Department of Neurology, Department of Brain and Cognitive Sciences, Department of Neurobiology and Anatomy, Department of Ophthalmology, and The Center for Visual Science, The University of Rochester Medical Center, Rochester, New York 14642
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ABSTRACT |
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Page, William K. and Charles J. Duffy. MST neuronal responses to heading direction during pursuit eye movements. As you move through the environment, you see a radial pattern of visual motion with a focus of expansion (FOE) that indicates your heading direction. When self-movement is combined with smooth pursuit eye movements, the turning of the eye distorts the retinal image of the FOE but somehow you still can perceive heading. We studied neurons in the medial superior temporal area (MST) of monkey visual cortex, recording responses to FOE stimuli presented during fixation and smooth pursuit eye movements. Almost all neurons showed significant changes in their FOE selective responses during pursuit eye movements. However, the vector average of all the neuronal responses indicated the direction of the FOE during both fixation and pursuit. Furthermore, the amplitude of the net vector increased with increasing FOE eccentricity. We conclude that neuronal population encoding in MST might contribute to pursuit-tolerant heading perception.
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INTRODUCTION |
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You see a radial pattern of optic flow during
self-movement, and its focus of expansion (FOE) indicates your heading
direction (Gibson 1950). If you make a pursuit eye
movement during self-movement, the rotation of your eye is added to the
retinal image of the optic flow and the FOE no longer indicates heading
(Longuet-Higgins and Prazdny 1980
) (Fig.
1). People have difficulty detecting
simulated heading when rotation is added to optic flow displays, but
they succeed when the rotation comes from their own pursuit eye
movements across such displays (Royden et al. 1992
;
Warren and Hannon 1988
). This suggests that optic flow
and eye movement signals are combined to support heading
discrimination.
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Neurons in the dorsal segment of the medial superior temporal area
(MSTd) of monkey extrastriate visual cortex respond to radial patterns
of visual motion that are common in optic flow (Duffy and Wurtz
1991b; Graziano et al. 1994
; Orban et al.
1992
; Saito et al. 1986
; Tanaka et al.
1989
). These neurons show selective responses to the simulated
heading direction in optic flow (Duffy and Wurtz 1995
)
that may reflect interactions between their excitatory and inhibitory
planar motion responses (Duffy and Wurtz 1997b
), with
augmentation by their speed gradient sensitivity (Duffy and Wurtz 1997a
). MST is thought to be involved in combining optic flow and oculomotor information because MST neurons are sensitive to
eye position (Bremmer 1997
; Squatrito 1996
,
1997
) with some MST neurons responding to the direction
of pursuit eye movements (Dursteler et al. 1987
;
Kawano et al. 1994
; Komatsu and Wurtz 1988
) and others responding to both heading direction and
pursuit (Duffy and Wurtz 1994
). Bradley et al.
(1996)
found that some MSTd neurons show FOE responses that are
less affected by pursuit eye movements such that they might reliably
indicate heading direction even during pursuit. However, these
pursuit-tolerant FOE responses were observed during pursuit that was
limited to the neuron's preferred pursuit axis with FOEs placed along
only that axis.
We recorded the responses of MSTd neurons to nine FOEs, widely distributed throughout the visual field, during fixation and pursuit in eight different directions. We found that almost all of the neurons showed significant changes in their responses during pursuit. However, the sum of the net vectors of their response profiles more reliably indicated the FOE during fixation and pursuit. This suggests that MSTd does not contain a subpopulation of pursuit-tolerant neurons but that MSTd neurons might support a population encoding strategy for heading detection during pursuit.
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METHODS |
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Animal preparation
We recorded single neurons from five cerebral hemispheres of
three rhesus monkeys. The animals were prepared for single neuron recording in a surgery performed under general anesthesia using inhaled
isoflurane. Scleral search coils were placed in both eyes (Judge
et al. 1980), and a head holder was embedded in a dental acrylic cap that covered the top of the skull. Bilateral recording cylinders were placed over trephine holes in the parietal calvarium (stereotaxic coordinates: AP
2 mm, ML ±15 mm, mean ± SD; angle 0) above area MST in the superior temporal sulcus
(STS). Postoperative analgesia was administered as judged appropriate
by evaluating the animal's behavior. All protocols were approved by
the Institute Animal Care and Use Committee and complied with Public
Health Service Policy on the humane care and use of laboratory animals.
