1Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh 15213-2683; 2Department of Neuroscience, University of Pittsburgh, Pittsburgh 15260; and 3Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Olson, Carl R., Sonya N. Gettner, Valérie Ventura, Roberto Carta, and Robert E. Kass. Neuronal Activity in Macaque Supplementary Eye Field During Planning of Saccades in Response to Pattern and Spatial Cues. J. Neurophysiol. 84: 1369-1384, 2000. The aim of this study was to determine whether neuronal activity in the macaque supplementary eye field (SEF) is influenced by the rule used for saccadic target selection. Two monkeys were trained to perform a variant of the memory-guided saccade task in which any of four visible dots (rightward, upward, leftward, and downward) could be the target. On each trial, the cue identifying the target was either a spot flashed in superimposition on the target (spatial condition) or a foveally presented digitized image associated with the target (pattern condition). Trials conforming to the two conditions were interleaved randomly. On recording from 439 SEF neurons, we found that two aspects of neuronal activity were influenced by the nature of the cue. 1) Activity reflecting the direction of the impending response developed more rapidly following spatial than following pattern cues. 2) Activity throughout the delay period tended to be higher following pattern than following spatial cues. We consider these findings in relation to the possible involvement of the SEF in processes underlying attention, arousal, response-selection, and motor preparation.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The supplementary eye field
(SEF) has been known since its discovery by Schlag and
Schlag-Rey (1985, 1987
) to play a role in oculomotor processes.
Evidence for this role has arisen from studies involving both
electrical stimulation and single-neuron recording. Electrical
stimulation of the SEF at reasonably low currents (<50 µA) elicits
saccadic eye movements (Chen and Wise 1995b
;
Fujii et al. 1995
; Lee and Tehovnik 1995
;
Mann et al. 1988
; Mitz and Goldschalk
1989
; Russo and Bruce 1993
; Tehovnik and
Lee 1993
; Tehovnik and Sommer 1997
;
Tehovnik et al. 1994
; Tian and Lynch
1995
). Further, SEF neurons fire during the preparation and
execution of saccades (Bon and Lucchetti 1992
;
Chen and Wise 1995a
,b
, 1996
, 1997
; Hanes et al.
1995
; Mann et al. 1988
; Mushiake et al.
1996
; Olson and Gettner 1995
, 1999
; Olson
and Tremblay 2000
; Russo and Bruce 1996
;
Schall 1991a
,b
; Schlag and Schlag-Rey 1985
,
1987
; Schlag-Rey et al. 1997
).
Several observations, however, suggest that the SEF is involved in
processes distinct from the simple programming and execution of eye
movements. For example, some SEF neurons are differentially active
during the learning of arbitrary associations between visual patterns
and eye-movement directions (Chen and Wise 1995a,b
, 1996
, 1997
). Further, some neurons fire at different levels when the monkey is preparing saccades to the right or left side of an object, even under conditions such that the saccades' physical direction is
constant (Olson and Gettner 1995
, 1999
; Olson and
Tremblay 2000
). Thus, the question remains open: to what degree
is neural activity in the SEF related to processes antecedent to the
final stages of oculomotor control?
One approach to answering this question is to study the SEF under
conditions such that the same eye movements are selected according to
different decision processes. Schlag-Rey et al. (1997), following this approach, recorded from the SEF in monkeys trained to
make delayed prosaccades or antisaccades. They found that neuronal activity was higher overall during antisaccade than prosaccade trials,
although the physical directions of the saccades were the same in both
cases. This finding indicates that SEF neurons are sensitive to some
nonmotor task variable; however, it leaves open several possibilities
with respect to the nature of that variable. The higher rate of
neuronal activity under antisaccade conditions might arise from the
selection of the target by means of an abstract rule (move to a
location diametrically opposed to the location of the cue).
Alternatively, it might arise from the need for suppression of eye
movements to the location marked by the cue. These two factors covary
across prosaccades and antisaccades. However, by appropriate task
design, they can be dissociated. Like humans (Klein et al.
1992
), monkeys are able to select saccade-targets not only in
response to peripheral cues presented at their location (Hikosaka and Wurtz 1983
), but also in response to
centrally presented patterns associated with them (Chen and Wise
1995a
,b
, 1996
, 1997
). Pattern-based target selection requires
use of an abstract rule but imposes no need to suppress eye movements
to the location marked by the cue because the cue is central. By
comparing between spatial trials (in which the cue is a spot flashed at
the target location) and pattern trials (in which the cue is a
centrally presented image), one should be able to determine whether and in what manner neuronal activity depends on the target-selection rule
in itself without regard to the need to suppress eye movements to the
location marked by the cue. Neuronal activity elicited by performance
of the two tasks has been compared previously in dorsolateral
prefrontal cortex (Wilson et al. 1993
) and the superior colliculus (Kustov and Robinson 1996
). However, while
SEF neurons are known to exhibit direction-selective activity in the
context of both the pattern task (Chen and Wise 1995a
,b
, 1996
,
1997
) and the spatial task (Olson and Tremblay
2000
; Olson et al. 1999
), no direct comparison
has previously been carried out in the SEF. Results obtained by direct
comparison, as described in this paper, have been reported previously
in an abstract (Olson and Gettner 1996
).
![]() |
METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
SUBJECTS. Two adult male rhesus monkeys were used (Macaca mulatta; laboratory designations Pk and Qu). Experimental procedures were approved by the Carnegie Mellon University Animal Care and Use Committee and were in compliance with the guidelines set forth in the United States Public Health Service Guide for the Care and Use of Laboratory Animals.
PREPARATORY SURGERY.
At the outset of the training period, each monkey underwent sterile
surgery under general anesthesia maintained with isoflurane inhalation.
The top of the skull was exposed, bone screws were inserted around the
perimeter of the exposed area, a continuous cap of rapidly hardening
acrylic was laid down so as to cover the skull and embed the heads of
the screws, a head-restraint bar was embedded in the cap, and scleral
search coils were implanted on the eyes, with the leads directed
subcutaneously to plugs on the acrylic cap (Remmel 1984;
Robinson 1963
). Following initial training, a
2-cm-diameter disk of acrylic and skull, centered on the midline of the
brain approximately at anterior 23 mm (Horsley-Clarke coordinates), was
removed and a cylindrical recording chamber was cemented into the hole
with its base just above the exposed dural membrane.
