamic neuron to the next ( 18). Furthermore, for somatosensory cortical neurons, the slope of the relationship between SD and MI and the y intercept value is negatively correlated. Although we initially interpreted the differences between our findings and those reported for thalamic neurons to indicate that the discharge patterns of some cortical neurons differ significantly from that of the thalamic neurons which provide their input, it seems more likely that the observations obtained in this and in the studies of Mountcastle and his co-workers (7, 10, 18) may not be directly compared. More specifically, the thalamic neuron population sampled consisted principally, but not entirely, of neurons activated by joint rotation or limb movement, whereas the neurons of the present study were exclusively activated from cutaneous receptive fields. Until entirely comparable populations of cortical and thalamic neurons are studied, it will remain uncertain whether, and to what extent, the variability in the timing of single-neuron discharge activity evoked by a tactile stimulus is different at thalamic and cortical levels of the somatosensory system. In addition, it remains to be determined if the variability associated with a level of cortical neuron discharge evoked by stimuli such as those employed in this study is identical to the variability associated with the same mean rate of discharge elicited by stimuli applied at right angles to the skin.

Functional meaning of y intercept and slope parameters of SD-MI relationship For each neuron examined in this study, we were able to fit a highly significant linear regression line to the relationship between S D and MI. This was the case both for neurons for which the value of the y intercept was not significantly different from zero, and for neurons for which the y intercept was significantly nonzero. Although it is meaningless to attempt to interpret the role of the y intercept for values of MI smaller than those actually observed (and we do not claim that the regression equation would hold if the unit were driven at higher or lower rates than those observed), the magnitude of they intercept of the regression equation can be demonstrated to be a factor in the stimulus coding within the range of firing frequencies evoked by moving tactile stimulation. Inspection of the regression equation SD = me MI + b makes it evident that at a high firing rate (within the observed range), MI (and m-MI) is small, and thus, the SD of a neuron with a nonzero y intercept is largely determined by the value of b. At the same high firing rate, a neuron with a positive y intercept will have a larger SD than will a unit with a

CODING

603

negative y intercept. A neuron with a zero intercept will have an S D intermediate in value. In general, therefore, at high firing rates the separation of two ISI distributions for a neuron is either increased (if the y intercept is negative) or decreased (if the y intercept is positive) over the separation that exists if they intercept is zero. It is on this basis that we suggest that a neuron with a negative y intercept is better suited to reflect small differences between stimuli that evoke high discharge rates than is a neuron with either a positive or zero y intercept value. At low firing rates the value of MI is large, and the effect of the y intercept value on the magnitude of SD is less consequential than it is at high firing rates. At low firing rates it is the value of the slope parameter that makes the greater contribution to the value of SD. Since neurons with a positive y intercept tend to have the smallest slope values, the SD of the discharge activity for these neurons is smaller than it would be for neurons with either zero or negative y intercept values. Accordingly, units with positive y intercept values are able to discriminate better at low firing rates than can neurons with zero or negative y intercept values. The general conclusion that emerges from these considerations is that the value of the y intercept influences discriminations attempted when the neuron is firing at high frequencies (the negative y intercept reduces the SD at high frequencies), and the slope value influences the capacity for discrimination when the neuron is firing at low frequencies (a small slope reduces the S D at low firing frequencies). Furthermore, the fact that slope and y intercept are negatively correlated is consistent with the idea that one of the three populations of SI neurons detected in this study is better suited to carry out discriminations when the stimuli lead to high-frequency discharge (the members of this population possess both negative y intercepts and large slopes), and that another population of SI neurons is better suited to carry out discriminations when the stimuli lead to low-frequency discharge (the members of this population possess both small slopes and positive y intercepts).