After recovery from surgery, the monkeys were trained to sit in a
primate chair while performing a visual fixation task for liquid
reward. The position of each eye was monitored with the use of the
magnetic search coil technique (Robinson 1963). Each trial began with the appearance of a red fixation spot (light-emitting diode, 0.25° diam, 2.7 cd/m2) at the center of a 90 × 90° rear-projection tangent screen 48 cm in front of the monkey. The
monkey had to fixate within 500 ms and maintain fixation (±3°) for
the 1-s stimulus period and finally for a variable period after each
stimulus (0.5-1.5 s). At the end of successful trials, a reinforcing
tone was sounded and the monkey received a liquid reward. Task
parameters were adjusted to maintain a high success rate during
training, with the monkeys successfully completing 90% of the
experimental trials after a few weeks.
Visual and pursuit stimuli
Visual stimuli were presented for 1 s during centered fixation and consisted of 500 white dots moving on a black background within the central 76° of the visual field. Each dot subtended 0.19° at 2.61 cd/m2 against a 0.18-cd/m2 background from a 480 × 480 pixelation of the central 90 × 90° of the visual field. The stimuli were generated off-line and presented in pseudorandom order by a 486-based personal computer driving a television projector (Electrohome ECP4100) at a frame rate of 60 Hz.
The movement in all FOE stimuli consisted of an outward radial pattern of dots emanating from the FOE and simulating the observer's approach to a single, remote frontoparallel surface. All dots were assigned a random initial position in the first frame and a random lifetime of 1-60 frames in each stimulus. Dots accelerated as a sine × cosine function of their distance from the FOE maintaining an average speed of 40°/s. Dots were replaced because of lifetime expiration or by a smoothing algorithm that maintained a uniform and consistent dot density across the stimulus in all frames.
Nine optic flow stimuli were used with FOEs at the center of the screen or at one of eight positions, displaced 30° from the center, and distributed around the center at 45° intervals. First, we recorded the responses of each neuron to the nine FOEs during centered fixation to identify the fixation-preferred FOE as that which evoked the largest response. We then recorded responses to pursuit in darkness with the pursuit target moving in eight directions across the central 15° of the screen. Finally, we recorded responses to all nine FOEs during pursuit. In each study, test conditions were interleaved randomly along with control trials in which the monkey fixated a stationary LED with no other visual stimulus.
Smooth pursuit eye movement studies consisted of initial fixation at the center of the screen followed by the fixation target being extinguished and then reappearing 7.5° from the center, in one of two opposite directions along the axis under study. The pursuit target was the red LED image reflected off of a two axis mirror galvanometer system. The animal was required to maintain fixation within a ±3° window while the target moved at 15°/s across the center of the the screen. Trials in which the monkey violated the eye movement window were aborted and those data were rejected. This yielded pursuit gains in the range of 0.8-0.9. The visual stimulus was illuminated 66 ms after the onset of target movement and remained on for 1 s while the target moved across the center to a point 7.5° from the center of the screen. Neurons were studied with pursuit in eight directions along four axes, the horizontal axis and three other axes at 45° intervals around 360°. In this partially blocked design, the horizontal axis generally was studied first, followed by the vertical and oblique axes. However, in many neurons, limits on response stability required that we curtail studies before all four pursuit axes were tested.
In the pursuit studies, the visual stimuli were centered on the moving target while the FOE remained at the same physical position on the screen. This resulted in moving aperture FOE stimuli that were 76 × 76°. The peripheral edge of these stimuli moved from a position 45° from the center of the screen to a position 30° from the center during each pursuit trial. These moving frame FOE stimuli were used to maintain a constant area of retinal stimulation during the movement of the pursuit target across the screen.
Neuron recording
Microelectrode penetrations were made using epoxy-coated
tungsten microelectrodes (Microprobe) that were passed into cortex through a transdural guide tube positioned within the recording cylinder (Crist et al. 1988). Neural activity was
monitored to locate the depth of physiological landmarks, and studies
were initiated whenever neuronal discharges clearly were isolated. Single neuron discharges were isolated using a dual window
discriminator and stored with the stimulus and behavioral event markers
using the REX experimental control system (Hays et al.
1982
). Neuron firing data were averaged across the 1-s period
of six to eight stimulus presentations to characterize responses to
each stimulus.
