Neuronal multipotentiality: evidence for network representation of physiological function

Brain Research, 580 (1992) 49-61 Elsevier

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Neuronal multipotentiality: evidence for network representation of physiological function Erwin B. Montgomery, Jr. a, Margaret H. Clare b, Shirley Sahrmann b, Steven R. Buchholz b, L y n d o n S. H i bb ard b and William M. L a n d a u b QDepartment of Neurology, The University of Arizona Tucson, AZ (USA) and bDepartment of Neurology and Neurosurgery [Neurology], Washington University, School of Medicine, St. Louis, MO (USA) (Accepted 24 December 1991)

Key words: Single neuron recording; Neuronal network

Extracellular action potentials of single neurons in motor cortex and rectified and integrated electromyographic activity (EMG) of gastrocnemius and anterior tibialis were recorded while a monkey performed isometric ankle plantar and dorsal flexion tasks. This study determined the consistency-of neuronal behaviors across different tasks. Methods characterized neuronal behaviors by determining which behavioral event within a single task, such as the appearance of the 'go' signal, force onset, or agonist and antagonist EMG onset, was best related to changes in neuronal activity. Another method compared the temporal profiles of discharge modulation across different tasks. Of 220 neurons recorded, 44 were selected because they were consistently active in the tasks. Of these, 37 were in the precentral cortex and the remaining seven were in the postcentral cortex. Only 14 of the 33 in motor cortex were consistent in their behavioral correlations. Several had multiple changes in activity within a single task that were related to different behavioral events. Half were consistent for direction of force and a third were consistent for magnitude of force. Furthermore, there was little consistency in the temporal profiles of discharge activity for all 44 neurons across tasks. Similar modulations of discharge activity among neurons in. one task were different in another task. Such inconsistencies are evidence against the cardinal cell hypothesis of physiological representation. We offer a new hypothesis analogous to connectionism in parallel distributed processing. INTRODUCTION Recording single n e u r o n activity in performing animals and correlating changes in neuronal activity with behavior have greatly increased our understanding of m o t o r function 14. While successful in m a n y applications, such analyses often have b e e n inconsistent. A t t e m p t s to provide a comprehensive identification and organization of neurons based upon different behavioral correlations have been frustrated by these inconsistencies 6'2°. For example, in the cerebellum most d e n t a t e neurons are directionally specific for fast m o v e m e n t s but a p p e a r to lose specificity during slow m o v e m e n t s 24. The activity changes of m a n y m o t o r cortical neurons correlate with force in a jaw bite task but not with n o r m a l chewing m o v e m e n t s , despite the a p p a r e n t similarity of the m o t o r acts 1°. Motor cortex neurons m a y be tightly linked, using postspike triggered averaging of electromyographic activity ( E M G ) , to a muscle during a task but not to the same muscle active in a n o t h e r task 6. While this variability of behavioral correlation is generally a p p r e c i a t e d by investigators in the field, the unresolved question is whether

the variability is due to m e t h o d o l o g y or is a m a t t e r of principle. The primary assumption underlying single neuron recording in performing m o n k e y s is the cardinal cell hypothesis, which holds that behavioral functions are uniquely and consistently e n c o d e d in the discharge activity of individual neurons 3'25. If the variability of neuronal a c t i v i t y - b e h a v i o r a l event correlations is a m a t t e r of principle, then the cardinal cell hypothesis and much of the knowledge based on its application in single neuron recording will have to be re-evaluated. The methodological p r o b l e m s of correlating single neuronal activity changes with behavioral events are considerable 16. T h e r e is the issue of 'question begging', in which the structure of the analysis determines the results. F o r example, changes in a n e u r o n ' s activity within a single task m a y be found to be correlated with an observed event only because the observed event is linked to some unobserved event with which the n e u r o n ' s activity is actually correlated. ' Q u e s t i o n begging' relative to the consistency of neuronal a c t i v i t y - b e h a v i o r a l event correlations across differ-

Correspondence: E.B. Montgomery Jr., Department of Neurology, The University of Arizona, Arizona Health Science Center, 1501 N. Campbell A~,e., Tucson, AZ, 85724, USA.

