Division of Biology, California Institute of Technology, Pasadena, California 91125
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ABSTRACT |
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Shenoy, Krishna V., David C. Bradley, and Richard A. Andersen. Influence of gaze rotation on the visual response of primate MSTd neurons. When we move forward, the visual image on our retina expands. Humans rely on the focus, or center, of this expansion to estimate their direction of heading and, as long as the eyes are still, the retinal focus corresponds to the heading. However, smooth rotation of the eyes adds nearly uniform visual motion to the expanding retinal image and causes a displacement of the retinal focus. In spite of this, humans accurately judge their heading during pursuit eye movements and during active, smooth head rotations even though the retinal focus no longer corresponds to the heading. Recent studies in macaque suggest that correction for pursuit may occur in the dorsal aspect of the medial superior temporal area (MSTd) because these neurons are tuned to the retinal position of the focus and they modify their tuning during pursuit to compensate partially for the focus shift. However, the question remains whether these neurons also shift focus tuning to compensate for smooth head rotations that commonly occur during gaze tracking. To investigate this question, we recorded from 80 MSTd neurons while monkeys tracked a visual target either by pursuing with their eyes or by vestibulo-ocular reflex cancellation (VORC; whole-body rotation with eyes fixed in head and head fixed on body). VORC is a passive, smooth head rotation condition that selectively activates the vestibular canals. We found that neurons shift their focus tuning in a similar way whether focus displacement is caused by pursuit or by VORC. Across the population, compensation averaged 88 and 77% during pursuit and VORC, respectively (tuning shift divided by the retinal focus to true heading difference). Moreover the degree of compensation during pursuit and VORC was correlated in individual cells (P < 0.001). Finally neurons that did not compensate appreciably tended to be gain-modulated during pursuit and VORC and may constitute an intermediate stage in the compensation process. These results indicate that many MSTd cells compensate for general gaze rotation, whether produced by eye-in-head or head-in-world rotation, and further implicate MSTd as a critical stage in the computation of heading. Interestingly vestibular cues present during VORC allow many cells to compensate even though humans do not accurately judge their heading in this condition. This suggests that MSTd may use vestibular information to create a compensated heading representation within at least a subpopulation of cells, which is accessed perceptually only when additional cues related to active head rotations are also present.
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INTRODUCTION |
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Visual motion provides primates with a wealth of
information about where objects are and how they move. In many
situations, however, visual motion alone is ambiguous because only
relative motions are seen. For example, rightward motion in the retinal image could be caused either by an object moving to the right or by a
turn of the eyes or head to the left. In these situations, the visual
system must rely on extraretinal signals containing information about
eye and head movement to interpret visual motion correctly. Although a
great deal is known about the neural structures that interpret visual
motion when the gaze is fixed (Maunsell and Newsome
1987) as well as about the neural mechanisms that integrate
visual and gaze-position signals (Andersen 1997
), much less is known about motion processing during eye and head rotations.
How does the brain combine visual-motion and extraretinal gaze-rotation
signals? We considered this question in the context of visual
navigation (Gibson 1950; Warren 1995
).
When we move forward, the retinal image expands. The center or focus of
this expansion (FOE) corresponds to the heading, or instantaneous
direction of translation, when the gaze is fixed, and humans can use
the FOE to estimate accurately their heading (Warren and Hannon
1988
). However, when we smoothly shift our gaze, as during
pursuit eye movements, the FOE on the retina is displaced from the true
heading as illustrated in Fig. 1. Humans
use an extraretinal pursuit signal to compensate for this displacement
and, thereby, are able to judge their heading quite accurately even
during pursuit (mean compensation of 90%) (Crowell et al.
1998a
; Royden et al. 1992
, 1994
).
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We recently reported that many neurons in macaque extrastriate cortical
area MSTd (dorsal subdivision of the medial superior temporal area) use
pursuit signals to compensate, at least partly, for the displacement of
the FOE caused by pursuit eye movements (Andersen et al.
1996; Bradley et al. 1996
). MSTd is well suited for such visual and nonvisual cue integration, which is essential for
estimating heading, because of the following receptive field specializations and extraretinal contributions: 1) large
receptive fields (RFs) (often >50° in diameter); 2)
selectivity for the direction of laminar motion; 3)
selectivity for expansion, contraction, rotation, or spiral optic-flow
patterns (Duffy and Wurtz 1991a
,b
; Graziano et
al. 1994
; Komatsu and Wurtz 1988a
,b
;
Lagae et al. 1994
; Lappe et al. 1996
;
Orban et al. 1992
; Raiguel et al. 1997
; Saito et al. 1986
; Sakata et al. 1985
,
1994
; Tanaka and Saito 1989
; Tanaka et
al. 1986
, 1989
); 4) optic-flow selectivity is typically invariant to the position of the pattern within the RF
(Duffy and Wurtz 1991b
; Graziano et al.
1994
; Lagae et al. 1994
; Orban et al.
1992
); 5) optic-flow selectivity does not depend on
the forms or cues of the moving objects (Geesaman and Andersen 1996
); 6) optic-flow selectivity is typically
invariant to the size of the visual pattern (Graziano et al.
1994
); 7) responses are modulated by the position of
the FOE in the RF (Duffy and Wurtz 1995
); 8)
responses are modulated by the rate of optic-flow expansion
(Duffy and Wurtz 1997
); 9) responses are
modulated by stereoscopic disparity (Roy and Wurtz 1990
;
Roy et al. 1992
); 10) smooth-pursuit signals
are direction and speed tuned (Bradley et al. 1996
;
Erickson and Thier 1991
; Kawano et al. 1984
,
1994
; Newsome et al. 1988
); and 11)
eye-position signals are present (Bremmer et al. 1997
;
Squatrito and Maioli 1996
; Squatrito et al.
1997
). Finally a recent report that microstimulating
expansion-selective columns in macaque MSTd systematically biases
heading estimates provides direct evidence that MSTd neurons contribute
to the visual sensation of heading (Britten 1998
;
Britten and van Wezel 1998
, Celebrini and Newsome
1995
; Geesaman et al. 1997
).