When a neuron was isolated, we used a hand-held projector to define its
approximate receptive field boundaries. We used physiological criteria
for identifying MSTd neurons including their having large receptive
fields (>20 × 20°), which contain the fixation point, with
direction-selective responses, preferring large moving patterns rather
than moving bars or spots (Duffy and Wurtz 1991a, 1995
; Komatsu and Wurtz 1988
). Location in area MST was
confirmed with deeper extension of the penetration across the STS to
identify the responses of MT neurons. MT was identified as having much smaller receptive fields that are proportionate to the eccentricity of
the receptive field center and show greater responsiveness to bar or
spot movement than is seen in MST. In addition, we preceded each study
of FOE selectivity with a study of the optic flow response properties
of that neuron to confirm characteristic MSTd responses: eight
directions of planar motion (with directions at 45° intervals around
360°), two directions of radial motion (inward or outward), and two
directions of circular motion (clockwise or counterclockwise).
Data analysis
Neuronal activity was sampled for the entire 1-s stimulus period
and averaged across six to eight repetitions of each stimulus. We
tested all responses for statistical significance using a Student's t-test (significance level of P 0.01)
comparing stimulus-evoked activity to the activity recorded in an equal
number of unstimulated control trials. This analysis was used to
annotate some response displays. To analyze the effects of visual or
pursuit stimulus direction on each neuron's activity, we entered
trial-by-trial discharge rates, for each stimulus direction, into an
analysis of variance (ANOVA) using the SAS statistical analysis package (SAS, 1988
). A one-way, between groups design was implemented using
SAS's general linear model routines to measure: FOE selectivity, pursuit direction effects on the fixation-preferred FOE, effects of
pursuit in darkness, and planar motion direction selectivity. A
two-way, between-groups design was used to measure FOE × pursuit interaction effects in studies of pursuit-induced changes in which FOE
is preferred. The results were tested for statistical significance at
the P
0.01 level of confidence with the degrees of
freedom specified by the number of directions and the total number of trials.
Three data display formats were used to visualize these data. Responses to the eight FOE stimuli presented during fixation were plotted as a group of circles. These circles were arranged so that their centers correspond to the relative position of the FOE in each stimulus and their radius is proportionate to the amplitude of the average response to that stimulus (see Fig. 2C). Responses to the eight directions of pursuit across an FOE stimulus were visualized as polar plots (see Fig. 4C). In these polar plots, each polar limb represents the response to one of the pursuit stimuli as a vector having the direction of the pursuit and a length proportionate to the amplitude of the average response. The response to that FOE during fixation is shown as a circle with a radius proportionate to the average response amplitude on the same scale as the polar limbs. Responses to the eight FOEs during either fixation or pursuit also were visualized as polar plots (see Fig. 12A, top left). In these polar plots, each polar limb represents the response to one of the FOE stimuli as a vector having the direction from the center toward that FOE and a length proportionate to the amplitude of the average response. Activity during unstimulated control trials is shown as a circle with a radius proportionate to the average response amplitude on the same scale as the polar limbs.
Population vectors
We followed the approach of Georgopoulos et al.
(1986) by combining the net vectors from the response profiles
of each neuron by making three assumptions. First, each neuron's FOE
responses during fixation can be represented as a net vector that
indicates the FOE direction signaled by that neuron. Second, a
neuron's response to an FOE, minus unstimulated control activity,
indicates the size of its effect on the population response to that
FOE. Third, the response vectors of all neurons sum to create a net vector that represents the population response,
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(1) |
where PNV is the population net vector, i is one of nine FOEs, j is one of nine eye movement conditions (fixation or pursuit), k is one of n neurons, Dk is the scalar mean direction of the FOE responses during fixation for the kth neuron, Rijk is the scalar response amplitude of the kth neuron to FOEi and eye movement condition j, and RCk is the scalar activity amplitude of the kth neuron during control trials of centered fixation without an FOE stimulus.
Population net vectors were displayed in polar plots with a polar limb for each vector. The direction of the vector matches the angle from which its limb emanates from the origin, and the length of each limb indicates the strength of the response. In the special case of the population net vectors shown in Fig. 13B, the origin of the polar plots were shifted to the centroid of the polar distribution to adjust for bias favoring the lower-right direction in the small (n = 40) sample of MST neurons tested with both 15 and 30° FOE conditions.