50 ent tasks also occurs. Clever behavioral paradigms have been constructed to identify a neuron's function. For example, movements can be made under different load conditions which allow a separation of direction from patterns of muscle activity23. Flexion at the wrist under no load conditions requires activity of the flexor muscles, while under a flexor load it is accomplished by extensor muscle relaxation. Neurons whose activity changes consistently with flexor muscle activity regardless of load conditions and therefore, different movement directions, would be labeled as neurons which function to control the flexor muscles. However, a neuron which changes its behavioral function under different task conditions, i.e. related to muscle in one task and movement direction in another, would escape classification or yield paradoxical results. Generally, these difficult neurons are excluded from further analysis. As a consequence, studies tend to select those neurons which are consistent, and therefore, are amenable to analysis. This is 'question-begging' and conveys a false impression in support of the cardinal cell hypothesis. To test whether variability of individual neuronal-behavioral event correlations and, by inference, physiological function is a matter of principle requires analysis methods which do not beg the question. This paper addresses this issue by using two different approaches. The first utilizes analytic techniques that correlate neuronal activity changes with behavioral events within an individual task. These methods are an extension of a method introduced by Evarts 7. While not completely free from the above-described methodological problems, the structure of the analyses at least does not presuppose consistency of behavioral correlations across different tasks. The second approach avoids individual neuronal activity-behavioral event correlations altogether by examining the consistency of the temporal profiles of neuronal discharges across different tasks.

key to relax to the rest condition. In the case of the large force holds, the monkey made a self-initiated rapid reversal to a large force target in the opposite direction followed by an immediate return to the rest condition. After training, a head holder and recording chamber were secured to the m o n k e y under sterile conditions using general anesthesia 5. Glass insulated platinum-iridium microelectrodes were inserted transdurally into the motor cortex. Extracellular action potentials of a single amplitude were counted in 10 ms bins. Ankle forces and full wave rectified and integrated E M G of the anterior tibialis and gastrocnemius were sampled every 10 ms using surface Ag/AgCI electrodes 3 m m in diameter. The experiments were controlled by a 6502 microprocessor which also collected data for each trial. Between trials, data were transferred to a V A X computer system for both on-line and later off-line analysis. Classification based upon behavioral correlations T h e degree of relatedness of a n e u r o n ' s activity to a behavioral event was determined in three ways. The first two methods are extensions of a m e t h o d first described by Evarts 7, based on the vari-

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MATERIALS AND METHODS A Macaca nemestrina monkey was trained to perform isometric ankle dorsal and plantar flexion force tasks in response to visual cues which indicated force targets and visual indicators of the ankle forces generated. The tasks included dorsal flexion to a small force (SD), plantar flexion to a small force (SP), dorsal flexion to a large force (LD), and plantar flexion to a large force (LP) (Fig. 1). Each performance began with a r a n d o m hold time at rest in which the force target light indicating neutral position was matched with a cursor light indicating no force generated. T h e n , the monkey exerted a plantar or dorsal directed force to a small force window (1,729-3,549 gram-centimeters) or to a large force window (greater than 4,095 gram-centimeters) in response to the force target light changing from rest to the appropriate force level (go signal). This was followed by maintained force during a random time period of between 0.5 and 1.5 s. In the case of the small forces, the force target light returned to the rest level, requiring the mon-

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and b comparison than in the a' and b' comparison. The methods used are summarized in Fig. 4 and have been described previously 17. The description is repeated here for clarity. Summed peri-event histograms of neuronal activity for the complete set of successful trials of a specific task were centered upon several behavioral events. These include the go signal, onset of initial force change, achievement of required force level (reaching target) and onset of agonist and antagonist E M G activity (Fig. 4C). The degree of relatedness of an actual neuron's activity to a behavioral event was determined in three ways. In the first method (degree of change) the summed spike counts across multiple trials in a 100-ms window (10 bins) were compared to spike counts in an adjacent 100-ms window (10 bins) using a non-parametric statistical method (Kolmogorov-Smirnov two-sample test) (Fig. 4D). The two 100-ms windows were moved through time at 10-ms intervals over a period from 500 ms before to 500 ms after the time of the behavioral event. The Z2 value was plotted (Fig. 4A). When the graph of the Z2 value reached the 0.05 level, the neuronal activity within the window was significantly different from the baseline to a P value of 0.05; similarly for the 0.01 and the 0.001 levels. One-hundred-ms windows were necessary to have sufficient data for statistical analyses. This is especially important in view of the low neuronal discharge rates and the number of trials it is possible to record from a single neuron. Furthermore, smaller windows would increase the possibility of transient variations in neuronal discharges being erroneously labeled as significant. The second method (change from baseline) is similar except that neuronal activity within the 100-ms window was compared to the activity during 500 ms of the hold period which preceded the 'go' signal (Fig. 4E). The results are shown in Fig. 4B. Thus, the histogram with the lowest P value indicates that the best relationship of neuronal activity is to the behavioral event upon which that histogram was centered.