Role of SD-MI relationship in coding of stimulus intensity In order to illustrate our view of the role of the y intercept and slope parameters of the S D-MI relationship in the central neural representation of somatosensory stimulus events, we have followed the example of Werner and Mountcastle (18) and have considered neural discrimination in the context of a statistical decision process (see Fig. 14). Plotted at the top of Fig. 14 is the relationship between the neuron discharge rate (R) for a hypothetical cortical neuron and the intensity (I - &) of the excitatory stimulus. This


604

WHITSEL,

I

SCHREINER,

);i

, IiI

1

i IiL

1

‘A INTENSITY NEURON

AND ESSICK

’

! t’1

’

‘B (l-l+)

TYPE

RA 0.25

I SD = CONSTANT

1

’

RB

-

1

0.25 II SD = 0.63

(MI)

I

I

4 0.25

SD = 0.2

(MI)

t

3

0.25 IV SD = 1.1 (MI)

- 6

50 MEAN

I NTERVAL

FIG. 14. Effect of parameters of the SD-MI relationship on neural response discrimination (adapted from Werner and Mountcastle, ref 18). Top: power law relation between stimulus intensity (e.g., velocity) and cortical neuron response magnitude (R = neural response in impulses/s). Bottom: for each class of cortical neuron (types I-IV), two pairs of probability distributions are plotted to illustrate the capacity of each type of neuron to discriminate between the stimulus pairs positioned at the lower (IA) or upper (Is) ends of the intensity continuum. Given the MI of the response to a particular stimulus intensity (obtained from the power law relation shown at the top) and its SD (obtained from the appropriate SD-MI relationship), the probability distribution of interspike intervals was computed (normality was assumed). For each neuron type the distribution with the smaller mean interval is plotted as a broken line. It can be seen that stimulus pair IA is best discriminated by the type III neuron, whereas stimulus pair I, is best discriminated by the type IV neuron.

reis described by the power function R = upper (IB) ends of the response continuum, where I, is the stimulus spectively. The members of each stimulus pair 24 -I- 13.9*(1 - It)0-42Y; intensity at threshold. From this relationship, ( IA,loWer, IA,upper; IB,lower, IB,upPer) are separated relation

four responses UL,lower~ %upperr ~~~~~~~~~ RB,up,,,) are identified, which were evoked by two pairs of stimuli positioned at the lower (IA) and

by equal increments in stimulus intensity. The bottom part of Fig. 14 shows that the discriminability of the IS1 distributions, computed


SOMATOSENSORY

for the pair of responses generated by each stimulus pair, depends on the manner in which cortical neuron variability is related to mean firing The IS1 distributions for the refrequency. sponses to stimulus pairs I* and IB are illustrated: a) for a neuron in which the SD of the interspike intervals remains constant at all levels of firing (a type I neuron); b) for the neuron in which the SD is a constant fraction of the MI (SD = 0.63. MI, a type II neuron); c) for a neuron in which the S D-MI relation is linear and possesses a positive y intercept and small slope (SD = 0.2mMI + 3, a type III neuron); and finally, d) for a neuron in which the SD-MI relationship is linear and possesses a negative y intercept and large slope (S D = 1.1. MI - 6, a type IV neuron). The amount of overlap of each pair of distributions provides a quantitative estimate of the discriminability of the two IS1 distributions computed from the responses evoked by each stimulus pair. In general, the analysis summarized in Fig. 14 demonstrates that the y intercept and slope parameters of the S D-MI relation for a somatosensory cortical neuron tune a cortical neuron so that it can transmit more stimulus information than can neurons belonging to the other categories when it is firing within a restricted frequency range (e.g., within this range of firing frequencies, the neural response continuum can be subdivided into a larger number of discriminable categories than would be possible if the neuron belonged to any of the three remaining categories of SI neurons). More specifically, the type III cortical neurons are better than any other type when the stimuli produce low firing rates, whereas the type IV are better than any other type when the stimuli elicit high frequency discharge. The experiments of this study have demonstrated that, with the exception of type I neurons, each of the neuron types illustrated in Fig. 14 can be found within those regions of area 3-l of SI and area SII/r which represent the hairy skin of the proximal limbs When considered in this context, the continuous distribution of y intercept and slope parameters across the SI and SII/r neuron population becomes meaningful; i.e., there is no region of the response continuum at which there is an absence of tuned cortical neurons. The analysis summarized by Fig. 14 incorporates the assumption that, at all levels of peripheral drive, the IS1 distributions for cortical neuron discharge are symmetric and mathematically identical except for change in mean and-standard deviation. Our experimental observations indicate, however, that this assumption is only rarely justified (see Fig. 8A and B) and, as a consequence, we have a) developed a method to characterize quantitatively the change in IS1 distribution symmetry as mean firing rate is varied,