SINGLE-NEURON RECORDING.
At the beginning of each day's session, a varnish-coated tungsten
microelectrode with an initial impedance of several megohms at 1 KHz
(Frederick Haer, Bowdoinham, ME) was advanced vertically through the
dura into the immediately underlying cortex. The electrode could be
placed reproducibly at points forming a square grid with 1-mm spacing
(Crist et al. 1988). The action potentials of a single neuron were isolated from the multineuronal trace by means of an
on-line spike-sorting system using a template matching algorithm (Signal Processing Systems, Prospect, Australia). The spike-sorting system, on detection of an action potential, generated a pulse the time
of which was stored with 1-ms resolution.
BEHAVIORAL APPARATUS. All aspects of the behavioral experiment, including presentation of stimuli, monitoring of eye movements, monitoring of neuronal activity, and delivery of reward, were under the control of a 486-based computer running Cortex software provided by R. Desimone, Laboratory of Neuropsychology, National Institute of Mental Health. Eye position was monitored by means of a scleral search coil system (Remmel Labs, Ashland, MA, or Riverbend Instruments, Birmingham, AL) and the X and Y coordinates of eye position were stored with 10-ms resolution. Stimuli generated by an active matrix liquid crystal display projector (Sharp, XG H4OU) were rear-projected on a frontoparallel screen 25 cm from the monkey's eyes. Reward in the form of approximately 0.1 ml of water or juice was delivered through a spigot under control of a solenoid valve on successful completion of each trial.
PATTERN-SPATIAL TASK. Both monkeys were trained to perform a task requiring them to make eye movements to targets selected on the basis of a pattern cue (a foveally presented digitized image associated with the target) or a spatial cue (a spot flashed in superimposition on the target). Essential features of the task are summarized in Fig. 1. At the beginning of each trial, the monkey fixated a central 0.8° × 0.8° white spot (Fig. 1A). After 400 ms, four potential targets (0.8° × 0.8° white spots) appeared at locations 20° rightward, upward, leftward, and downward from fixation (Fig. 1B). Then, for 100 ms, either a pattern cue (Fig. 1Cp: central 1.6° × 1.6° digitized image) or a spatial cue (Fig. 1Cs: 1.6° × 1.6° white square superimposed on one of the four targets) was presented. During a subsequent delay period (which varied randomly in duration across the range 550-750 ms), the monkey was required to maintain central fixation (Fig. 1D). Then offset of the fixation spot (Fig. 1E) signaled him to make an eye movement. If the monkey made a saccade directly to the target indicated by the earlier cue (Fig. 1F) and maintained fixation on the target for a variable period of 300-450 ms, he was rewarded with a drop of water and the display was simultaneously extinguished. There were eight trial conditions differentiated by the nature of the cue. The cue might be any of four standard, highly overlearned patterns or a white spot superimposed on any of the four targets. The eight conditions were presented in random sequence until 10-16 trials had been completed successfully under each condition.
|
LOCALIZATION OF RECORDING SITES. In each monkey, recording was carried out in a pair of regions, each a few millimeters in extent, disposed approximately symmetrically across the interhemispheric midline. Following sacrifice with an overdose of sodium pentobarbital and transcardiac perfusion with 10% formalin, the brains were photographed. Marks indicating the location of the recording chamber were compared with gross anatomical landmarks, including the hemispheric midline and the arcuate and principal sulci. On the basis of the grid coordinates at which the electrode had been placed, recording sites were then projected onto the image of the cortical surface.
ANALYSIS OF DEPENDENCE OF FIRING RATE ON CONDITION IN INDIVIDUAL NEURONS. A set of identical procedures was applied to data collected from each neuron. The trial-epoch under consideration was defined as the period between two identifiable events. The mean firing rate during this period was computed for each trial completed successfully during recording from the neuron. Then an analysis of variance (ANOVA) was carried out to determine whether firing rate varied significantly across the trials as a function of cue type or direction.
ANALYSIS OF THE CORRELATION OF TRAITS ACROSS A NEURONAL POPULATION.
Neurons in a population might exhibit trait a or
b in one test and trait x or y in
another test. In such cases, to test whether the distribution of
neurons with respect to a and b was significantly correlated with the distribution with respect to x and
y, we employed a Pearson chi-square test of association
(Hayes 1988; Olson and Tremblay 2000
).
ANALYSIS OF THE TIME COURSE OF NEURONAL ACTIVITY.
The aim of this analysis was to determine the extent to which the type
of cue (pattern or spatial) affected the time course of postcue
neuronal activity. Three aspects of neuronal activity were considered:
1) the time at which firing attained its maximal rate,
2) the magnitude of the maximal rate, and 3) the
average magnitude of activity 500-600 ms after presentation of the
cue. To ensure that the results were robust, we applied two independent approaches. 1) Regression splines and parametric
analysis. We assumed that the spike times followed an
inhomogeneous Poisson process and obtained a smooth estimate of its
intensity function using maximum likelihood (ML) estimation for Poisson
regression splines, as described in the APPENDIX, with the
statistical software S-PLUS (see Venables and Ripley
1997). We checked the Poisson assumption using exponential QQ
plots of the interspike intervals on the integral transform scale.
Statistical significance was based on standard asymptotic theory, which
shows that for large samples, the characteristics of interest are
normally distributed (Agresti 1990
, Chapter 12). For
population analysis, we used a normal hierarchical model (Gilks
et al. 1996
), which assumed that the three characteristics of
interest were normally distributed across neurons; this was empirically
verified via a normal probability plot. We determined the population
means and standard deviations of the three characteristics using
Bayesian analysis using Gibbs sampling (BUGS)
(Spiegelhalter et al. 1996
). Further details are given
in the APPENDIX. 2) Gaussian filtering and
nonparametric bootstrap analysis. Without making assumptions about
the spike time distribution, we obtained a smooth estimate of firing
intensity by use of kernel density estimation (Gaussian filtering) with
bandwidth selected by methods indicated in the APPENDIX.