Statistical tests of circular distributions
We used the Hotelling T2 test
(P 0.01) to determine whether two circular
distributions were significantly different. This is a parametric method
for comparing the degree of overlap between two sets of vectors
(Batschelet 1981
). With the single neuron net vectors,
we compared the responses recorded during fixation to the responses
recorded during the pursuit direction that yielded the net vector that
was most distant from the fixation net vector. With the population net
vectors, we compared the responses to each FOE with the responses to
all other FOEs recorded under that condition of fixation or pursuit.
This evaluates the uniqueness of the population response to each FOE
compared with all other population responses. In these cases, we used a
Bonferroni correction for repeated measures because the circular
distribution used for comparison was the same in each condition. In
comparing the population net vectors derived from fixation and pursuit
trials, we used Mann-Whitney U tests as a nonparametric
method for determining whether pursuit had a significant effect on the
population responses.
Recording sites
The stereotaxic positioning of the recording chambers and the depths of microelectrode penetrations direct neuron recordings into cortical area MST. During the course of these experiments, microelectrode positioning in MST was confirmed by magnetic resonance imaging of the brain with microelectrodes in place. Images were obtained in the sagittal plane on a 1.5 Tesla magnet (General Electric) with fast spoiled gradient echo technique (TR = 23.5, TE = 10.3, 30° flip angle). The MR scans confirmed the location of the electrode tips in the anterior bank of the superior temporal sulcus.
At the end of experiments on a monkey, electrolytic marks (25 µA × 25 s) were made along the penetration tracks in three guides tubes in each hemisphere. After perfusing the animal and fixing the
tissue, posterior cortical blocks were cut in 50-µm thick sections.
Every fourth and fifth section was stained by the Nissl and Luxol Fast
blue methods, respectively. The electrolytic lesions were identified
relative to anatomic landmarks to extrapolate the position of the
recording sites. Histological analysis indicates that the neurons
studied were located in the anterior bank of the superior temporal
sulcus that is included in MSTd (Komatsu and Wurtz
1988).
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RESULTS |
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We studied the responses of 196 neurons in the anterior bank of
the superior temporal sulcus. All neurons preferred large pattern
motion to moving bars and had large receptive fields that included the
fovea and often more than a full quadrant of the visual field, typical
characteristics of MSTd neurons (Komatsu and Wurtz
1988).
Pursuit alters responses to the fixation-preferred FOE stimulus
We first studied response selectivity for the location of the FOE
in radial optic flow fields to identify the fixation-preferred FOE.
Nine optic flow stimuli were used with FOEs at the center of the screen
and at eight locations distributed around the center at 45° intervals
and 30° eccentricity (Fig.
2A). Responses to the
presentations of each stimulus were averaged across the 1-s stimulus
period (Fig. 2B) to yield response amplitudes for each stimulus (Fig. 2C). FOE selectivity was quantified as an
F value derived from a one-way ANOVA. A few neurons were
observed at both extremes of FOE selectivity (Fig.
3A), but the great majority of
neurons (85%, 167/196) showed statistically significant F
values (P 0.01; Fig. 3B, filled
box).
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To characterize the stability of responses to the fixation-preferred
FOE, we recorded the activity of these neurons during pursuit in eight
directions across the fixation-preferred FOE stimulus (Fig.
4A). Responses to the trials
of each direction of pursuit across the fixation-preferred FOE were
averaged (Fig. 4B) to characterize the effects of each
pursuit direction on the FOE response (Fig. 4C). Pursuit
effects were quantified as F values derived from a one-way
ANOVA of direction effects. A wide range of effects was recorded (Fig.
5A) but 72% (142/196) of the
neurons showed statistically significant F values
(P 0.01) for pursuit-induced changes in the
amplitude of responses to the fixation-preferred FOE (Fig.
5B, filled box).
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To assess the potential utility of these neurons as heading detectors,
during fixation and during pursuit, we compared their FOE selectivity
and pursuit effects. Figure 6 shows the
strength of FOE selectivity (abscissa) and pursuit effects (ordinate)
as measured by F values from ANOVA of the 196 neurons
studied. Only 18% (36/196) of the neurons showed significant
(P 0.01) FOE selectivity without significant pursuit
effects (circled symbols).
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Pursuit alters which FOE stimulus is preferred
Pursuit not only changes responses to the fixation-preferred FOE stimulus, it also changes which of the nine FOE stimuli is preferred. Pursuit effects on FOE preferences were tested by recording responses to all nine optic flow stimuli during fixation and pursuit. Figure 7 shows the results of a study in which responses to the nine FOEs during fixation indicated a clear preference for FOE 0 (center circle), whereas responses during right-downward pursuit indicated a clear preference for the upper-left FOE (FOE 4, bold line).