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ability of the monkey's performance. Rasters of a neuron's activity from our study are shown in Fig. 2. Each vertical dash in a row indicates a neuronal discharge. Each row represents a single trial. The raster A orders the trials based on their occurrence, with the first trial at the top and the last trial at the bottom. When data from the same trials in the raster were ordered according to the monkey's reaction time (raster B in Fig. 2), the time at which the monkey initiated a response positively correlated with the time of neuronal activity change. This is confirmed by visual inspection of raster C which contains neuronal activity centered on force onset. Thus, the neuronal response was temporally locked to ankle force onset rather than to the go signal. The method used here extends Evarts' rationale by relating neuronal activity changes to multiple behavioral events. Thus, rasters aligned on the most relevant behavioral event have the most consistent alignment of neuronal activity. Fig. 3 is a schematic representation of rasters for a hypothetical neuron whose activity changes are most tightly linked to force onset. A hypothetical neuron is shown for the sake of clarity. Raster A is aligned on the go signal and raster B aligns the same data on force onset. Because the reaction times are variable, the two rasters do not have the same structure. Spike counts for each trial within two adjacent time windows are compared in each of the two histograms. The distributions of the spike counts per trial within each window are shown in a, b, a' and b' (Fig. 3C and D). There is greater variability in a and b compared to a' and b'. A comparison of a and b using a non-parametric statistical method (Kolmogorov-Smirnov two-sample test) will show a lower Z2 value than a comparison of a' and b'. Thus, the P value will be greater in the a

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Fig. 3. Summary of the bases for the first two analytic methods for a hypothetical neuron 17. Rasters of the same neuronal activity for five trials are centered on the go signal (A) and force onset (B). Since there is variability in the reaction times, the two rasters will not have the same structure. Two adjacent windows of neuronal activity are compared in each raster (a and b and a' and b'). The distributions of spikes per trial within each window are shown in C and D. There is greater variability in the distribution of spikes per trial in a and b compared to a' and b'. The Zz value will be lower (higher P value) in the a to b comparison than in the a' and b' comparison. This confirms the visual impression that the change in neuronal activity is better related to force onset. (Reproduced with permission.)

52 The third method (intertrial variance) identifies homologous regions in the trains of neuronal activity for each individual trial and measures their latencies to behavioral events (Fig. 4F). It is assumed that the set of latencies with the least variance indicates the neuronal activity that is most tightly linked to that event 16. In practice, the results of these methods were generally consistent. Occasionally, the second method (change from baseline) showed a significant change when the first method (degree of change) did not. In these situations, the second method was used to assign a behavioral characterization to the neuron. When the first method showed a significant change, the second method always reached the minimum P value which would not allow for a distinct assignment of behavioral type. In this case the first method was used to characterize the neuron. The third method (intertrial variance) was used when the plots of the Z2 value in both the first and second method reached the minimum P value. Some neurons were encountered for which all histograms reached the minimum P value and the latency variabilities for the third method were at the minimum for more than one behavioral event. The behavioral correlations for these neurons were judged to be indeterminant for that task. An example of an initial force onset-related neuron is seen in Fig. 5. Peri-event rasters, histograms and the associated plots of the Z2 values are centered on the go signal (Fig. 5A and C) and initial force onset (Fig. 5B and D). Plots of the Z2 values for the first method using two adjacent windows (upper trace in Fig. 5C and D) reach the lowest P value for the histogram centered on initial force onset (Fig. 5D). This implies that the change in neuronal activity is better related to initial force onset than to the go signal. The second method (lower trace) reaches the minimum P value for both histograms and does not permit a distinction. An example of a go signal-related neuron is represented in Fig. 6. Rasters and histograms are organized on the occurrence of the go signal (time 0 in Fig. 6A and D), force onset (time 0 in Fig. 6B and E) and the onset of agonist EMG (Fig. 6C and F). Fig. 6G shows the rectified and integrated agonist EMG for several trials. The timing of organization is the same for the neuronal activities in Fig. 6C, F and G. It is clear that the neuronal activities are most consistently organized when centered on the go signal. Thus, the