Statistical significance was then based on a nonparametric bootstrap
analysis (Davison and Hinkley 1997
). This analysis made
no distributional assumptions about the data or the test statistics used.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Behavior
Both monkeys performed the pattern-spatial task at a level well above chance. Across all runs of the task during which neuronal data were collected, monkey 1 scored 94.6% on pattern trials as compared with 99.8% on spatial trials, while the corresponding values for monkey 2 were 94.9 and 99.1% (numbers based on trials in which the monkey completed an eye movement to one of the four targets). The difference between pattern and spatial percent-correct scores was highly significant in both monkeys (2-tailed paired t-test, P < 0.0001). The behavioral reaction time, measured as the interval between offset of the fixation spot and initiation of the saccadic eye movement on correct trials, also varied as a function of cue condition. In monkey 1, the mean behavioral reaction time was 225.5 ms on pattern trials as compared with 231.6 ms on spatial trials, while, in monkey 2, the corresponding values were 193.9 and 215.8 ms. The tendency for the behavioral reaction time to be shorter on pattern than on spatial trials was present for all four response directions in each monkey and attained significance for two directions in monkey 1 and all four directions in monkey 2 (2-tailed paired t-test, P < 0.001). Decision time was not a factor in this effect because a long delay intervened between the instructional cue and the imperative signal. In summary, both monkeys gave moderately faster but slightly less accurate responses under pattern as compared with spatial conditions.
Recording sites
Having centered the recording chamber over the approximate
location of the SEF as determined in previous mapping studies
(Tehovnik 1995), we proceeded to select recording sites
according to the following strategy. We first placed exploratory
penetrations at widely spaced locations in each hemisphere until we
found neurons exhibiting robust task-related activity in the context of
the pattern-spatial task. We then proceeded to record from neurons at
these and adjacent sites, moving out in all directions from the
initially identified loci until we reached the limits of the domain
within which neuronal activity was robustly task-related. Using this
approach, we collected data from 439 SEF neurons during performance of
the pattern-spatial task (327 neurons in monkey 1 and 112 neurons in monkey 2). The recording sites are projected onto
dorsal views of the frontal lobes in Fig.
2, A and B, where they are shown in relation to a square marking the approximate limits
of the SEF (Fig. 2C) as determined in studies summarized by
Tehovnik (1995)
. The issue of the relation of recording
sites in this study to the location of the SEF as determined by
electrical stimulation in classic studies will be taken up at greater
length in the DISCUSSION.
|
Conservation of preferred direction across cue conditions
The selectivity of each neuron for response direction was assessed by carrying out independent ANOVAs on data from pattern and spatial trials, with firing rate as the dependent variable and with response direction (right, up, left, or down) as the single factor. The results, summarized in Table 1, indicate that under both pattern and spatial conditions and during both the delay epoch (cue-onset to fix-spot offset) and movement epoch (fix-spot offset to 100 ms after target attainment) around 40% of the population exhibited significant (P < 0.05) direction selectivity. During the delay epoch, the proportion of neurons exhibiting direction selectivity under pattern conditions was slightly lower than the number exhibiting it under spatial conditions (38 vs. 45%), an effect which just attained significance (P = 0.047). This difference may arise from the fact that direction selectivity developed later under pattern conditions (see Time course of population-averaged cue-dependent activity).
|
It was evident on casual inspection of histograms representing neuronal
activity (Fig. 3) that the response
directions eliciting strongest neuronal activity tended to be the same
under pattern and spatial conditions. To assess this tendency
quantitatively, we analyzed data from neurons exhibiting significant
direction selectivity under both cueing conditions (125 during the
delay period and 129 during the movement period). For each neuron, and for each cueing condition independently, we estimated the best direction by summing vectors pointing toward the four targets after
weighting them by the four associated firing rates. Then we computed
the absolute angular difference between the best directions estimated
on the basis of pattern and spatial data. The results, summarized in
Fig. 4, indicate that the estimated best
directions were within 20° of each other in a majority of cases. It
might be objected that by testing with only four directions, we
obtained poor estimates of the preferred directions of those SEF
neurons possessing tuning curves narrower than 90° (Russo and
Bruce 1996). It is true that estimates of preferred direction
would have been inaccurate in these cases. However, the resulting
inaccuracy could not have given rise spuriously to the observed
tendency for preferred directions to match.
|
|
Dependence of firing rate on cue condition
In many neurons, the strength of activity during the period between presentation of the cue and the signal to respond appeared to depend on the type of the cue. For example, the neuron of Fig. 5 fired more strongly when any given direction had been signaled by a central pattern than when it had been signaled by a spatial cue, while the neuron of Fig. 6 showed the opposite pattern. To ascertain whether the type of cue systematically affected the strength of neuronal activity, we carried out ANOVAs on data from each neuron, with firing rate during the delay period as the dependent variable and with cue-type (spatial or pattern) and response-direction (right, up, left, or down) as factors. Independent analyses were carried out on data from the delay epoch (cue onset to fix-spot offset) and the movement epoch (fix-spot offset to 100 ms after target attainment).
|
|
During the delay period, there was a significant (P < 0.05) dependence on cue-type in 40% (176/439) of all tested neurons. Among 176 neurons showing significant dependence on cue-type during this period, 131 fired more strongly during pattern trials and 45 during spatial trials (Fig. 7). Each of
these counts significantly (chi-squared test, P < 0.0001) exceeded the level of 2.5% expected by chance with the
significance criterion (P < 0.05) employed in the
ANOVA. We conclude therefore that the SEF contained at least two
classes of neurons with cue-dependent activity: those more active on
pattern and those more active on spatial trials. However, the number of
cue-dependent neurons favoring pattern conditions (131/176) exceeded
the number favoring spatial conditions (45/176) by a large margin, with
the ratio not significantly different between monkeys (chi-squared
test, P = 0.11). The excess of neurons firing at a
higher rate under the pattern condition was highly significant in each
monkey and attained even higher significance in the combined data
(chi-squared test, P 0.0001).
|
During the movement period, there was a significant (P < 0.05) dependence on cue-type in 27% (119/439) of all tested
neurons. Among 119 neurons showing significant dependence on cue-type
during this period, 105 fired more strongly during pattern trials and 14 during spatial trials (Fig. 7). The excess of neurons firing at a
higher rate under the pattern condition was highly significant in each
monkey, attained even higher significance in the combined data
(chi-squared test, P 0.0001) and was not different
between monkeys (chi-squared test, P = 0.14). That the
number of neurons exhibiting a significant main effect of cue type was
lower during the movement than during the delay period might reflect a
genuine decline in cue-dependent activity or, alternatively, might
reflect the fact that noise was higher due to the shorter sampling
interval (approximately 300 vs. approximately 750 ms).