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Such pursuit-induced changes in FOE preference were typical of the 146 neurons studied. Forty-five percent (66/146) of the neurons were tested with all eight pursuit directions, 26% (38/146) were tested with six pursuit directions on three of the four axes, and 29% (42/146) were tested with four pursuit directions on two orthogonal axes. During pursuit, 93% (136/146) of the neurons showed their largest amplitude response to some FOE other than the fixation-preferred FOE.
We measured the significance of pursuit-induced changes in FOE
preference using F values for FOE × pursuit interaction
effects in a two-way ANOVA with FOE and pursuit main effects. Figure
8A illustrates an example of
such interaction effects by showing the amplitude of responses to the
nine FOE stimuli during fixation (circles) and during right-downward
pursuit (right-downward polar limbs). This neuron preferred FOE 7 in
fixation (bold circle) but had a much larger response to FOE 0 (bold
polar limb) during right-downward pursuit. Figure 8B shows
FOE × pursuit interaction effects (abscissa) plotted against the
percentage of neurons yielding such effects (ordinate). Significant
F values (P 0.01) were seen in 86%
(125/146) of the neurons (Fig. 8B, filled bars), indicating that the great majority of neurons significantly changed their FOE
preferences as a function of pursuit condition.
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Figure 9 shows FOE selectivity during fixation (abscissa, as in Fig. 6) plotted against the significance of pursuit-induced changes in FOE preferences (ordinate, as in Fig. 8B) as measured by F values from ANOVA showing the statistical strength of these effects. This analysis reveals that only 5% (7/146) of the neurons showed significant FOE selectivity in fixation without showing significant changes in their FOE preferences during pursuit (circled). The statistical significance of these pursuit effects do not necessarily reflect a systematic change in FOE preference toward, or away from, the pursuit direction.
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We identified pursuit-tolerant FOE selectivity in neurons with stable responses to the fixation-preferred FOE, and maintained FOE preference, during all pursuit directions. Only 3% (5/146) of the neurons showed pursuit-tolerant FOE selectivity by both criteria. We conclude that the responses of individual MST neurons do not account for accurate heading detection during pursuit.
Pursuit responsive neurons
We considered whether neuronal responsiveness to pursuit might
enhance the pursuit tolerance of FOE responses. Pursuit responsiveness was measured as an F value from a one-way ANOVA of responses
to eight directions of pursuit in darkness. About half (47%, 69/146) of the neurons showed significant pursuit effects (P 0.01). There was no systematic relationship between the preferred
direction of pursuit and the preferred FOE. However, there was a
relationship between the preferred pursuit direction and the preferred
planar motion direction (tested using 8 directions of planar motion at 45° intervals around 360°). About two-thirds (64%, 38/59) of the neurons that showed significant response selectivity (P
0.01 by ANOVA) for pursuit in darkness and planar motion showed
nearly opposite preferred directions (180 ± 45°) in those two
studies.
Pursuit-responsive neurons were analyzed separately to test whether they showed less effect of pursuit on their FOE responses. Figure 10A plots FOE selectivity versus pursuit effects on responses to the fixation-preferred FOE (as in Fig. 6) for pursuit responsive neurons. Twenty percent of these neurons (14/69) showed significant FOE selectivity without significant pursuit-induced changes in responses to the fixation-preferred FOE, which is comparable with 18% in the whole sample. Figure 10B plots FOE selectivity versus pursuit effects on which FOE is preferred (as in Fig. 9). Two percent (1/69) of pursuit-responsive neurons showed significant FOE selectivity without a significant shift of FOE preference during pursuit, which is somewhat less than the 5% seen in the whole sample. Combining the criteria of stable responses to the fixation-preferred FOE and stable FOE preference (as in Fig. 9), we find that none (0/69) of the pursuit responsive neurons show pursuit-tolerant FOE selectivity compared with 3% in the whole sample. Thus we conclude that pursuit responses do not enhance, and even may confound, the pursuit tolerance of FOE responses in individual MST neurons.
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Single neuron response vectors
We have used circular statistical analysis in considering all eight FOE responses as vectors having the direction of the FOE from the center and the length of the response amplitude. These vectors are summed to create a net vector with a mean direction that indicates the directional preference of the neuron and a resultant length that indicates the strength of that directional preference. Pursuit-induced changes in single neuron vectors reflect the magnitude of pursuit effects on FOE responses.