The question remains whether MSTd neurons also shift focus tuning to
compensate for FOE displacements caused by smooth head rotations, which
commonly occur during gaze tracking (see Fig. 1). Recent human
psychophysical experiments indicate that self-motion judgments are
quite accurate during active, smooth head rotations (observers smoothly
rotate their heads while fixating a target moving with the head; mean
compensation of 94%) (Crowell et al. 1998a). In this
condition, there are three sources of extraretinal information that
potentially drive compensation: proprioceptive information from the
neck muscles, efferent information about the motor commands sent to the
neck muscles, and vestibular canal information about head rotation.
As a first step, we asked if vestibular canal signals contribute to
MSTd focus-tuning compensation during head-in-world rotations, as
pursuit signals contribute to focus-tuning compensation during eye-in-head rotations. We suspected canal signals in MSTd because otolith signals recently were found in MSTd (Duffy 1998)
and because canal signals have been reported in MSTl and in nearby
areas of the posterior parietal cortex (Kawano et al. 1980
,
1984
; Sakata et al. 1994
; Snyder et al.
1998
; Thier and Erickson 1992a
,b
). To
investigate this question, we trained two monkeys to perform a
vestibulo-ocular reflex cancellation (VORC) task in which we mechanically rotated their bodies and heads while they fixated a target
rotating with their bodies and heads. The eyes rotate in the world, but
not in the head, during VORC. By measuring the response of MSTd cells
to optic-flow patterns during both VORC and fixed gaze conditions, we
could assess vestibularly induced focus tuning shifts. We found
substantial focus tuning compensation during VORC. Interestingly
although vestibularly-derived signals are essential for accurate
self-motion estimates during active, smooth head rotation
(Crowell et al. 1998a
), humans do not judge their
self-motion accurately during the VORC task where the only extraretinal
signal available is vestibular in origin (mean compensation of 4%)
(Crowell et al. 1998a
). MSTd physiology results are
compared with human psychophysical performance in the
DISCUSSION.
The oculomotor mechanisms engaged during VORC are not well understood.
There appear to be three possibilities. First, during VORC the
vestibular ocular reflex (VOR) could be shut down, thereby allowing
fixation of the VORC target without any eye movement commands. Second,
it is possible that the VOR is active during VORC and that a pursuit
signal is generated to oppose the VOR signal, thereby enabling
VORC-target fixation. Finally a combination of these mechanisms could
underlie VORC-task performance. Recordings from the brain stem appear
to implicate both ocular and vestibular sources of VORC signals,
consistent with this last possibility (Cullen and McCrea
1993; Cullen et al. 1991
, 1993
; Tomlinson
and Robinson 1984
). Posterior parietal cortex (PPC) studies
also appear to be consistent with this view. Robust responses have been
reported in MSTl, the lateral subdivision of MST, during sinusoidal
VORC (Thier and Erickson 1992a
,b
). These responses
persisted, though at roughly half the strength, during sinusoidal
rotation in complete darkness, which isolates the vestibular canal
component of the signal. Regardless of the exact origin, VORC signals
are present naturally during tracking head movements and are viable
extraretinal cues for heading estimation.
Brief reports of this material have appeared previously (Shenoy
et al. 1996, 1997
).
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METHODS |
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Animal preparation
Experiments were conducted in two hemispheres of two adult, male
Rhesus monkeys (Macaca mulatta), and all protocols were
approved by the Caltech Institutional Animal Care and Use Committee. In a sterile surgical procedure under sodium pentabarbitol anesthesia, stainless steel bone screws were implanted in the skull, and a fixture
for immobilizing the head was constructed with methylmethacrylate. In
the same procedure, a Teflon-insulated, 50-gauge stainless steel wire
coil was implanted between the conjuctiva and the sclera for the
measurement of eye position (Judge et al. 1980;
Robinson 1963
). The coil was connected electrically to a
coaxial connector embedded in the methylmethacrylate.
Behavioral training on oculomotor tasks began no sooner than 1 wk after surgery. Monkeys received juice rewards for correct performance during both behavioral training and experimental sessions. Adequate performance levels, typically >90% on all tasks, were reached after several weeks of training. A subsequent surgery was performed to open a craniotomy and to implant a Lucite cylinder (5 mm posterior, 17 mm lateral, dorsoventral orientation), which provided chronic access to cortical area MSTd for electrophysiological recording.
Recording techniques
Extracellular action potentials were monitored with
varnish-coated tungsten microelectrodes, with ~1 M impedance at 1 kHz. A stainless steel guide tube was advanced manually dorsoventrally through the dura and the electrode was extended further into the brain
with a hydraulic micropositioner. Action potentials were amplified and
single neuron waveforms were isolated with a time-voltage discriminator. MSTd was identified based on the following criteria: 1) depth below the dura; 2) position relative to
gray and white matter boundaries; 3) location relative to
the middle temporal (MT) cortical area; 4) receptive field
size (typically >50° diam with both contra- and ipsilateral visual
responses); 5) selectivity for optic-flow type (e.g.,
expansion); and 6) position invariance of optic-flow
selectivity within the receptive field. Neurons tuned for the type of
optic flow, by visual inspection and in at least one location in the
RF, were tested in all experiments and are included in our database.
Action potential event times and behavioral states were stored for
subsequent analysis.
Visual stimuli
All experiments were conducted in a sound-insulated room, which was totally dark except for the visual stimuli. We generated expanding random dot optical flow fields by simulating forward translation at 16.5 cm/s toward a fronto-parallel wall held 38.1 cm distant. One thousand dots were placed randomly in computer memory representing an 82 × 82° area, and each dot was assigned a random age. Dots moved at constant velocity for the remainder of their 300-ms lifetime or until they crossed the area perimeter, in which case they were extinguished and reborn at a random location. Dot speeds were proportional to the eccentricity from the FOE, reaching 9.2°/s at 24° eccentricity. The direction of dot motion was rotated by 90, 180, or 270° from the expansion stimuli to create counterclockwise rotation, contraction, or clockwise rotation stimuli, respectively. Dots were white (~10 candela/m2) on a completely black background and were not anti-aliased. Displays were viewed binocularly.