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Neuronal discharge pattern analyses A classification scheme was developed based upon the pattern of neuronal discharge over the course of a task. Consistency of the neuronal activity representation of a physiological parameter (e.g. force, direction, behavioral event correlation) implies consistency of a neuron's discharge pattern in different tasks which contain the same physiological parameter. However, the exact discharge pattern of a single neuron may vary in different behavioral contexts associated with different tasks although still consistently correlated with the physiological parameter. An example is shown in Fig. 7. Peri-event histograms of neuronal

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activity are shown for three neurons (A, B and C) for two tasks. Figs. 1 and 2A,B and C are for the LP task and Figs. 3 and 4A,B and C are for the SP tasks. Both raw and smoothed histograms are shown. The profile of neuronal activities for each neuron are different comparing the two tasks. However, the activity profiles for neurons A and B are similar in both tasks. The activity profiles of neurons B and C are similar only for the LP and not the SP task. Thus, comparison of an individual neuron's discharge patterns across different tasks is problematic. Another comparison is possible, using as a measure the similarity of a neuron's discharge patterns to that of other neurons (Fig. 7). If two or more neurons' discharge patterns are consistent in their representations of a physiological parameter in different tasks,

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54 then the similarities should be maintained in different tasks. Comparison of neuronal discharge patterns between tasks in this study was appropriate because all tasks were a simple reaction time paradigm containing the same behavioral events (go signal, force onset, reaching target forces, and agonist and antagonist E M G activity). Furthermore, at least two tasks were either of the same direction or force magnitude. Comparisons are valid only within a single monkey, as in this study. Neuronal discharge patterns for similarity analysis were constructed from peri-event histograms centered on the initial force onset (Fig. 8A). Hypothetical neurons are used for the sake of clarity. These histograms were smoothed by averaging five adjacent 10-ms bins beginning 500 ms before onset of initial force change and assigning the value to the center bin location. Then, the 5-bin window was shifted in time by one bin (10 ms) and the process repeated until 500 ms after the onset of initial force change. Results of the smoothing are shown in Fig. 8B. The degree of similarity (SM) was determined by finding the Pearson correlation coefficients between discharge patterns for all possible pairs of neurons (Fig. 8C). It is important to note that the similarity between the discharge patterns of two neurons is not only a function of the discharge pattern shapes but also of their temporal relationship. Thus, neurons with similar discharge pattern shapes but offset in time would result in lower Pearson correlation coefficients and be judged dissimilar. Activities of the neurons were not recorded simultaneously. However, comparison of neurons recorded on different days is possible if one demonstrates that the neuronal activity is tightly linked to the behavior and that the behavior is reproducible day-to-day. Neuronal discharge patterns should then be reproducible day-today. The neurons analyzed here were selected on the bases of their consistent and significant correlation with behaviors controlled in the task. Furthermore, the monkey was overtrained in the tasks and its performance was consistent during the period of behavior analyzed (see Results). Comparisons of discharge patterns between individual pairs of neurons would result in large amounts of data which would make interpretation difficult if presented in tabular form. For n equal to the number of neurons in the analysis, there are n(n-1)12 unique