To investigate this issue, we computed, for each neuron during each
task epoch, a nonstatistical index of cue-dependent activity: i = (p s)/(p + s), where p and s were the mean firing
rates on pattern and spatial trials, respectively. The distributions for both task epochs (Fig. 8,
A and B) had means significantly different from
zero (One Group t-test, P < 0.0001).
Further, the mean was actually greater during the movement period
(0.060) than during the delay period (0.043) and this difference was
significant (paired 2-tailed t-test, P = 0.02). The average rate of firing on pattern trials, expressed as
a percentage of the average rate of firing on spatial trials
[100 * (1 + i)/(1
i)], was 113% during the movement period, as contrasted to 109% during the delay period. Despite this moderate difference between task epochs, neurons
exhibiting cue-dependent activity during either epoch in general did so
during the other epoch as well. This is indicated by the fact that
indices based on activity during the two epochs (Fig. 8C)
were significantly and positively correlated (P < 0.0001; r-squared = 0.246).
|
To determine whether dependence on cue-type was related to dependence on response-direction, we analyzed data from the delay period in both monkeys. In each monkey, neurons exhibiting direction selectivity also tended to display dependence on cue-type (Pearson chi-square test of association, P = 0.0016 and P = 0.0011 in monkey 1 and 2, respectively). Among neurons exhibiting a main effect of direction, the proportion exhibiting a significant main effect of cue-type was 51% (116/227) during the delay period and 52% (99/189) during the movement period (as contrasted to 40 and 27% among all neurons). In contrast to the presence of a cue-type effect, the sign of the effect (pattern greater than spatial or vice versa) was not correlated with the presence of direction selectivity.
Cortical location of neurons with cue-dependent activity
To determine whether neurons exhibiting a significant main effect of cue type were distributed systematically with respect to the cortical surface, we constructed maps showing the locations of neurons that exhibited specific forms of task-related activity during the delay period. These are shown in Fig. 9, with data from monkey 1 in the left column and data from monkey 2 in the right column. It is evident from these maps that neurons whose activity was significantly elevated (Fig. 9, C and D) or suppressed (Fig. 9, E and F) on pattern compared with spatial trials were not systematically segregated from each other. Nor were these neurons, as a group, segregated systematically from neurons exhibiting selectivity for saccade direction (Fig. 9, A and B).
|
Relation of cue-dependent activity to the frequency of behavioral errors
The tendency for neurons to fire more strongly on pattern
trials, although documented through an analysis of data from correct trials, might nevertheless have arisen from the fact that the monkeys
made more errors under the pattern condition. Pattern cues, due to
their more frequent association with errors, might have elicited phasic
arousal and this, in turn, might have produced an enhancement of
neuronal activity. To test this possibility, we took advantage of the
fact that the percent-correct score under pattern conditions varied
from run to run of the task (the standard deviation was 7.1% in
monkey 1 and 5.2% in monkey 2). This permitted us to ask whether the tendency for neuronal activity to be elevated on
pattern trials was correlated, across runs of the task, with the
tendency for errors to occur more frequently on pattern trials. For
each session during which neuronal data were collected, we computed
four values: the mean firing rate on successful pattern trials (PR),
the mean firing rate on successful spatial trials (SR), the frequency
of errors on pattern trials (PE), and the frequency of errors on
spatial trials (SE). We then assessed the correlation across sessions
between an index of higher pattern-trial firing rate, (PR SR)/(PR + SR), and an index of higher pattern-trial error rate,
(PE
SE)/(PE + SE). The two indices were not significantly correlated and the trend was negative. Thus, if task difficulty did
underlie the enhancement of activity on endogenously cued trials, the
critical variable must have been some aspect or consequence of task
difficulty other than the higher rate of errors in itself.
Relation of cue-dependent activity to behavioral reaction time
Both monkeys, as described in an earlier section, showed a
significant tendency to respond more swiftly following offset of the
fixation light under pattern than under spatial conditions. Perhaps
presentation of a pattern cue elicited some state which gave rise both
to stronger firing on pattern trials and to faster behavioral
responses. If so, then insofar as the tendency of pattern cues to
elicit this state varied from run to run of the task, we would expect
to observe, across runs, covariation of 1) the tendency for
firing to be stronger on pattern trials and 2) the tendency
for behavioral reactions to be swifter on pattern trials. The
difference between pattern and spatial reaction times indeed varied
from run to run of the task (the standard deviation of the difference
between the reaction times was 27 and 14 ms in monkey 1 and
2, respectively). Accordingly, we asked whether the tendency
for neuronal activity to be elevated on pattern trials was correlated,
across runs of the task, with the tendency for behavioral reaction
times to be shorter on pattern trials. For each session during which
neuronal data were collected, we computed four values: the mean firing
rate on successful pattern trials (PR), the mean firing rate on
successful spatial trials (SR), the reaction time on pattern trials
(PT), and the reaction time on spatial trials (ST). We then assessed
the correlation across sessions between an index of higher
pattern-trial firing rate, (PR SR)/(PR + SR), and an index of
faster pattern-trial reaction times, (ST
PT)/(ST + PT). In
monkey 1, the two indices were positively (slope = 0.47 [sp/s]/ms) and significantly (P = 0.0015) correlated
(r = 0.175). In monkey 2, the trend was
opposite (slope =
0.43 [sp/s]/ms) but not significant
(P = 0.13, r = 0.145). A simple
description of the effect observed in monkey 1 is that, during runs in which firing on pattern trials was especially strong, behavioral reaction times on pattern trials were especially short. This
result is intriguing but failure to observe it in the second monkey
leaves its significance in doubt.