STIMULUS

CODING

605

and 6) determined the effects of experimentally observed changes in IS1 symmetry on the capacity of SI neurons to signal a change in stimulus conditions by a change in mean firing rate. In general, this as yet unpublished work demonstrates that the effect on discrimination of the changes in IS1 symmetry is less significant than are changes in the slope and y intercept parameters of the neuron’s SD-MI relationship. The clear association between the form of the hazard function for a cortical neuron and the values of the y intercept and slope of its SD-MI relationship suggests a variety of cortical mechanisms that may lead to the different discharge patterns exhibited by the different populations of cortical neurons. For example, the different types of cutaneous cortical neurons may differ in their capacity to recover following an action potential or may exhibit differences in the random component of the excitatory synaptic drive (3-5). These alternative mechanisms can only be tested in subsequent studies using intracellular recording techniques. It also should be recognized, however, that the mechanisms which determine the form of a hazard function computed for cortical neuron activity may not be operative at cortical levels; e.g., the present study has not eliminated the possibility that the different categories of somatosensory cortical neurons may be reflecting passively the discharge patterns of different populations of thalamocortical projection neurons.

Is there a differential distribution of neurons belonging to different SD-MI categories within SI? Our sample indicates that over 60% of the neurons studied in area 3-l have SD-MI parameters (negative y intercept and large slope) that tend to reduce the SD of discharge activity at high firing rates and to increase the SD at low firing rates On the other hand, only 16% of the neurons recorded from area 3-l exhibited parameters (positive y intercept and small slope) that would favor neural discrimination at low firing frequencies. Although these differences in proportions indicate that area 3-1, as a whole, is better tuned to discriminate between stimuli that elicit high rates of discharge activity, it is uncertain whether the cutaneous neurons in areas 3a and l-2 of SI (cf. ref 21) are similarly tuned. Moreover, in view of the demonstrations that I) areas l-2 and 3a in the proximal hindlimb field of SI receive their input from spinal pathways other than the dorsal columns (2), and 2) the types of cutaneous first-order afferents which leave the fasciculus gracilis prior to cervical levels and project to the border zones of ST, frequently exhibit SD-MI relationships having positive y intercepts and low slopes, it seems


WHITSEL,

606

SCHREINER,

possible that areas 3a and l-2 may be better tuned to discriminate between stimuli-evoking low rates of discharge than is area 3- 1. The question of if, and to what degree, there exists a significant correlation among somatosensory neuron variability, relative capacity to carry out fine discriminations at different ranges of firing rates, and cortical cytoarchitecture cannot be answered until the neuron populations in areas 3a and l-2 are systematically studied. ACKNOWLEDGMENTS

The authors acknowledge the contributions of Mr. J. Fierst, who developed the software for data collection with the LINC-8 computer, Mr. L. Spalla, who

AND ESSICK

designed and constructed the mechanical stimulators employed, Mrs. C. Metz, who provided histological services, Mr. E. Allen, who participated in data analysis and text and figure preparation, and Dr. S. HaIaby of Corning Glass Works in Raleigh, North Carolina, who kindly provided access to the necessary equipment for converting LINC tapes to a format suitable for analysis on a PDP-H/20 computer. C. Vierck contributed valuable comments and suggestions, which led to the revision of the initial version of this paper. G. K. Essick is a predoctoral student in the Department of Physiology of the University of North Carolina. He is also a candidate for the D. D.S. degree in the University of North Carolina School of Dentistry.