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combinations of similarity measures for each task. Therefore, principal components analysis was used to reduce the tabular data to a single two-dimensional geometrical figure for each task. Principal component analysis can be considered as mapping the discharge pattern similarity data for all n neurons onto a n-dimensional space. Each neuron in turn serves as an axis. The remaining neurons were mapped onto that dimension by their similarity to the neuron serving as the axis. Thus, neurons with the same degree of similarity to the axis neuron would cluster along that dimension. Since most of the variance among the neuronal similarities is concentrated in the first few components, most of the n-dimensional correlations can be reasonably reduced to a two-dimensional space, referred to as the similarity space 4. The plot axes correspond to the first and second principal components so that the distance between any pair of neurons indicates the degree of discharge pattern similarity. Groups of neurons with similar discharges are clustered together in one area and are farther away from neurons with dissimilar discharge patterns. In this way, the similarities among all neurons can be appreciated simultaneously and conveniently and compared over multiple tasks. Consider the hypothetical example depicted in Fig. 8. The smoothed discharge patterns for neurons 1, 2 and 3 are similar (Fig. 8B). In the geometrical representation these neurons are placed close together (Fig. 8D). The smoothed discharge patterns of neurons 4 and 5 are only partially similar to those of neurons 1, 2 and 3. However, neuron 4 is dissimilar to neuron 5. As a consequence, neurons 4 and 5 are close to neurons 1, 2 and 3 but far from each other. Thus, the geometrical representation in Fig. 8E is a concise description of the similarities among all neurons. The distance between each pair of neurons (distance in Fig. 8E) is plotted against their similarity measure (SM in Fig. 8E). The linear regression line allows for calibration of distance in the spatial geometric representation in terms of the similarity. Neurons are represented in the similarity space as a 'star burst' pattern where the radius (length of an arm) is equal to 0.1 unit. Thus, two neurons whose centers in the 'star burst' pattern are within one radius or unit of each other have a Pearson correlation of 0.9. Two neurons spaced 2 radii or units apart indicates a Pearson correlation of

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O f 220 r e c o r d e d neurons, 44 were selected for analysis because of highly consistent neuronal activity changes across trials for at least one of the tasks. In Fig. 9, the anatomical distribution of the electrode penetrations containing the recording sites of these 44 neurons is shown. Thus, 37 neurons were found in the precentral gyrus while seven were found in the postcentral gyrus. M o r e medial regions were not accessible due to technical limitations. A t various times during the experiment, microstimulation was carried out through the recording microelect r a d e (up to 30 g A for 11 shocks at 330 Hz) and the m o t o r responses were r e c o r d e d from visual observations. Locations and results are shown in Fig. 9B. A t other times, the responses to sensory stimuli were r e c o r d e d and the results r e p r e s e n t e d in Fig. 9B. The m o n k e y ' s behavior was consistent across individual trials during a single daily recording session and across the recording sessions during the experiment. Force tracings during the analysis p e r i o d are shown in Fig. 10. Time zero indicates c o m p u t e r detected force onset which required crossing a threshold. Note that

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56 force tracings are drawn to scale within a task. The scales are not the same between tasks but are autoscaled to show the greatest detail. The average force tracings associated with neuronal recordings from each of seven sessions equally spaced throughout the 2-month recording period are shown in figures labeled LP, L D , SP, and SD. Force tracings shown in figures labeled A , D, G, and J are individual trials r e c o r d e d early in this period. The data from recordings o b t a i n e d in the middle of the experiment are shown in figures labeled B, E, H, and K while figures C, F, I, and L are from recordings obtained late in the experiment. Because neurons were selected for consistency of activity changes correlating with the behaviors and because the behaviors were consistent across recording sessions, comparisons of neuronal activities recorded on different sessions are valid. Classification based on behavioral correlations

Of 37 active neurons in the hindlimb m o t o r cortex, the behavioral event to which the neuron's activity change was significantly and best related (i.e. 'go' signal, force onset, reaching force targets, onset of agonist or antagonist E M G ) was identified in each of the four tasks. Two were related only to the force reversals in the large force tasks. These were excluded from further behavioral analysis because comparison to small force tasks was not possible. Two additional neurons were excluded because behavioral correlations were indeterminant. This was because the variance measures for all three methods were minimal for all events across all tasks. Thus, behavioral analyses were possible on 33 of the 37 neurons in the precentral gyrus. Their classifications are listed in Table I. Twelve (neurons 3-14) had significant activity changes in only one task. Nineteen (neurons 1533) had significant changes in m o r e than one task and were inconsistent in their behavioral event correlations. Only two (neurons 1 and 2), which had significant activity changes with m o r e than one task, were consistent in their behavioral event correlations. Some patterns in the behavioral correlations were seen. For example, 10 neurons were related to the go signal during at least one small force task, and to force onset in at least one large force task. Some neurons changed their patterns of activity with respect to different events within the same task. All tasks were stimulus-initiated. H o w e v e r , the force hold periods of the L P and the L D tasks were followed by a self-initiated force reversal. Several neurons had a rapid initial build up of activity which diminished following onset of force change (go response in Fig. l l A ) . Towards the end of the force hold phase, there was a build up of neuronal activity in anticipation of the force reversal ('set' response in Fig. l l B ) . This second phase of