Strength of directional signals in relation to cue-dependent activity
Having analyzed whether the mean firing rate depended on cue-type (as indicated by a main effect of cue-type), we next asked whether the strength of the directional signal depended on cue-type (as indicated by an interaction between cue-type and direction). The ANOVA described in the preceding section revealed interaction effects in 91/439 neurons during the delay period and 57/439 neurons during the movement period. In each of these cases, we measured the strength of direction-selectivity (the variance in mean firing rate across the 4 directions) under both pattern and spatial conditions during the corresponding epoch. Then we classified neurons with a significant interaction effect into two categories: those in which direction-selectivity was stronger (as indicated by higher variance) under the pattern condition (Pds > Sds) and those in which direction-selectivity was stronger (as indicated by higher variance) under spatial condition (Sds > Pds). The counts in these two categories, as given in Table 2, were not significantly different. We conclude that neurons carrying stronger directional signals under the pattern condition were no more common than those exhibiting the opposite pattern.
|
It might still be the case that the impact of cue-type on the strength of the directional signal was correlated with its impact on mean firing rate. To determine whether this was so, we considered all cases in which, during a given epoch, a neuron exhibited both a main effect of cue-type and an interaction of cue-type with direction (Table 3). We then asked whether the property of firing more strongly on pattern (or spatial) trials was correlated with the property of carrying stronger directional signals on pattern (or spatial) trials. We found that this tendency was present at a highly significant level (Pearson chi-square test of association, P = 0.0008 and P = 0.0001 during the delay and movement periods, respectively). We conclude that neurons firing at a higher level under a given condition (pattern or spatial) tend to carry stronger directional signals under that condition.
|
Time course of population-averaged cue-dependent activity
Given that SEF neurons tended to be more active following pattern
than spatial cues, by a measure based on mean firing rate during the
delay period as a whole, we next asked at what time during the delay
period the enhancement was present. To do so, we employed a population
measure, the mean firing rate as a function of time during the trial,
computed independently for trials in which the required eye movement
was in the neuron's preferred direction and those in which it was in
the opposite direction. This analysis was performed on data from all
neurons that exhibited statistically significant main effect of
direction during the delay period (ANOVA, P < 0.05).
There were 189 such neurons in monkey 1 and 38 in
monkey 2. The results are shown in the form of population
histograms in Fig. 10, A and
B. Several general features are apparent in these
histograms. 1) The mean firing rate increased steadily from
the starting point of the analysis, 125 ms before cue onset until
around 75 ms after cue onset (activity thus anticipating the appearance
of a task-relevant cue is common in premotor areas: Mauritz and
Wise 1986; Vaadia et al. 1988
). 2)
Following the cue, activity became stronger during trials in which the
required response was in the neurons' preferred direction (thick
curves, "pref") as compared with trials when it was in the
antipreferred direction (thin curves, "anti"). 3) This
difference emerged earlier on spatial trials (gray curves: directional
signal fully developed at around 150 ms following cue presentation)
than on pattern trials (black curves: directional signal fully
developed at 300-500 ms following cue presentation). 4) At
a variable time following cue-presentation, on the order of several
hundred milliseconds, activity became stronger on pattern than on
spatial trials, both when the required movement was in the preferred
direction (thick black curve, "pat," higher than thick gray curve,
"spa") and when it was in the antipreferred direction (thin black
curve, "pat," higher than thin gray curve, "spa").
5) This elevation of activity on pattern compared with spatial trials persisted to the end of the delay period, which occurred, at the earliest, 650 ms following onset of the cue.
|
We next assessed how the time course of neuronal activity differed between neurons that fired significantly more strongly on pattern trials and those that fired significantly more strongly on spatial trials. This analysis was performed on data from all neurons from monkey 1 that exhibited statistically significant main effects of both direction and cue-type during the delay period (ANOVA, P < 0.05). In this sample were 67 neurons that fired significantly more strongly on pattern trials and 32 that did so on spatial trials. No meaningful analysis was possible in monkey 2 because only one direction-selective neuron exhibited enhanced activity on spatial trials. The results for monkey 1 are shown in the form of population histograms in Fig. 11, A and B. Comparison of data from neurons firing more strongly on pattern trials (Fig. 11A) and those firing more strongly on spatial trials (Fig. 11B) suggests the following general conclusions. 1) In both pattern-enhanced (Fig. 11A) and spatial-enhanced (Fig. 11B) neurons, just as in the entire population (Fig. 10A), the directional signal (thick curve minus thin curve) attained a maximum earlier on spatial (gray) than on pattern (black) trials. 2) In pattern-enhanced neurons (Fig. 11A), the directional signal on pattern trials (thick black curve minus thin black curve) was stronger than the directional signal on spatial trials (thick gray curve minus thin gray curve). In spatial-enhanced neurons (Fig. 11B), the reverse was true. Thus, in each population there was a linkage between conditions that induced higher firing rates and those that induced a stronger dependence on direction. This reinforces the finding, described above, that, in neurons exhibiting both a main effect of cue-type and an interaction between cue-type and direction, the cue-type eliciting stronger activity tended also to elicit deeper modulation by direction. 3) Pattern enhancement (Fig. 11A) was present both on trials when the response was in the neuron's preferred direction (thick black curve minus thick gray curve) and when the response was in the opposite direction (thin black curve minus thin gray curve). In contrast, spatial enhancement (Fig. 11B) was strong on preferred-direction trials (thick gray curve minus thick black curve) but nearly absent on opposite-direction trials (thin gray curve minus thin black curve). 4) On preferred-direction trials (thick black and gray curves), the mean level of activity tapered off during the second half of the delay period in pattern-enhanced neurons (Fig. 11A) but remained constant or rose in spatial-enhanced neurons (Fig. 11B). These observations suggest a general contrast between pattern-enhancement (which occurs regardless of response direction) and spatial enhancement (which arises because of stronger activity on preferred-direction trials). They also suggest a general contrast between cells exhibiting pattern enhancement (whose rate of activity declines during the delay period) and those exhibiting spatial enhancement (whose rate of activity remains high to the end of the delay period).
|
Time course of cue-dependent activity: single-neuron analysis
Population histograms are potentially misleading in that they represent the mean rate of firing of neurons which, considered individually, might exhibit quite different patterns of activity. Accordingly, we tested the general conclusions of the population-averaged analysis by analyzing the time course of activity following pattern and spatial cues in individual neurons. We restricted this analysis to a subset of neurons in which we judged that the signal-to-noise ratio was sufficiently high to permit a cell-by-cell analysis. In particular, we considered neurons with the following traits: 1) from monkey 1; 2) no fewer than 10 spikes per trial on average; 3) a significant main effect of direction; 4) preferred direction (right, up, left, or down), as judged on the basis of firing rate 300-600 ms after cue onset, identical under pattern and spatial conditions; 5) the fitted curves obtained by regression spline analysis (see APPENDIX) possessed maxima between onset of the cue and the signal to move. These criteria were met by 84 neurons. We analyzed data from each neuron by means of a regression spline approach. Firing rates were fitted with a piecewise cubic polynomial function (Fig. 12). Then comparisons between pattern and spatial conditions were carried out based on the assumption that the binwise spike counts conformed to Poisson distributions (see METHODS and APPENDIX for details).