REFERENCES 1. BENDAT, Datu:

J. S. AND

Analysis

and

PIERSOL, Measurement

New York: Wiley-Interscience, 2.

3.

4.

5.

6.

7.

A. G. Random Procedures.

1971, p. 122-125,

235-237. DREYER, D. A., SCHNEIDER, R.J., METZ,C. AND WHITSEL, B. L. Differential contributions

B., of in post-

spinal pathways to body representation central gyrus of Macaca mulatta. J. Neurophysiol. 37: 119-145, 1974. GOLDBERG, J. M., ADRIAN, H. O., AND SMITH, F. D. Responses of neurons of the superior olivary complex of the cat to acoustic stimuli of long duration. J. Neurophysiol. 27: 706-749, 1964. GOLDBERG, J. M. AND GREENWOOD, D. D.Response of the dorsal and posteroventral cochlear nuclei of the cat to acoustic stimuli of long duration. J Neurophysiol. 29: 72-93, 1966. JANSEN, J.K., NICOLAYSEN, K., AND RUDJORD, T. Discharge pattern of neurons of the dorsal spinocerebellar tract activated by static extension of primary endings of muscle spindles. J. Neurophysiol. 29: 1061-1086, 1966. MOORE, G. P., PERKEL, D. H., AND SEGUNDO, J. P. Statistical analysis and functional interpretation of neuronal spike data. Ann. Rev. Physiol.

28: 493-522, 1966. MOUNTCASTLE, V. B., POGGIO, G. F., AND WERNER, G. The neural transformation of the

12. 13. 14.

15.

by F. 0. Schmidt. New York: Rockefeller Univ. Press, 1970, p. 597-604. 16. STEIN, R. B. The stochastic properties of spike trains recorded from nerve cells. In: Stochastic Point Processes: Applications,

8.

9.

Med.

Intern.

W.H., SAKATA, neuronal mechanisms in flutter-vibration studied in unanesthetized monkeys. Neuronal periodicity and frequency discrimination. J. Neurophysiol. 32: 452-484, PERKEL,

1969.

391-418,

1967.

D. H., GERSTEIN, G.L., AND MOORE, G. P. Neuronal spike trains and stochastic point processes. I. The single spike train. Biophys. J. 7:

10. POGGIO, G. P. AND VIERNSTEIN, L. J. Time series analysis of impulse sequences of thalamic somatic sensory neurons. J. Neurophysiol. 27: 517-545,

1964.

11. POWELL, T. P. S. AND MOUNTCASTLE, V. B. The cytoarchitecture of the postcentral gyrus of

Statistical

Analysis,

Theory

and

edited by P. A. W. Lewis. New York: Wiley-Interscience, 1972, p. 700-73 1. E. J. Probability density estimation. I. 17. WEGMAN, A summary of available methods. Technometrics 18.

sensory stimulus at the cortical input level of the somatic afferent system. Intern. Congr. Physiol. Sci. 22nd, Leiden, 962; Excerpta Congr. Ser. 49: 196-217, 1962. MOUNTCASTLE, V.B., TALBOT, H., AND HYVARINEN, J. Cortical

the monkey Macaca mulatta. Bull. Johns Hopkins Hosp. 105: 108-132, 1959. ROSENBLATT, M. Remarks on some nonparametric estimates of a density function. Ann. Math. Statist. 27: 832-837, 1956. SANDERSON, A. C., KOZAK, W. M., AND CALVERT, T. W. Distribution coding in the visual pathway. Biophys. J. 13: 218-244, 1973. SCHREINER, R. C., ESSICK, G. K., AND WHITSEL, B. L. The effect of changes in IS1 distributions on frequency coding. Proc. Sot. Neurosci. 5: 654, 1975. STEIN, R. B. The role of spike trains in transmitting and distorting sensory signals. In: The Neurosciences: Second Study Program, edited

14: 533-546, WERNER,

1972.