We displayed an 18 × 18° subregion (window) of the total area simulated on a computer monitor operating in 640 × 480 pixel resolution and 60 frames/s mode. This was the largest possible stimulus area due to monitor size (50 × 38° at 38.1 cm), monitor weight (the monitor moved with the vestibular chair), stimulus movement, and stimulus position constraints. Such display windows contained 48 dots (0.15 dots/deg2) with each dot subtending 0.08 × 0.08° of visual angle (1 pixel). Display windows, including the optic flow and the invisible window frame, were presented: at a fixed location in the room, moving with the fixation target, which moved in the room, or drifting across the room (see Behavioral tasks).
Pursuit targets moved an integral number of pixels/frame resulting in
smooth motion (e.g., 2 pixels/frame = 9.2°/s); consequently, horizontal and vertical pursuit targets moved at 9.2°/s while 45°
diagonal pursuit targets moved times faster. Fixation and
VORC targets remained stationary on the display, but during VORC trials
the entire display moved at 9.2°/s or
times faster for
diagonal trials. The direction and speed of pursuit- and VORC-tracking
targets were identical in all experiments. Fixation, pursuit, and VORC
targets subtended 0.24 × 0.24° of visual angle.
To simulate different headings, we created nine optic-flow patterns
with varying focus (origin) positions by shifting the origin of the
82 × 82° pattern behind the 18 × 18° window (aperture). Focus positions varied from 32 to 32° in 8° increments along an
axis either parallel to the neuron's preferred-null axis (see Data analysis) for expansion/contraction neurons, or
orthogonal to this axis for rotation neurons. Different axes are
required because the direction of origin shift depends on both the
visual pattern and on the direction of gaze rotation (Andersen
et al. 1996
; Bradley et al. 1996
). For example,
rightward pursuit across an expansion pattern shifts the focus
rightward (parallel to pursuit), whereas rightward pursuit across a
clockwise rotation pattern shifts the origin of rotation upward
(orthogonal to pursuit). Diagonal focus spacing and range was increased
by
to account for the
increased real and simulated gaze-tracking speeds during diagonal trials.
We selected the gaze rotation and display parameters described above to
shift the focus (origin) during gaze rotation by 24°. Physical
geometry and gaze rotation lead to the following governing equations:
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Vestibular stimuli
Monkeys were seated comfortably in a primate chair which we attached to a vestibular chair (Acutronic, Pittsburgh, PA). Precise horizontal plane (yaw) and sagittal plane (pitch) rotations were executed by the feedback control system. Vestibular chair sensors reported real-time position information, which was monitored and used to trigger visual stimulus onsets during VORC trials. We fixed the monkeys' heads to the primate chair such that the axis of yaw rotation passed through the midline (medial/lateral), midway between the ear canals and the center of the eyes (anterior/posterior). The pitch rotation axis was positioned midway between the ear canals and the center of the eyes (anterior/posterior), in the plane of the ear canals and the center of the eyes (dorsal/ventral). This arrangement is intermediate between the natural eye rotation axes, passing roughly through the center of the eyes, and the natural head rotation axis, passing through the neck. This compromise produces rotations that approximate natural head rotations but without creating excessive visual translation during VORC conditions, which is not present during eye pursuit.
A computer monitor and electromagnets also were mounted on the vestibular chair. The electromagnetic coils were attached to the vestibular chair to improve eye position accuracy by maintaining a more uniform local magnetic field during large-angle body rotations. Even with such measures, it was not possible to maintain a perfectly constant magnetic field because the position and orientation of various vestibular chair components in the magnetic fringe fields changed through the course of a vestibular chair rotation. The resulting systematic drift in eye position measurement over the range of vestibular chair rotations (±20° yaw and/or pitch) was ~1° and was substantially <1° over the smaller rotation range traveled during visual stimulus presentation and data collection (±4.6° yaw and/or pitch).
Although the on-line eye position tolerance (demand box) was enlarged slightly to account for this variation (typically ±4° square), inspection of the eye traces revealed accurate pursuit (very few, small catch-up saccades), accurate VORC (very few, small VOR/OKN drifts), accurate simulated gaze rotation (fixation), and accurate fixed gaze (fixation). Off-line analysis consisted of selecting 16 heading experiments at random, 8 from each monkey and totalling 20% of all heading experiments, and calculating the deviation of the eye from the average fixation position (simulated gaze rotation and fixed gaze trials), from the average VORC position (VORC trials), or from a line regressed through the eye trace (pursuit trials). The average standard deviation across all trials was quite small in all conditions: horizontal (0.31°) and vertical (0.32°) channel VORC, horizontal (0.30°) and vertical (0.31°) channel pursuit, horizontal (0.18°) and vertical (0.30°) channel simulated gaze rotation, and horizontal (0.18°) and vertical (0.30°) channel fixed gaze.
We reproduced the retinal image seen during pursuit by generating head,
body, and eye rotations using the VORC paradigm. Both pursuit and VORC
conditions require an ~2 s constant angular rotation period
(9.2°/s, times faster during combined yaw and pitch
rotations), during which time the visual stimulus is displayed and
neural data are collected. This constant angular velocity period was
embedded in the middle of a trapezoidal speed trajectory: 307 ms,
30°/s2 constant acceleration phase; ~2 s constant
velocity phase; and a 307 ms, 30°/s2 deceleration phase.
To verify that this protocol effectively generates VORC signals, we
recorded the response of two representative MSTd neurons in a pursuit
and VORC paradigm in which only a pursuit or VORC target was present
and in which the constant rotation period lasted 4 s. Robust
responses persisted out to
4 s in both conditions, indicating that
the velocity signal, which is integrated from rotational acceleration
cues by the vestibular canal apparatus and which ultimately drives the
MSTd VORC signal, has a time constant of at least a few seconds
(Wilson and Jones 1979
).