|
TIME TO PEAK. We asked, for each neuron, whether its firing rate achieved a maximum significantly (P < 0.05) later under the pattern than under the spatial condition or vice versa. Regression spline analysis revealed a "pattern later" effect in 41/84 neurons (49%) and a "spatial later" effect in 3/84 neurons (3.6%). The population mean difference in maximal firing rate (the mean delay in achieving maximal rate under the pattern condition as compared with the spatial condition as estimated from the hierarchical model) was 137 ms (SE 18 ms), which was significantly different from zero. The distribution of values was fit well by a normal distribution; thus neurons form a continuum with respect to the difference in time-to-peak between pattern and spatial conditions. In conclusion, the cell-by-cell analyses supported the population-averaged analysis in indicating that firing on preferred-direction trials tended to peak later under pattern than under spatial conditions.
PEAK RATE.
We asked, for each neuron, whether the maximal firing rate attained
between cue onset and the signal to respond was significantly (P < 0.05) greater under the pattern than under the
spatial condition or vice versa. Regression spline analysis revealed a
"pattern greater" effect in 21/84 neurons (25%) and a "spatial
greater" effect in 24/84 neurons (30%). The mean difference between
the maximal rates on pattern and spatial trials as estimated from the
hierarchical model was 1.15 spikes/s (SE 2.06), which was not
significantly different from zero; the distribution of differences was
fit well by a normal distribution. In conclusion, these analyses revealed only an insignificant trend toward greater maximal activity on
spatial than on pattern trials. This trend is consonant with population
data from monkey 1 (Fig. 10A, thick gray curve
versus thick black curve) but not from monkey 2 (Fig.
10B, thick gray curve versus thick black curve). We conclude
that the maximal firing rate did not exhibit a strong or consistent
dependence on cue condition.
LATE RATE. We asked, for each neuron, whether the mean firing rate attained late in the delay period (500-600 ms following cue onset) was significantly (P < 0.05) greater under the pattern than under the spatial condition or vice versa. Regression spline analysis revealed a pattern greater effect in 44/84 neurons (52%) and a spatial greater effect in 9/84 neurons (11%). The mean difference between the late rates on pattern and spatial trials as estimated from the hierarchical model was 9.67 spikes/s (SE 1.59), which was significantly different from zero. The distribution of differences was fit well by a normal distribution. In conclusion, this analysis revealed a significant trend toward greater late-delay-period activity on pattern than on spatial trials, in agreement with population-averaged data from both monkeys.
To establish that these results were not an artifact of statistical methodology, we repeated the analysis using a bootstrap approach (see METHODS and APPENDIX for details). In this approach, firing rates were fitted with a Gaussian smoothing function and the validity of the subsequent comparisons did not rest on assumptions concerning the distribution of values. The results were in close agreement with those of the regression spline analysis. ![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Overview
The central finding of this study is that neuronal activity in the SEF was influenced by the nature of the cue that signaled the direction of an eye movement to be performed later in the trial. Differences between spatial trials (in which the cue marked the target location) and pattern trials (in which the cue was a central pattern associated with the target location) were evident during both early and late task epochs. 1) Early activity: activity reflecting the direction of the impending response developed more rapidly on spatial than on pattern trials. 2) Late activity: the mean level of activity throughout the delay period was higher on pattern than on spatial trials.
Early activity: differential timing
On spatial trials in which the stimulus was in the neuron's
preferred direction (Fig. 10, thick gray curves), there was an early
increase in activity at the latency of previously described visual
responses (Schall 1991a,b
; Schlag and Schlag-Rey
1987
). On pattern trials, there was no comparable early
increase. This difference could be accounted for in at least three
different ways.
VISUAL RESPONSIVENESS.
The occurrence of early activity on spatial but not pattern trials
could be accounted for by assuming that SEF neurons possess eccentric
visual receptive fields spatially congruent with the targets of eye
movements in their preferred directions, as reported previously
(Schall 1991a; Schlag and Schlag-Rey
1987
), and that these receptive fields do not encroach on the
fovea, contrary to one previous report (Schall 1991a
),
or are weaker at the fovea. However, even visual stimuli presented
during passive fixation probably trigger automatic responses: shifts of
spatial attention and the incipient programming of eye movements.
Additional research will be required to distinguish between passive
visual responses, on one hand, and, on the other hand, neuronal
activity correlated with attentional and motoric processes elicited
automatically by visual stimulation.
SPATIAL ATTENTION.
In studies of spatial attention, a fundamental distinction is made
between exogenous and endogenous cueing (Egeth and Yantis 1997; Posner 1980
). Exogenous cueing occurs when
a peripheral stimulus of sudden onset draws attention to its location,
whereas endogenous cueing results when a symbolic cue at one location (e.g., an arrow at fixation) directs attention to another location (e.g., the peripheral site to which the arrow points). Either type of
cue can elicit a shift of attention, as reflected by improved detection
and discrimination for stimuli presented at the cued location and
reduced reaction time. However, shifts elicited by exogenous and
endogenous cues occur with different time courses. Monkey and human
studies have indicated that the reaction-time and accuracy benefits
conferred by an exogenous cue peak at around 100 ms following its
presentation, whereas the benefits conferred by an endogenous cue peak
at around 300 ms (Bowman et al. 1993
; Cheal and
Lyon 1991
; Müller and Rabbit
1989
). Lateralized scalp potentials correlated with the
direction of attention also develop more rapidly in response to
exogenous cues (Yamaguchi et al. 1994
). The low speed
with which attention is shifted in response to an endogenous cue might
reflect the time required for recognition or for implementing the
arbitrary association between the stimulus and the response. On spatial
trials, the monkey's attention presumably was drawn to the target
location by an exogenous process (an automatic tendency to attend to
the location of the cue) as well as by an endogenous process (a
deliberate effort to attend to the cued location), whereas, on pattern
trials, an endogenous process alone was active. It is reasonable
therefore to speculate that the earlier appearance of directional
activity on spatial trials reflected the greater speed with which
attention was allocated under exogenous control.
OCULOMOTOR PROGRAMMING.