increased activity e n d e d abruptly before onset of force reversal. The initial increase in neuronal activity represents a response to the appearence of the go signal. The latter increases are analogous to the activity of 'set' neurons previously described in m o t o r cortex 22. Thus, within a single task, the neuron changed its activity from responding to a go signal in the stimulus-initiated phase to a 'set' function in the self-initiated phase, anticipating the force reversal. A classification scheme based on the consistency of behavioral criteria across different tasks was established, and the numbers of neurons fitting those criteria were identified (Table II). O n e category (directional-weak) contained half of the neurons; the remaining categories, contained less than a third. Combining neurons related to particular behavioral events (e.g. go signal, initial force change, reaching target and agonist and antagonist E M G activity), only half were consistent across tasks.

TABLE I Behavioral classification

GO = go signal; FO = intial force onset; RT = reaching target; EMG = agonist EMG onset; NSC = no significant change; IND = indeterminant. Neuron

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GO GO GO GO NSC NSC NSC GO NSC NSC NSC NSC NSC NSC GO NSC GO GO GO GO GO GO NSC NSC NSC NSC NSC NSC NSC NSC FO RT GO

NSC NSC NSC NSC GO NSC GO GO NSC NSC NSC FO NSC NSC FO GO FO NSC NSC NSC NSC GO GO GO GO GO GO GO RT NSC GO NSC GO

NSC NSC NSC NSC NSC NSC NSC NSC NSC FO FO FO EMG EMG IND NSC IND NSC FO FO NSC FO NSC FO FO IND FO NSC FO FO IND NSC RT

NSC NSC NSC NSC NSC GO NSC NSC FO NSC NSC FO NSC NSC IND FO IND FO NSC IND IND FO IND NSC NSC NSC NSC FO NSC RT RT GO GO

57 TABLE II

Category

Criteria

Number of neurons meeting criteria

best relationship to forces in only one direction for both force levels, irrespective of behavioral event best relationship to forces in only one direction for either or both force levels, Directional-Weak irrespective of behavioral event best relationship to large forces not small forces irrespective of Large force related direction or behavioral event best relationship to small forces not large forces irrespective of Small force related direction or behavioral event best related to only 'go' signal irrespective of direction or magnitude of force Go signal related best related to only force onset irrespective of direction or magnitude of force Force onset related Achievement of force related best related to only reaching force target irrespective of direction or magnitude of force best related to change in agonist EMG irrespective of force, Onset of agonist EMG related direction or behavioral event Onset of antagonist EMG related best related to change in antagonist EMG irrespective of force, direction or behavioral event Directional-Strong

5 16 7 7 8 4 0 2 0

Neuronal discharge pattern analysis Similarity measures (Pearson correlation coefficients) were determined for the 946 unique pairs of all 44 neurons recorded in both pre- and postcentral gyrus. Dif-

tween their similarity measures would be 0.5. Comparing SP and SD flexion tasks, 320 pairs (34%) showed a difference in their correlations of at least 0.5, as did 388 pairs (41%) comparing LP to L D flexion, 388 pairs (41%) for LP vs. SP, and 161 pairs (17%) for LD vs. SD. Thus, for a third of neuronal-task pairs (1,257 of 3,784) there was a significant change in the similarity measures across

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tasks. The 946 pairs for each task were mapped onto a twodimensional similarity space using the first and second principal c o m p o n e n t s derived from principal components analysis. The first and second m o m e n t s were able to account for 85% of the variance in the SD task, 92% in the SP task, 93% in the LD task, and 94% in the LP task (Fig. 12). Clustering of n e u r o n s (shown in Fig. 12) is defined as the set of n e u r o n s which are within a distance of 0.2 units of an adjacent n e u r o n , m e a n i n g that their Pearson correlational coefficients are at least 0.8 of an adjacent neuron. Individual clusters are represented by different colors in the top left corner graph in each Fig. 12A through D. A n e u r o n at one end of a cluster may be dissimilar (Pearson correlation <0.8) to another n e u r o n at the other end of the cluster, but nevertheless, may be linked by intervening neurons. This justifies their inclusion in a c o m m o n cluster and their having the same color code. If similarity of discharge patterns is preserved across different tasks, then there should be a preservation of