Behavioral tasks
Monkeys were trained to perform three types of behavioral tasks: fixation, pursuit, and VORC. One or more of these behavioral tasks were employed in three sequential, blocked experiments: preferred optic flow, preferred direction, and heading.
Preferred-optic-flow experiment trials consist of fixating
(less than ±2.5° eye box) a target for 1.7 s, during which time a 1.2-s optic-flow display appears. To determine the preferred type of
optic flow, expansion, contraction, clockwise rotation, and
counterclockwise rotation stimuli were presented pseudorandomly (stimuli randomly drawn without replacement and blocked by repetition number). Optic-flow stimuli were centered at 0°,0°; 10°,+10°;
10°,
10°; 10°,10°; and 10°,
10° (horizontal, vertical
pairs; + indicates either ipsilateral or up) while gaze was fixed at 0°,0°. An additional configuration with gaze fixed at
10°,0° and optic flow centered at +15°,0° also was included to test for far ipsilateral responses. Receptive field sizes were estimated using
this timing and hand-positioned patterns.
Preferred-direction experiment trials consist of pursuit or
VORC of a moving target (9.2°/s or faster) for 1.5 s, during which time a 1.2-s preferred-optic-flow display appears and
moves with the target (less than ±4.0° eye box). Trial illustrations
and timing diagrams are shown in Fig. 2.
During VORC trials, the target remains fixed on the screen (screen
moved with the vestibular chair), and there is no eye-in-head rotation (confirmed by monitoring eye position). Also there is nominally no
head-on-body rotation, promoted by seating the monkeys comfortably and
noting his relatively constant seating position in the primate chair
(Lucite box). Eye pursuit and VORC trials were presented pseudorandomly
(monkey FTZ) or blocked (monkey DAL) in eight
directions in the fronto-parallel plane (0, 45, ... , 315°; 0°
indicates eye rotation or chair yaw to the right; 90° indicates
upward eye rotation or chair pitch). Gaze trajectories were centered on
the same room location for both tasks to equalize all gaze angles on
average. Gaze rotated by ±4.6° about the central location during the
1.0-s data-analysis period.
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We presented and moved the preferred-optic-flow stimulus (e.g.,
expansion) along with the pursuit and VORC target in this experiment
for two reasons. First, optic flow significantly increases the neural
response and allowed us to better determine gaze-rotation directional
tuning. Second, we sought to select the axis along which the
gaze-rotation signal varies maximally. As long as the retinal stimulus
is comparable between pursuit and VORC trials, the presence of a visual
stimulus is acceptable. That the results of the heading experiment
reported here are similar to the results we reported previously
(Bradley et al. 1996), where a visual stimulus was not
present during pursuit-axis selection, suggests that directional tuning
largely is unaffected by the presence of the optic-flow pattern.
Heading experiment trials consist of fixation, pursuit or
VORC of a target (less than ±4.0° eye box) for 1.5 s, during
which time a 1.2-s preferred-optic-flow display appears. Nine
optic-flow stimuli, with differing focus (origin) positions, and four
behavioral tasks were presented pseudorandomly. Trial illustrations and
timing diagrams are shown in Fig. 3.
Pursuit and VORC tasks are identical to those in the
preferred-direction experiment except that the optic-flow stimuli are
stationary in the world in this experiment. Gaze tracking almost always
occurred along the VORC preferred-null axis with small differences
occurring if the pursuit and VORC preferred-null axes differed; we
split the difference. Gaze rotations were performed in both the
preferred and the null directions. The fixed gaze task was identical to
the gaze tracking tasks except that the eye, head, and body were
stationary in the room. The simulated gaze-rotation condition was
identical to the fixed gaze condition except that the visual stimulus
drifted in the direction opposite to the direction tracked in the gaze
tracking tasks. This created a retinal stimulus identical to the
retinal stimuli in the gaze-tracking tasks to the extent that eye
movements were performed perfectly. Counter drifting the visual
stimulus approximates counter rotating the visual stimulus quite well
for the small, centrally located stimuli used in these experiments.
Gaze trajectories were centered on the same room location across all
tasks to equalize all gaze angles on average. Gaze rotated by ±4.6°
( more for diagonal gaze rotations) about the central
location during the 1.0-s data-analysis period. The gaze trajectory
center was located within 7.07° of the screen center, and the visual
stimuli were centered within 14.1° of the gaze trajectory center.
Gaze and stimulus centers were offset to position the optic-flow
stimuli in the RF "hot spot."
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Data analysis
Horizontal and vertical eye positions were sampled every millisecond and yaw and pitch head positions were sampled every 8 ms. Action potential event times were stored for off-line analysis with microsecond resolution. Neurons from two monkeys were recorded. Data trends are similar and significant in both monkeys so the data were pooled for analysis.
We analyzed the preferred-optic-flow experiment data with a nonparametric, one-way ANOVA (Kruskal-Wallis) to test for optic-flow pattern tuning. Each of the six locations was considered separately. Mean firing rates from three (DAL) or four (FTZ) replicates during the last 1.0 s of the 1.2-s stimulus presentation were used for the ANOVA.
We analyzed the preferred-direction experiment data to
determine the preferred pursuit and VORC directions. We estimated these directions as the angle of the response-weighted vector sum
(Fisher 1993; Geesaman and Andersen
1996
). The preferred direction (
) is equal to
arctan(S/C), where S is the sum of
Fi sin
i and
C is the sum of Fi cos
i over all eight gaze-rotation
directions (i = 1, 2, ... , 8).