Figure 11A shows net vectors from the 196 neurons that were tested with eight shifted FOE stimuli during centered fixation. There is a roughly uniform distribution of mean directions around 360° and wide variation in mean resultant lengths. In 40 neurons, we presented the eight FOE directions with FOE eccentricities of 15° for comparison with the usual FOE eccentricities of 30°. In 80% (32/40) of those neurons, the stimuli with FOE eccentricities of 30° yielded net vectors with larger resultant lengths than did the stimuli having FOE eccentricities of 15° (Fig. 11B). Thus the net vector is affected by FOE eccentricity, as well as FOE direction, and might represent the location of the FOE in the stimulus.
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We assessed the effects of pursuit on single neuron net vectors by
comparing the net vectors obtained during fixation (Fig. 12A, top left) to
those obtained during pursuit. The neuron illustrated in Fig.
12A shows a strong left-downward directional preference for
FOE stimuli presented during fixation. However, there is wide variation
in the mean direction of the net vectors from responses recorded during
pursuit ([arrows]). We determined whether pursuit had significant
effects on a neuron by comparing its fixation net vector to its pursuit
net vectors. This was done by applying Hotelling's
T2 test of the significance of
differences between two circular distributions, those that generated
these two net vectors. Figure 12B illustrates that 81%
(118/146) of the neurons showed significant pursuit effects on the net
vectors (P 0.01 after Bonferroni correction for
multiple comparisons). If we consider only those neurons that showed
significant FOE selectivity, we find that 88% (107/121) showed
significant pursuit effects on the net vectors (P
0.01 after Bonferroni correction). Thus the net vectors from individual
neuron response profiles reveal statistically significant pursuit
effects in the great majority of MST neurons.
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Population response vectors
The vector representation of FOE selectivity can be combined
across neurons to characterize each FOE's cumulative effects on all of
the neurons in the sample. We used population vector analysis
(Georgopoulos et al. 1986) to test whether the combined responses of MST neurons might support pursuit-tolerant heading detection. The net vectors from each neuron's responses to the eight
FOEs in fixation were summed across all neurons as a measure of the
population response (see METHODS). This created a family of
population net vectors, one for each FOE recorded during fixation (Fig.
13A). The population net
vectors (bold lines) reliably indicated the direction of the FOE in the
stimulus. The average difference between the direction of the
population net vector and that of the actual FOE in the stimulus was
9.6 ± 6.7° (mean ± SD).
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These population net vectors also indicated the eccentricity of the FOE in the stimulus. Figure 13B shows the population net vectors for eight FOE stimuli presented at 15° (top left) and 30° (top right) eccentricity from the fixation point. Even though only 40 neurons were recorded with both sets of stimuli, all eight directions evoked larger population net vectors at 30° eccentricity (Fig. 13B, bottom). Thus the population net vectors indicate the position as well as the direction of the FOE.
We examined the population net vectors for responses recorded during
pursuit across the eight FOE stimuli. This analysis created 72 population net vectors, one for each of the eight FOEs recorded in nine
conditions, fixation, and eight pursuit directions (Fig. 14A). During pursuit, the
average difference between the direction of the population net vector
and that of the FOE was 16.0 ± 10.6°. We compared the
population net vectors from fixation with those obtained during pursuit
for each of the eight FOEs. Hotelling's T2 test of the differences between two
sets of vectors was used to determine whether the circular
distributions that created each pair of population net vectors were
significantly different from each other. None of the 64 comparisons
revealed a significant difference (P 0.01, even
without Bonferroni correction) between responses during fixation and
pursuit (Fig. 14B). Figure 14C illustrates the
close relationship between FOE direction and the population net vector
direction across all pursuit directions (left) (slope = 0.99, Yint = 3.1, r2 = 0.97) with no relationship between pursuit direction and the population
net vector direction (right) (slope = 0.004, Yint = 156.1, r2 = 0).
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We compared the population net vectors from fixation and pursuit to
test whether they were comparable in strength and direction selectivity. Ninety-four percent (68/72) of population net vectors had
mean directions within 30° of the FOE that evoked the responses and
54% (39/72) were within 15° (Fig.