58

Fig. 12. The result of mapping the similarities between neurons onto a two-dimensional space determined by the first and second principal components showing the clustering of neurons. In A, the color code is determined by clustering in the large plantar flexion task and shows that neurons of the same color are scattered in the plots of the other tasks. B shows the results for color coding from large dorsal flexion, C small plantar flexion and D small dorsal flexion. Thus, clustering according to similarity of neuronal discharge pattern is not conserved across different tasks.

the color clustering defined in the top left corner graph of each figure (12A through D) in the remaining graphs in each figure. Thus, neurons of the same colors should remain close together in the different tasks, Fig. 12A shows the distribution and clustering for each task color

coded for the LP task, Fig. 12B for LD, Fig. 12C for SP and Fig. 12D for SD. There is some preserved color clustering for LD and SD tasks. In Fig. 12B, the color code is based on the clustering in the LD task. The clustering of neurons with

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the same color code is somewhat preserved in the SD task but not at all in the LP and SP tasks. Similarly, in Fig. 12D color clustering is somewhat preserved in L D tasks but not in the LP and SP tasks. However, there is virtually no preservation across tasks for clusters defined in the LP task (Fig. 12A) or in the SP tasks (Fig. 12C). Closeness of neurons in the similarity space represents the degree of similarity of their discharge patterns. As a check, the distances between all possible pairs of neurons were correlated with the similarity measure (Fig. 13). Pearson correlation values were 0.66 for the SD task, 0.82 for the SP task, 0.80 for the LD task and 0.78 for the LP task. DISCUSSION While a single m o n k e y was studied, the results of different analysis methods are striking and strongly suggest that neuronal activity-behavioral event correlations are variable in principle and are not due solely to methodological concerns. As such these findings argue against the cardinal cell hypothesis. Based upon behavioral criteria using two different classification schemes, few neurons were consistent for direction or magnitude of force across different tasks or in their correlation with individual behavioral events. The results of the individual neu-

ronal classifications are generally consistent with previous studies using other methods which showed the variability in directional preference and force associations 2°. The apparent large degree of variability in the present study may be due to the large number of degrees of freedom. One neuron's activity could be correlated with 5 variables in 4 tasks. The observed variability is not an artificial consequence of the way the classification schemes were constructed. Independent statistical measures were used to determine which behavioral event within a single task was best related to the change in neuronal activity. The method did not presuppose preservation of behavioral correlations across tasks. Similarity of discharge patterns also was not preserved across different tasks. When comparing similarities between individual pairs of neurons across different tasks, a third of the pairs had significant differences. When examining the distribution of neurons in the similarity space, clustering was not preserved across tasks. Thus, a neuron may have a discharge pattern similar to another neuron for one task but not for a different task which contains the same behavioral events. The question becomes what are the alternatives to the cardinal cell hypothesis for representation of physiological function? One option is the mass action hypothesis which holds that behavioral function is represented not in individual neurons but in the combined activities of groups of neurons 8'12'25. Macroscopic states, such as electroencephalographic activity, emerge as a continuum from the discrete activities of large groups of neurons and can be related to behavioral function without explicit knowledge of local neuronal interconnections 8. A n analogy in single neuron recording is Georgeopoulos's analytic technique which determines a resultant vector from the magnitude of discharge of single m o t o r cortical neurons for different directions of movement 9. This vector implies a directional selectivity represented in the individual neuron's activity. Individual vectors for a set of such neurons are combined into a group vector which correlates with the direction of the associated task. Thus, one overall consistent property, direction of movement, can be abstracted from the behavior of a population of neurons. However, the mass action approach does not account for individual neuron activities or for their interactions. Furthermore, within a single task the neuronal activities do correlate with unique behavioral events. Thus, at the level of a single task, the cardinal cell hypothesis seems to hold. A third approach to behavioral representation comes from recent developments of parallel distributed processing (PDP), or connectionism as contrasted to sequential