Fi and
i correspond to the average firing rate and specified gaze-rotation angle, respectively, associated with each of the eight gaze-rotation directions. The preferred direction was adjusted to the proper quadrant
based on the signs of S and C. We also calculated
the trigonometric mean which we used as a selectivity index (SI). SI is
equal to
divided by the sum of Fi over all eight
gaze-rotation directions. Unlike other selectivity measures, such as
1
(null/pref), which only indicate the modulation along a
single axis, SI reaches unity (perfect selectivity) only if all
nonpreferred directions are totally suppressed. An SI of zero indicates
the complete lack of tuning.
Circular, nonparametric statistics were used to assess
preferred-direction biases (Rayleigh) and cell-by-cell
preferred-direction correlation (angular-angular correlation). We
determined the significance of SIs with boot-strap methods: we created
directional tuning curves by drawing (without replacement) eight mean
firing rates from a given cell's response database, we randomly
assigned the firing rates to the eight directions, and finally
calculated the SI for this tuning curve. We repeated this procedure
1,000 times for pursuit and VORC for all cells. If the measured SI
exceeded the 95% point of the simulated distribution, we considered
the SI significant. We found that the Rayleigh test, which has been used previously for determining SI significance (Geesaman and Andersen 1996), is overly conservative. Finally a nonparametric correlation test (Spearman) was used to test for relationships between
SIs in the population. All analyses used the mean firing rates from two
(FTZ) or three (DAL) replicates during the last 1.0 s of the 1.2-s stimulus presentation.
We analyzed the heading experiment data to determine the influence of gaze tracking on the visual response. We constructed seven focus tuning curves for each neuron, one for each gaze tracking condition in both tracking directions, using mean firing rates and variances from three (DAL) or four (FTZ) replicates during the last 1.0 s of the 1.2-s stimulus presentation. We used Kruskal-Wallis analyses to assess the tuning significance for each focus tuning curve.
To understand the effects of preferred- and null-direction VORC, pursuit, and simulated gaze rotation, it is necessary to compare tuning curves in the different gaze-rotation conditions with the fixed-gaze condition tuning curve. In general, we found that gaze rotations preserved the primary shapes of the fixed-gaze tuning curves, which are often sigmoidal or Gaussian (examples will be presented in RESULTS). This observation led to our approach for comparing tuning curves: the relationship between two tuning curves with similar shapes can be characterized approximately by two parameters, one reflecting horizontal effects and one reflecting vertical effects.
Figure 4 shows three possible alignments
of two focus tuning curves. Figure 4A illustrates a
horizontal, or independent variable, offset between two tuning curves
with related structures. We calculated this offset with a
cross-correlation analysis, which provides a measure of tuning curve
alignment at each relative horizontal offset, as one curve is shifted
past the other. The optimal shift is identified as the relative
horizontal offset with the maximum correlation coefficient.
Parameterizing the vertical, or dependent variable, relationship is
more difficult because the tuning curves are not necessarily functions
(single valued) of the dependent variable, ruling out cross-correlation
techniques. Moreover, as shown in Fig. 4B, the relationship
between the curves may be described as multiplicative, additive,
linear, or even nonlinear. Distinguishing between these possibilities
has inherent difficulties and is beyond the scope of this report.
Instead, we attempted to quantify the vertical relationship with a
single parameter to describe the basic effect and to compare this
effect across conditions. We compared multiplicative and additive
measures and found that the 2 goodness-of-fit values
were comparable across gaze-rotation conditions and across the
population of cells. We chose the multiplicative measure for two
reasons: multiplicative gain, or the ratio between two responses, is
normalized automatically for firing rate so comparison between
conditions and cells is straighforward and multiplicative gain commonly
is used for characterizing modulatory effects (Brotchie et al.
1995
; Snyder et al. 1998
). To calculate the
gain, we used the mean discharge rates during visual stimulus presentation as opposed to the difference between the peristimulus and
the prestimulus discharge rates. Such subtraction would remove the
additive contributions of the gaze-rotation signals, which is an
influence we wish to consider.
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Figure 4C illustrates the general case of two tuning curves related by a horizontal shift and a vertical gain. We adopted the following approach to characterize such a relation: 1) calculate the optimal horizontal shift between the two curves by cross-correlation, which is insensitive to vertical gain and offset; 2) subtract the optimal shift thereby horizontally aligning the curves; and 3) calculate the gain over the range of tuning curve overlap. The effect of a given gaze-rotation condition thereby is characterized by two scalars, optimal shift and gain, and relates the gaze-rotation tuning curve to the fixed-gaze tuning curve.
Although cross-correlation has advantages and disadvantages
compared with other methods, we adopted this method because the advantages are well suited for the specific questions we ask. Cross-correlation reduces to correlation at a given horizontal shift,
as expressed
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(2) |
Cross-correlation is well suited to our analysis approach for several reasons. First, it is quite sensitive to the tracking, or alignment, of two tuning curves regardless of the exact functional form of the curves. This allows us to avoid curve fitting (e.g., sigmoids or Gaussians), which would be appropriate for only a part of the data given the strict assumptions of parametric methods. Second, cross-correlation is insensitive to vertical shifts between the two tuning curves, which is apparent in the correlation equation by substituting x + k for y, where k is a scalar vertical offset: this case reduces to autocorrelation (independent of k). Finally, cross-correlation is insensitive to vertical, multiplicative gains between two tuning curves and is apparent in the correlation equation by substituting gx for y, where g is a multiplicative gain: this case also reduces to autocorrelation (independent of g).
However, we also must contend with two limitations. First, we
restricted the total range of shifts tested so as to avoid situations where fewer than five data points overlap. We found that less than five
overlapping points results in erroneously high correlation values due
to alignment of the edges of the tuning curves not to the alignment of
the prominent features of the tuning curves that we sought. This shift
range is 8 to +56°, where 0° corresponds to retinal coordinates
and 24° corresponds to screen coordinates. Screen coordinates, or
equivalently room coordinates, is an operational term and alignment in
screen coordinates indicates complete compensation for gaze rotation
(Bradley et al. 1996
). Alignment in retinal coordinates
indicates no compensation for the visual effects of gaze rotation.