15, abscissas). We used a Mann-Whitney
U test to compare the directions of the population net
vectors for each FOE during fixation (9.6 ± 6.7°) and pursuit (16 ± 10.6°) and found no significant difference
(P 0.01). We measured the strength of the population
net vectors by their resultant lengths (Fig. 15A, ordinate)
and found that fixation and pursuit evoked comparably sized effects.
The average resultant length in fixation was 771 ± 189, and in
pursuit that value was 662 ± 151; these values were not
significantly different (MWU, P
0.01). We measured
the uniqueness of each population net vector by comparing its direction
to that of the population net vectors from other FOEs during the same
condition of fixation or pursuit. Figure 15B (ordinate)
shows that 64% (49/72) of the population net vectors were within 15°
of the ideal separation of 45°. The average directional separation in
fixation was 41 ± 10°, and in pursuit that value was 36 ± 12°, which were not significantly different (MWU, P
0.01). These findings lead us to conclude that MST population net
vectors can indicate the direction of heading during fixation and
pursuit.
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DISCUSSION |
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Pursuit effects on individual neuron responses
We tested optic flow selective MST neurons (Figs. 2 and 3) with a variety of FOE and pursuit stimuli (Fig. 4). Our findings suggest that almost all MST neurons show substantial changes in their responses to optic flow stimuli when those stimuli are presented during different directions of pursuit. This assessment is based on pursuit-induced changes in the responses of MST neurons that show significant FOE selectivity. Only 18% of MST neurons showed significant FOE selectivity without significant changes in responses to the fixation-preferred FOE during pursuit (Figs. 5 and 6). Only 5% of MST neurons showed significant FOE selectivity and maintained the same FOE preference during pursuit (Figs. 8 and 9). Combining these criteria revealed that only 3% of these neurons showed stable FOE response characteristics during pursuit (Fig. 9).
We considered that a subpopulation of MST neurons, those that respond to pursuit in darkness, might be more pursuit tolerant, but that was not the case (Fig. 10). We also considered that an analysis based on single neuron response vectors might reveal more stable FOE selectivity during pursuit. Net vectors from each neuron's response profiles were compared from fixation and pursuit trials (Figs. 11 and 12). The great majority of FOE selective neurons (88%) showed significant changes in the net vectors evoked by FOE stimuli presented during fixation and pursuit. This number might have been even higher if all eight directions of pursuit had been recorded in all neurons.
The earlier finding of pursuit tolerance in 27% of MST neurons was
based on studies of FOEs arranged along the axis of each neuron's
preferred pursuit direction, with pursuit limited to only that axis
(Bradley et al. 1996). That approach presupposes an
additional mechanism that selects a different subset of neurons as
heading detectors for each pursuit axis. Using single neuron vectors,
we too found that some neurons (19%) might appear to be pursuit
tolerant. We consider this to indicate that any single criterion will
fail to detect pursuit effects in some neurons. However, the more
comprehensive analyses detailed above show that very few (3%) of the
single neurons actually maintain stable FOE selectivity during pursuit.
We cannot exclude the possibility that more pursuit-tolerant single neurons might appear in some other analysis. Rather we conclude that single neuron responses are vulnerable to pursuit effects from a variety of perspectives. In contrast, the complete population of MST neurons might mediate heading detection for all FOEs and all pursuit directions.
Pursuit effects on population responses
To assess the viability of a population encoding strategy for heading detection, we summed the net vectors from each neuron to derive a population net vector for each stimulus. We used the direction of each neuron's net vector to indicate the direction of the FOE signaled by that neuron's firing. The population response to a given stimulus was derived by scaling each neuron's net vector by its response amplitude to that stimulus and then summing the vectors for all neurons. This approach revealed that population net vectors from MST neurons indicated the direction (Fig. 13A) and eccentricity (Fig. 13B) of the FOE during fixation. Population net vector direction selectivity was maintained during pursuit (Fig. 14A) as reflected by the absence of significant differences between population net vectors recorded during fixation and pursuit (Fig. 14B). There is a strong relationship between population net vector direction and FOE direction but no systematic relationship to pursuit direction (Fig. 14C). When we compared the strength and directionality of population net vectors from fixation and pursuit, we found no significant differences (Fig. 15). These findings suggest that the population net vector can indicate the location of the FOE in optic flow stimuli during both fixation and pursuit.