G. AND MOUNTCASTLE, V. B. The variability of central neural activity in a sensory system, and its implications for the central reflec26: tion of sensory events. J. Neurophysiol. 958-977, WERNER,

1963.

G. AND WHITSEL, B. L. Topology of body representation in somatosensory area I of primates. J. Neurophysiol. 31: 856-869, 1968. 20. WERNER, G. AND WHITSEL, ture detection by neurons in somaaosensory areas I and II of primates. IEEE Trans. Man-Machine Systems 11: 36-38, 1970. 21. WLQITSEL, B. L. AND DREYER, D. A. Comparison of single unit data obtained from the different topographic subdivisions of the postcentral gyrus of the macaque: implications for the organization of somatosensory projection pathways. Exptl. Brain Res. In press. 22. WHITSEL, B.L., DREYER, D. A., AND ROPPOLO, J. R. Determinants of body representation in postcentral gyrus of Macaques. J. Neurophysiol. 19.

34: 1018-1034,

23. WHITSEL,

1971.

B.L., LOE, P. R., AND~CHREINER,

R.


SOMATOSENSORY

STIMULUS

C. Response of somatosensory cortical neurons to moving tactile stimuli. Proc. Sot. Neurosci. 4: 24.

25.

477, 1974. WHITSEL, WERNER,

B. L., PEITRUCELLI, L. M., AND G. Symmetry and connectivity in the map of the body surface in somatosensory area II of primates. J. Neurophysiol. 32: 170-183, 1969. WHITSEL, B. L., ROPPOLO, J. R., AND WERNER,

CODING

607

G. Cortical information processing of stimulus motion on primate skin. J. Neurophysiol. 35: 26.

691-717, WHITSEL, K., AND

1972.

B. L., SCHREINER, R. C., ESSICK, G. P. R. Statistical analysis of the spike trains evoked from somatosensory cortical neurons by moving tactile stimuli. Exptl. Brain Res. In press. LOE,


Spontaneous single cell discharge in rat somatosensory cortical slices and its relationship to discharge in the urethane-anaesthetized rat.

Far-field and cortical somatosensory evoked potentials in motor neuron disease.

A proportional relationship between peripheral innervation density and cortical neuron number in the somatosensory system of the mouse.

Cortical somatosensory magnetic responses in multiple sclerosis.

Cortical astrocytes rewire somatosensory cortical circuits for peripheral neuropathic pain.

Prediction of primary somatosensory neuron activity during active tactile exploration.

Variability of somatosensory cortical evoked potentials during spinal surgery. Effects of anesthetic technique and high-pass digital filtering.

Interpositus neuron discharge in relation to a voluntary movement.

Jaw movement-related primary somatosensory cortical area in the rat.

Task-Specific Somatosensory Feedback via Cortical Stimulation in Humans.

Pain-related somatosensory evoked potentials in cortical reflex myoclonus.

Finger dexterity deficits in Parkinson's disease and somatosensory cortical dysfunction.

Somatosensory cortical barrels and thalamic barreloids in reeler mutant mice.

Visual and somatosensory evoked cortical potentials in multiple sclerosis.

Persistent pain inhibits contralateral somatosensory cortical activity in humans.

Somatosensory evoked cortical responses in children with spina bifida.

Mechanisms underlying somatosensory cortical dynamics: II. In vitro studies.

EEG feedback training: enhancement of somatosensory cortical activity.

Multilevel Cortical Processing of Somatosensory Novelty: A Magnetoencephalography Study.

Synaptic variability in a cortical neuromorphic circuit.

Discharge planning in chronic conditions: an evidence-based analysis.

Somatosensory cortical-evoked responses after feline experimental spinal cord injury.

Sensory incongruence leading to hand disownership modulates somatosensory cortical processing.

Positive prognostication from median-nerve somatosensory evoked cortical potentials.