Second, if the prominent features (e.g., minima, maxima, inflection
points) of two tuning curves are aligned, but the shape of one or both
of the two tuning curves is altered slightly, this can lead to
misestimations of the alignment of the prominent features. However,
this misestimation grows as the extent of the shape change increases,
which means that the misestimation is small for small shape changes.
Alternate methods also suffer from such misestimations, but
cross-correlation deviates from what we consider proper alignment of
prominent features in a graded manner.
We began the cross-correlation analysis by smoothing the tuning curves with a three-point moving average (twice; uniform weights) followed by a spline interpolation (1° sampling). Although all results are qualitatively similar without smoothing and interpolation, such methods provide reasonable intersample interpolation and reduce anomalous cross-correlogram peaks at the edges of the correlation range. Cross-correlation results also remained qualitatively similar for tuning curves formed by integrating activity as brief as 200 ms, centered on the midpoint of the data collection interval.
Nonparametric tests were used to analyze cross-correlation population data. Wilcoxon t-tests were used to determine the significance of optimal shifts in the various gaze tracking tasks across the population. Mann-Whitney t-tests were used to test if distribution means are different. Spearman correlation tests were used to determine if a significant correlation exists between optimal shifts in different gaze tracking conditions across the population. Nonparametric tests also were used to evaluate gain data.
Gaze tracking conditions along diagonal axes result in tuning curves
with focus spacings of 8 ° instead of 8°. However, the
theoretical shift along these axes also is expanded to
24
°. Because the focus spacing, theoretical shift, and
tracking speeds all scale proportionally, the resulting shifts are
readily remapped (i.e., divide shift by
) for inclusion with
the rest of the population data.
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RESULTS |
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We recorded and analyzed data from 80 neurons in two monkeys, 56/80 from monkey FTZ and 24/80 from monkey DAL, in the preferred-optic-flow, preferred direction, and heading experiments.
Optic-flow tuning
We measured the response of MSTd neurons to expansion (EX), contraction (CO), clockwise rotation (CW), and counterclockwise rotation (CCW) optic-flow stimuli presented at six locations. We found most cells to be tuned significantly for the pattern of optic flow in at least one of the stimulus locations (54/80, 67.5%, P < 0.05/6, Kruskal-Wallis). Although additional trial replicates likely would increase the number of significantly tuned cells, we were able to identify clear tuning trends in all neurons. We conserved trials in this experiment, and in the preferred-direction experiment, because the heading experiment (the core experiment) required many long trials.
We noted the stimulus location eliciting the strongest tuning, and we refer to the preferred-optic-flow pattern at this location as the preferred-optic-flow pattern of the cell. The distribution of preferred-optic-flow patterns in the population is as follows: 43/80 (54%) EX, 24/80 (30%) CO, 9/80 (11%) CW, and 4/80 (5%) CCW. We also noted the preferred-optic-flow pattern at the location centered on fixation, which was typically the same as the cell's preferred-optic-flow pattern, for use in the preferred-direction experiment.
Although large optic-flow patterns elicit the strongest neural
responses, we were restricted to 18 × 18° visual stimuli.
However, we routinely found strong, tuned responses that were modulated by the position of the stimulus within the receptive field. These characteristics are indicative of well-activated MSTd neurons. We also
found similar proportions of cells selective for expansion, contraction, and rotation (54, 30, and 16%, respectively) as compared with our previous study in a different monkey (41, 33, and 27%, respectively) which used 50 × 50° stimuli (Bradley et
al. 1996). These similarities indicate that MSTd response
characteristics persist as stimulus size decreases and are consistent
with a previous report (Graziano et al. 1994
).
Pursuit and VORC tuning
We then recorded neural activity during the preferred-direction
experiment and analyzed the responses to determine the
preferred-pursuit and preferred-VORC directions. Figure
5A shows the basic result that
individual MSTd neurons are tuned for the direction of both pursuit and
VORC. The response of this neuron clearly is enhanced during rightward
(ipsilateral) pursuit and VORC and clearly is suppressed during
leftward (contralateral) pursuit and VORC. The estimated preferred
pursuit and VORC directions are 0.7 and 344.8° (15.2°),
respectively (angle of the response-weighted vector sum). Angles are
measured counter clockwise from the ipsilateral direction. The
preferred gaze-rotation directions are well aligned in this cell
(15.9° difference), considering that the possible range of direction
differences is 180°. The directions opposite the preferred directions
are designated the null directions and also were noted for use in the
heading experiment.
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To visualize the degree of directional tuning in the population, Fig. 5B plots tuning curves averaged across the population. Pursuit and VORC tuning curves from each neuron were rotated (independently) to align preferred directions at 0°. The curves were normalized (separately) to the preferred-direction responses before averaging across the population. Mean pursuit and VORC tuning curve shapes are quite similar, indicating similar directional selectivities in the population. Pursuit has a slightly sharper mean tuning curve, but both pursuit and VORC have preferred:null response ratios of ~2:1 and both have tuning bandwidths (full width at half-maximum) of ~90°.
Does this similarity between pursuit and VORC tuning, seen in the population, arise from an equivalence at the single-cell level? To answer this question, we examined the distribution of preferred directions, the cell-by-cell difference in preferred directions and the cell-by-cell preferred-direction correlation, the population distribution of directional selectivity indices, and the cell-by-cell correlation of these indices.
Figure 6A shows population
histograms of the pursuit and VORC preferred directions. Both
distributions appear to favor some directions over others. The
downward-ipsilateral direction is favored significantly for pursuit
(P < 0.01, Rayleigh) and although VORC failed to reach
significance (P = 0.31, Rayleigh), there appears to be
a similar trend. Figure 6B is a population histogram of the
cell-by-cell difference between the VORC and pursuit preferred directions. A single, strong peak occurs at ~0° (7.7 ± 80.1°, mean ± SD) and is significant (P < 0.001, Rayleigh). The preferred directions also are correlated
significantly on a cell-by-cell basis (raa = 0.08, P < 0.01, nonparametric angular-angular
correlation). These findings indicate that individual cells tend to
have well-aligned pursuit and VORC preferred directions.