Early direction-selective activity in the SEF, as observed on spatial
trials, might have been correlated with the rapid programming of
oculomotor responses. Preparedness to make an eye movement to a cued
location, as measured in monkeys with a technique based on electrical
stimulation, peaks at around 100 ms following an exogenous cue but
rises steadily over several hundred milliseconds following an
endogenous one (Kustov and Robinson 1996). To
distinguish definitively between activity related to saccade
programming and activity related to the spatial allocation of attention
is a formidable challenge because the two processes are very tightly
yoked and may rely on substantially overlapping neural substrates
(Corbetta 1998
; Kustov and Robinson 1996
;
Rizzolatti et al. 1994
; Sheliga et al.
1994
). This challenge is faced by any study that attempts to
establish a relation between neural activity in the SEF and spatial
attention (Bon and Lucchetti 1997
).
Late activity: differential strength
The finding that population activity late in the delay period was
higher on pattern than on spatial trials fits with a growing body of
literature indicating that activation of some frontal areas is elevated
during the performance of tasks which, because they are not automatic,
require a high degree of endogenous control. The observation that
population activity in the SEF is greater on antisaccade than on
prosaccade trials (Schlag-Rey et al. 1997) can be
accounted for in these terms. Further, human imaging studies have
demonstrated enhanced frontal-lobe functional activation under several
conditions requiring greater engagement of voluntary resources,
including response-selection based on novel versus familiar
associations (Paus et al. 1993
; Raichle et al.
1994
), attention to multiple versus single visual-feature
dimensions (Corbetta et al. 1991
; Rees et al.
1997
), comprehension of syntactically complex versus simple
sentences (Just et al. 1996
), performance of tasks
placing a higher versus lower load on short-term memory (Cohen
et al. 1997
), execution of the Stroop task with incongruent versus congruent conditions (Carter et al. 1995
;
Pardo et al. 1990
), and performance of the antisaccade
versus prosaccade task (Sweeney et al. 1996
).
On one hand, these findings might be accounted for by supposing that
frontal areas including, in particular, medial premotor areas such as
the SEF, contribute preferentially to self-generated as opposed to
stimulus-driven behavior (Passingham 1993; Tanji and Shima 1996
). Under neither the pattern nor the spatial
conditions is behavior strictly speaking self-generated, for, in both,
a sensory stimulus provides the directional cue. However, the rule linking the stimulus to the response is entirely arbitrary and unnatural in pattern trials. In that sense, the rule, if not the response, is self-generated. Wise et al. (1996)
captured
this distinction in proposing that premotor cortex is selectively
responsible for behaviors requiring "nonstandard" sensorimotor mappings.
On the other hand, explanations based on arousal cannot be ruled out.
For example, pattern-based target-selection, being slower and more
difficult than the process set in motion by a spatial cue, might give
rise to phasic arousal manifest in a higher rate of neuronal activity
(for a similar argument as applied to functional imaging studies of
anterior cingulate cortex, see Paus et al. 1998). An
arousal-based mechanism could account for the fact that enhanced firing
persists past the point at which target selection is complete, as
marked by the advent of robust direction-selective activity. It is also
consonant with the fact that population activity on pattern trials
differed from population activity on spatial trials primarily with
respect to a nonspecific signal (mean rate) rather than with respect to
a motorically relevant signal (the difference in rate between neurons
representing the preferred and antipreferred directions).
Recording location
Although we did not map out the SEF by observing electrically
stimulated eye movements, we feel confident that all or nearly all of
the neurons in this study were in the SEF. Our confidence is based on
two factors: the functional properties of the neurons and their
location relative to the SEF as mapped out in other studies.
1) Functional properties: neurons exhibiting
significant effects of cue-type (Fig. 9, C-F) were
intermingled with neurons exhibiting selectivity for saccade direction
(Fig. 9, A and B), the latter a functional
signature of the SEF. Further, on analysis of trends across the
neuronal population, we found that selectivity for saccade direction
and significant effects of cue-type showed a significant tendency to
occur together. 2) Location relative to standard
maps: to compare recording sites in this study to the location of
the SEF as characterized in classic studies based on electrical
stimulation, we constructed the map shown in Fig. 2C, which
is based on Table 1 of a review by Tehovnik (1995). Tehovnik summarized the results of 10 studies in which electrical stimulation was used to map out the SEF, indicating, for each study,
the area's mediolateral extent (ML, defined relative to the
interhemispheric midline) and anterior-posterior extent (AP, defined
relative to the genu of the arcuate sulcus). These results are
translated, in Fig. 2C, into a graph in which the area of each dot corresponds to the fraction of the 10 studies in which electrical stimulation at the dot's location elicited eye movements. Loci at which electrical stimulation elicited eye movements extend from
3 mm posterior to 9 mm anterior to the level of the genu of the arcuate
sulcus and from the midline to 6 mm lateral. In recent mapping studies,
this general pattern has been confirmed. For example, Fig.
8A of Chen and Wise (1995b)
show sites
positive for elicitation of eye movements as extending 2-6 mm anterior to the genu, while Fig. 1 of Fujii et al. (1995)
show
such sites at levels 1-8 mm anterior to the genu. The anterior limit
of the SEF as demarcated in these studies coincides approximately with the posterior limit (10 mm anterior to the genu) of an area from which
ear movements can be elicited (Fig. 2A of Bon and
Lucchetti 1994
). To facilitate comparison of recording sites in
this study to the limits of the SEF as defined in other studies, the
zone marked by dots in Fig. 2C is represented by a square in
Fig. 2, A and B. All recording sites in both
monkey 1 and monkey 2 were within 6 mm of the
hemispheric midline and thus were within the mediolateral limits of the
SEF as defined in preceding studies. With respect to anterior-posterior
location, the following conclusions can be drawn. In monkey
1, all recording sites, with one exception, were between 0 and 8 mm anterior to the genu of the arcuate sulcus (Fig. 2A) and
thus were clearly within the confines of the SEF as demarcated in
mapping studies. The exceptional site was approximately 3.5 mm
posterior to the genu of the arcuate sulcus and thus was at the border
of this zone. In monkey 2, recording sites extended from
approximately 0 mm to approximately 13 mm anterior to the genu of the
arcuate sulcus (Fig. 2B); however, recording sites at which
neurons exhibited direction selectivity or fired differentially as a
function of cue type extended anteriorly no farther than 9 mm (Fig. 9,
B, D, and F). Neurons rostral to this
level may well have been outside the confines of the SEF. Given the
impossibility of drawing a precise line between the SEF and adjacent
areas, we chose to include them in our sample at the cost of slightly reducing the percentage of neurons exhibiting task-related activity in
monkey 2. Their inclusion had no impact on our major
conclusions, which concerned the relative frequency of different forms
of task-related activity.