The population encoding strategy used here was derived from work on
cortical motor neuronal encoding of arm movements. As in motor cortex,
MST population net vectors have the disadvantage of only considering a
neuron's preferred direction instead of its complete response profile
(Sanger 1996). In addition, just as population net
vectors in motor cortex might reflect links to muscle actions instead
of movement direction (Mussa-Ivaldi 1988
), MST
population net vectors might reflect links to planar motion or speed
tuning for optic flow (Duffy and Wurtz 1997b
) rather
than the FOE created by the pattern.
However, population encoding of FOE location has two advantages over
approaches that focus on the responses of single neurons. First,
population encoding does not require an additional mechanism that
selects a different subpopulation of neurons to be used under different
conditions (i.e., different pursuit directions). Second, population
encoding does not require that signals from certain neurons be used in
specific combinations to derive FOE location, rather the output of all
neurons can be combined in all instances. Population net vectors
previously have been shown to reflect known visual cortical properties
(Vogels 1990) and can bridge the needed transformations
between visual perception and motor control (Rossetti et al.
1995
).
We do not conclude that population encoding in MST necessarily
uses vector summation. Many viable alternative algorithms have been
proposed that would be expected to yield qualitatively similar results
with somewhat greater precision in the population signals (Salinas and Abbott 1994; Sanger 1996
;
Zemel et al. 1998
), but no clear consensus has emerged
favoring one particular algorithm. We report the success of the
population vector approach to demonstrate that the information needed
for FOE identification is in the population response of MST neurons and
to draw attention to this further opportunity for detailed analysis of
neural population encoding.
MST contributions to heading perception during pursuit
Pursuit eye movements disrupt heading detection from optic
flow (Longuet-Higgins and Prazdny 1980; Royden et
al. 1992
; Warren and Hannon 1990
). Extraretinal
signals about eye movements facilitate performance during pursuit so
that it approaches performance attained during fixation (Royden
et al. 1992
; Stone and Perrone 1997
;
Warren and Hannon 1990
). In the absence of an
extraretinal signal, retinal cues from motion parallax support residual
heading detection from optic flow (Warren and Hannon
1990
), at least at slow speeds (<1°/s) of simulated pursuit
(Royden et al. 1992
), more effectively in some subjects
than others (Stone and Perrone 1997
). The potential utility of retinal cues has been modeled using several algorithms for
heading recovery from optic flow during pursuit (Lappe and Rauschecker 1993
; Perrone and Stone 1994
;
Verri et al. 1992
) and this remains an area of active
inquiry.
Neurophysiological findings show that MST neurons might use both
retinal and extraretinal strategies to compensate for pursuit effects
on heading detection. MST neurons respond to the directional patterns
in optic flow (Duffy and Wurtz 1991b, 1995
;
Graziano et al. 1994
; Orban et al. 1992
;
Saito et al. 1986
) and to extraretinal signals about
pursuit eye movements (Dursteler et al. 1987
;
Komatsu and Wurtz 1988
; Newsome et al.
1988
). In addition, MST neurons are sensitive to speed
(Orban et al. 1995
; Tanaka et al. 1989
) and depth cues (Duffy and Wurtz 1997a
) in optic flow as
well as vestibular cues about self-movement (Duffy 1998
). In addition, neurophysiological recordings (Celebrini and Newsome
1994
), chemical lesions (Pasternak and Merigan
1994
), and electrical microstimulations (Britten and van
Wezel 1998
; Celebrini and Newsome 1995
) suggest that MST is involved in the motion perception. Together these findings
support the notion that MST is well suited to contribute to heading
detection from optic flow during fixation or pursuit. Our findings
suggest that a population encoding strategy may be implemented in this
process.
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ACKNOWLEDGMENTS |
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This work was supported by National Eye Institute Grant R01-10287 to C. J. Duffy. Additional support for C. J. Duffy was provided by the Sloan Foundation, the Human Frontier Science Program Grant RG71/96, and a grant to the University of Rochester Department of Ophthalmology from Research to Prevent Blindness. Support for W. K. Page was provided by a National Research Service Award F31MH-11616 from the National Institute of Mental Health and a National Eye Institute Training Grant T32EY-07125 to the University of Rochester Center for Visual Sciences.
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FOOTNOTES |
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Address for reprint requests: C. J. Duffy, Dept. of Neurology, Box 673, University of Rochester Medical Center, 601 Elmwood Ave., Rochester, NY 14642.
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 May 1998; accepted in final form 22 October 1998.
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REFERENCES |
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