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We quantified the pursuit and VORC directional tuning in each cell with a selectivity index (SI). Figure 6C plots population histograms of the SIs. Both populations are quite selective with pursuit slightly more selective on average. We found that 20/80 (25%) cells have significant pursuit tuning and 16/80 (20%) cells have significant VORC tuning (P < 0.05, boot-strap analysis of SIs). As with optic-flow pattern tuning, these percentages are likely underestimates due to the relatively low number of repetitions. Nevertheless, as Fig. 5B shows quite directly, there is clear evidence that pursuit and VORC signals are well tuned and we were able to identify clear tuning trends in all neurons.
Figure 6D plots VORC SIs versus pursuit SIs on a cell-by-cell basis. We found a significant correlation in these data (P < 0.001, rs = 0.58, Spearman) and the best fit line has a slope of 0.89 (2-dimensional least mean squares fit). This indicates that pursuit and VORC directional selectivity is related, and is nearly equal, in individual neurons.
With evidence that pursuit and VORC gaze-tracking signals are similarly tuned in individual neurons, we now examine how these gaze-tracking signals interact with visual-motion patterns simulating translation through the world (i.e., heading experiment).
Focus tuning
The heading experiment consists of fixed gaze, pursuit, VORC, and simulated gaze-rotation conditions conducted along the cell's preferred-null axis. Figure 7 presents all neural and behavioral data collected from a single MSTd neuron in the heading experiment. The seven rows represent the seven behavioral/directional combinations while the nine columns represent the FOE positions on the screen. Comparisons of the relative alignment and magnitude of the various behavioral/directional tuning curves are presented in the following two sections, SHIFT COMPENSATION and GAIN MODULATION.
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The fixed-gaze response (Fig. 7, middle) is a typical
example of how the neural response changes as the focus position is varied along the cell's preferred-null gaze-rotation axis (Fig. 7,
schematic illustrations). The preferred-optic-flow pattern for this
cell is expansion and the preferred gaze-rotation direction is
rightward. The fixed-gaze response is tuned for the FOE position (P < 0.001, Kruskal-Wallis), responding vigorously to
leftward FOE positions but hardly at all to rightward FOE positions.
Although this neuron was classified as expansion selective, because it responded better to expansion patterns than to contraction or rotation
patterns, it also responds to nearly laminar motion. This is evident in
the fixed-gaze response because the 32° FOE stimulus (nearly
rightward laminar flow) elicits a strong response. That FOE tuning
arises from the combination of expansion (or contraction or rotation)
and laminar motion selectivities is not surprising. Moreover by varying
the FOE positions along the preferred-null gaze-rotation axis, we
expect to couple into the neuron's laminar flow response because it
has been reported that most MSTd neurons have laminar motion
selectivities that align with the preferred-null pursuit axis
(Komatsu and Wurtz 1988b
).
The fixed-gaze response appears to reliably encode the FOE position, but does it represent the FOE position on the retinae or the FOE position on the screen (in the world)? Retinal and screen coordinates are identical when the gaze is fixed. To dissociate retinal FOE position tuning from true heading tuning, which requires the cell to encode the FOE position on the screen, we must consider the neuron's focus tuning while gaze is rotating because the retinal focus is shifted then relative to the screen (see Fig. 1). Constant-velocity pursuit, VORC, and simulated gaze rotations introduce a constant FOE position difference, or displacement, between the retinal and screen images. Cell-by-cell comparisons of pursuit, VORC, and simulated gaze-rotation focus tuning curves with the fixed gaze focus tuning curve are discussed below.
To verify that MSTd neurons are sensitive to the dimension of visual motion that we varied, we tested the significance of FOE position tuning for each behavioral/directional tuning curve. The number of neurons in the population with significant tuning is as follows: 53/80 (66%) fixed gaze; 34/80 (43%) preferred-direction VORC; 40/80 (50%) null-direction VORC; 45/80 (56%) preferred-direction pursuit; 34/80 (43%) null-direction pursuit; 45/80 (56%) preferred-direction simulated gaze rotation; and 45/80 (56%) null-direction simulated gaze rotation (P < 0.05, Kruskal-Wallis). We expected to find fewer significantly tuned cells in the gaze-rotation conditions than in the fixed gaze condition because gaze rotation adds laminar flow to the retinal image, thereby causing more stimuli to have essentially laminar retinal flow. Neurons respond more similarly to visual patterns with only slight variations from laminar flow than to visual patterns that span both directions of laminar flow and, for example, expansion (see Fig. 7). Few cells are tuned significantly for focus position in all seven behavioral/directional conditions (12/80, 15%, P < 0.05, Kruskal-Wallis), but most cells are for at least one of the seven conditions (59/80, 74%, P < 0.05/7, Kruskal-Wallis).
Influence of gaze rotation
SHIFT COMPENSATION. Figure 8 plots tuning curves constructed from the heading experiment data presented in Fig. 7. Rows represent the three gaze-tracking conditions, which are retinally identical except for the visual effects caused by extremely small eye-movement deviations during fixation, pursuit, and VORC. The curves are plotted in both the coordinates of the screen and the retinae to help visualize the alignment of features.
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GAIN MODULATION. We now ask if MSTd neurons also use gaze-rotation signals, whether retinal, pursuit, or VORC in origin, to gain modulate focus tuning curves. Figure 11 plots focus tuning curves from the heading experiment in the same format as Fig. 8 but with data from a different cell. Comparing these two figures reveals very different alignment patterns. These tuning curve maxima, minima, and inflection points align better in retinal coordinates, than in screen coordinates, for all conditions. "Retinal," or nearly retinal, neurons are characterized by this type of alignment pattern. Figure 11 also shows that VORC and pursuit cues appear to modulate the firing rate. Preferred-direction tuning curves are enhanced while null-direction tuning curves are suppressed with respect to the fixed gaze tuning curve. The simulated gaze rotation condition exhibits less gain modulation.