Comparison to dorsal premotor cortex
Kurata and Wise (1988) recorded from the dorsal
premotor cortex of monkeys during the performance of tasks similar to
the spatial and pattern ones used here with the exception that the nonspatial cues were differentiated by color and the responses were
reaching movements. Neurons exhibiting a significant effect of cue-type
were significantly less frequent in their sample than in ours (19 vs.
30%, P < 0.0001, chi-squared test). However, among neurons exhibiting an effect of cue-type, there was in premotor cortex,
as in the SEF, an approximately 3:1 preponderance of cells firing more
strongly under the nonspatial condition. Making allowance for
uncertainty arising from methodological differences, we conclude that
the effects of cue-type in the SEF and dorsal premotor cortex are
qualitatively similar although quantitatively different.
Comparison to dorsolateral prefrontal cortex
Neuronal activity in the dorsolateral prefrontal cortex
surrounding the principal sulcus has been monitored under conditions closely approximating the ones used in this study (Asaad et al. 1998; Wilson et al. 1993
). However, there has
been no report of phenomena equivalent to the ones described here (a
slower rise of directional signals or a higher net level of sustained
activity on pattern trials). Some prefrontal neurons fire more strongly on pattern trials simply because they are selective for particular patterns (Hasegawa et al. 1998
; Hoshi et al.
1998
; Miller et al. 1996
; Ó
Scalaidhe et al. 1997
; Rao et al. 1997
;
White and Wise 1999
; Wilson et al. 1993
).
This cannot be the mechanism of pattern-trial enhancement in the SEF
because SEF neurons are not selective for patterns. At least three
observations support this point. First, in monkeys performing an
object-centered eye movement task, SEF neurons are unaffected by marked
changes in the visual properties of cues (Olson and Gettner
1999
). Second, in monkeys performing a chromatic
delayed-match-to-sample task, SEF neurons are insensitive to the colors
of the samples and probes (Olson and Tremblay 1997
). Third, in the present study, the pattern selectivity of SEF neurons was
predictable from the directions associated with the patterns and so was
a simple extension of their spatial selectivity. Even when factors such
as pattern selectivity are ruled out as a cause, some prefrontal
neurons fire at different levels when the monkey is following a pattern
or spatial rule (White and Wise 1999
; Wilson et
al. 1993
). Further, neurons more active under pattern or
spatial conditions may be regionally segregated (White and Wise
1999
; Wilson et al. 1993
). However, within
prefrontal cortex as a whole, neither population is obviously more numerous.
Recording in the context of pattern and spatial working memory tasks
has provided some evidence that prefrontal neurons as a population are
more active during the processing of pattern than of spatial
information. Hoshi et al. (1998), studying
movement-period activity in prefrontal neurons of monkeys performing a
delayed match to sample task, found that the activity of some neurons varied as a function of whether the monkey had selected the target on
the basis of its form or location. Further, they found that neurons
firing more strongly on pattern-match trials outnumbered those firing
more strongly on spatial-match trials by a factor of around two to one.
Although the tasks of Hoshi et al. are quite different from ours, this
phenomenon could be interpreted as analogous to pattern-trial
enhancement in the SEF.
![]() |
APPENDIX |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Regression splines
Let tij denote the jth
spike time on the ith trial. To fit regression splines, we
aggregate the spikes {tij} into bins
Bk of width centered at
t*k. The Poisson regression
likelihood function is
![]() |
![]() |
To compare formally the temporal evolution of the firing rates in the
two tasks, we define features of interest. We denote by
max the maximal firing rate, by
max the time at which this maximum occurs, and
by
end the mean end firing rate, that is, the
firing rate averaged over the interval [500, 600] ms after presentation of the cue. Superscripts s or p
distinguish their values for spatial and pattern tasks. For each
neuron, we consider the differences
maxp
maxs,
maxp
maxs, and
endp
ends. We test whether these differences are greater
than (or less than) zero by evaluating their ML estimates and SE using
standard asymptotic theory (the delta method, e.g., Agresti
1990
, Chapter 12), comparing estimate/SE to a standard normal
distribution to obtain a P value.
Kernel smoothing and the bootstrap
For a given neuron and condition, we use a Gaussian kernel
estimator of the intensity function (t), having the form
![]() |
![]() |
Population analysis
We consider each neuron to be drawn at random from a population
of neurons. The differences maxp
maxs,
maxp
maxs, and
endp
ends are assumed to follow a three-dimensional
multivariate normal distribution across the ensemble of neurons:
letting
= (
maxp
maxs,
maxp
maxs,
endp
ends), for the ith neuron we place a
subscript on
and assume
![]() |
(A1) |
![]() |
(A2) |
Q-Q plots to check the Poisson process assumption
Suppose that on the ith trial we have spikes at times
ti1,
ti2, ... ,
tini in time interval (0, T] with i = 1, ... , N.
The likelihood function in terms of the intensity (t) =
(ti;
) is
![]() |
![]() |
![]() |
ACKNOWLEDGMENTS |
---|
We thank K. Rearick for excellent technical assistance.
Support was provided by National Institutes of Health Grant RO1 EY-11831 (C. R. Olson), the Center for the Neuroscience of Mental Disorders, NIH Grant MH-45156 (C. R. Olson), the McDonnell-Pew Program in Cognitive Neuroscience (S. N. Gettner), NIH Grant RO1 CA-54852 (V. Ventura, R. Carta, and R. E. Kass), and National Science Foundation Grant DMS 9631248 (V. Ventura). Technical support was provided by NIH Core Grant EY-08098.
![]() |
FOOTNOTES |
---|
Address for reprint requests: C. R. Olson, Center for the Neural Basis of Cognition, Mellon Institute, Rm. 115, 4400 Fifth Ave., Pittsburgh, PA 15213-2683 (E-mail: colson{at}cnbc.cmu.edu).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 28 March 2000; accepted in final form 18 May 2000.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|