60 processing in computer science 19. In sequential processing, a single complex computer processor operates in a single step-wise fashion, while in PDP, multiple simple processors operate simultaneously. In sequential processing, the state of a single processor uniquely specifies the computational solution. In PDP, connections among the simple processors are varied in order to solve computational problems. The solution is represented in the pattern of interconnections, not in the states of the processors themselves. By analogy, the connectionistic hypothesis holds that physiological functions are represented in the patterns of neuronal interconnections and not in the activities of single neurons. Furthermore, neuronal activities are specified by the pattern of neuronal interconnections. The connectionistic hypothesis allows for a specific correlation of neuronal activity change with a behavioral event within a single task in the manner consistent with the cardinal cell hypothesis. Behavioral correlations with individual neuronal activity changes are specified. The patterns of interconnections may be unique and consistent within a single task. Thus, neuronal activity specified by the pattern of neuronal interactions would be consistent within a single task; this would allow correlation with behavioral events. However, different tasks may be associated with different patterns of neuronal interactions which would change the behavioral correlations. Thus, neurons may be multipotential with respect to their functional roles. Neuronal multipotentiality also has been demonstrated for invertebrates ~1. Changes in the pattern of neuronal interconnections or changes of network structure in response to different behavioral contexts, represent a departure from standard PDP. Variable patterns of interconnections are possible if connections are viewed as physiological rather than strictly anatomical, the latter being fixed over the short time course of behavior. Inhibition of interneurons or presynaptic inhibition may block neuronal activation through specific anatomical linkages in one task and a different set of linkages in another task. Variability of neuronal linkage and network structure is evidenced by differences in the degree of cross-correlation between two neurons simultaneously recorded in monkeys under different task conditions 13. Aertsen et al. also have found evidence of modulation of effective connectivity 1. Like the mass action hypothesis, the connectionistic hypothesis appeals to integrative action of neuronal assemblies. However, unlike the mass action hypothesis but consistent with the connectionistic hypothesis, the integrative activity of neuronal assemblies follows directly from the patterns of neuronal interconnections

and activities of individual neurons. Although of heuristic value, a strict distinction between purely parallel and purely sequential processes is artificial. In the solution of the parallel computational problem, some degree of sequentiality is superimposed on the network by virtue of strengthening some connections and weakening others. Similarly, to the degree localization and sequentiality are superimposed on the biological neuronal networks determined by the pattern of interconnections, some aspect of behavioral function will be consistently identifiable with the activities of individual neurons. This is in agreement with the cardinal cell hypothesis. Thus, some degree of preservation of behavioral correlations and similarity of neuronal discharge pattern across tasks will be seen. This may account for the partial successes of previous single neuron studies. In agreement with the mass action hypothesis, to the degree that the biological neuronal network is parallel and distributed, behavioral function will be less consistently identifiable in the behaviors of individual neurons. Behavioral representation will have to be sought in some larger descriptor of the network as a whole, but based on local neuronal interactions consistent with the connectionist hypothesis. The arguments and data presented suggest a large degree of PDP at the neuronal level. However, caution must be used in extrapolation of these results to higher levels of central nervous system organization, such as between functional areas (e.g. motor, somatosensory and striate cortex). Multiple retinotopic 2, somatotopic 17 and motor representations 2~ argue for PDP. But, there is also evidence for sequentiality in the anatomical interconnections between functional areas TM. These results also suggest caution in the use of comparisons of individual neuronal activity changes across different tasks in order to make physiological inferences. For example, significant changes in neuronal activity with force or movement in both directions does not necessarily mean a lack of directional specificity in the physiological function of the structure. Modeling of neuronal interactions in the putamen has shown that for neurons whose activity changes are not directionally specific, the direction of a movement may be encoded in the pattern of interactions 15. Methods are needed which go beyond analysis of individual neuronal activity to identify patterns of neuronal interaction. Acknowledgements. The authors wish to thank Ms. Rose Park for her computer programming. This work was supported by a grant from the American Parkinson Disease Association and its Greater St. Louis Chapter (E.B.M.) and the Jane K. Pelton Fund for Movement Disorders Research (E.B.M.)

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