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SHIFT COMPENSATION AND GAIN MODULATION.
So far the gain modulation analysis has considered all cells in the
population and did not partition the cells according to their optimal
horizontal shifts. However, the functional role of gain perhaps is
understood most readily for retinal cells (cells without horizontal
shift) where pursuit and VORC cues are combined with retinotopic
information. We are now in a position to consider each cell's
horizontal shift and gain modulation. Figure
14 plots gain modulation versus optimal
shift on a cell-by-cell basis for both gaze-rotation directions and in
each of the three gaze-rotation conditions. For comparative purposes,
we consider cells with compensatory shifts of 8, 0, and +8° to be
in or near retinal coordinates and cells with 16, 24, or 32° shifts
to be in or near screen coordinates. Note that while compensatory
shifts were determined to within a 1° resolution, we returned to the
experimental 8° resolution, set by the FOE spacing, to calculate gain
by shifting tuning curves integral multiples of the FOE spacing.
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DISCUSSION |
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Cortical area MSTd has been implicated as a visual navigation
center for the past decade. Many studies have shown that neurons in
this area are sensitive to the visual-motion patterns present during
locomotion, and we recently demonstrated that MSTd neurons use a
pursuit signal to effectively subtract visual-motion artifacts introduced by moving the eyes in the orbits (Bradley et al.
1996). This study asked if MSTd also can contend with
visual-motion distortions caused by passive head movements because we
typically shift our gaze with head rotations as well as eye rotations.
As predicted for a visual navigation center, we found that MSTd
receives a signal of vestibular origin (VORC signal) and uses this
signal to compensate, at least partly, for the rotational flow added by
rotating the head in the world. This is accomplished by adjusting a
neuron's focus tuning curve so that it becomes maximally sensitive to
the sum of the preferred motion pattern in the world and the rotational
flow added by the gaze rotation. In this way, many neurons remain
maximally sensitive to heading. It appears that considerable
compensation also can be driven by retinal cues alone.
We also found that most MSTd neurons receive both pursuit and VORC
signals and that these signals are similar in a given cell. Moreover,
many neurons use these signals to similarly compensate for eye and head
rotations. However, not all MSTd neurons adjust their focus tuning so
as to become invariant to gaze rotations. Although most cells do
compensate at least partly, those that remain in retinal coordinates
are substantially gain-modulated by both pursuit and VORC signals.
These cells could converge to form the inputs to heading cells as we
have suggested previously (Bradley et al. 1996) or could
form a distributed representation of heading as other PPC neurons do or
both. Area MSTd receives an exquisite convergence of retinal and
extraretinal signals that enable neurons to encode heading even during
eye and head rotations. These results suggest strongly that MSTd plays
a central role in heading computation and perception in primates.
Comparison of pursuit and VORC signals
The preferred-direction experiment explored the influence of
pursuit and VORC signals, or cues, on visual responses. However, we
cannot be sure of the exact nature of the pursuit and VORC signals. A
pure pursuit signal may reflect an eye-muscle proprioceptive signal or,
more likely, an efference copy of an eye movement command and such a
signal is known to exist in MSTd (Newsome et al. 1988). A pure VORC signal, as discussed in the INTRODUCTION, may
be a vestibular signal, an eye-movement signal, or more likely a
combination of both. The origin of these signals is extraretinal. In
the preferred-direction experiment, we moved the preferred-optic-flow
stimulus with the pursuit and VORC targets, and we measured the
modulatory effects of the extraretinal signals on the cell's visual
response. The presence of the optic-flow pattern raises the possibility
that less than perfect pursuit and VORC could contribute a sizable retinal slip signal to the measured response, which would otherwise consist of just the optic-flow visual response and the extraretinal signal. Although we confirmed that the monkeys pursued and canceled the
VOR quite accurately, we cannot rule out this possibility completely.
Another observation also suggests that we selected an axis close to the
true, extraretinally based preferred-null axis. Just before the
optic-flow stimulus appeared, the monkey pursued or VORC-tracked a
single point in an otherwise dark room. This response tended to be
greater in preferred-direction pursuit and VORC than in the null
direction (compare prestimulus periods in Fig. 7).
Pursuit and VORC signals tended to be tuned for ipsilateral and
downward gaze rotation (Fig. 6A), although only the pursuit trend is significant. Downward gaze rotation is intriguing because it
is consistent with a role in visual navigation, where tracking objects
on the ground while moving forward often leads to downward gaze
rotations. This bias is reminiscent of the overrepresentation of
expansion cells in MSTd; this presumably is related to our habit of
looking along our heading path, which causes expanding retinal flow
(Graziano et al. 1994).
Individual MSTd neurons also tend to be selective for a given direction
of gaze rotation regardless of whether gaze rotation is produced by eye
or passive-head rotation (Fig. 6B). This suggests that MSTd
may encode the direction of total gaze rotation. If VORC signals also
are tuned for the speed of gaze rotation, as is the case for pursuit
signals, then it is possible that total gaze velocity also is encoded
in MSTd (Kawano et al. 1980, 1984
; Shenoy et al.
1998
). It is also possible that other important extraretinal
signals are present in MSTd. Natural head turns, in contrast with the
VORC paradigm that rotates the entire animal, are accompanied by
proprioceptive signals from neck muscles and joints and efference copy
of head-turn motor commands. Although the VORC signal alone, which
derives from the vestibular canals, clearly encodes passive head
movements, it is possible that proprioceptive and efference copy
signals serve additional roles in reporting the metrics of active head movements.
Shift compensation
Many MSTd neurons are able to maintain their selectivity for visual-motion patterns in the world despite large retinal image changes caused by gaze rotations (Fig. 8). This is achieved by shifting their retinal-focus position sensitivity. Such dynamic adaptation is critical for seeing motion in the world independent of ego motion, and this perceptual constancy allows us to navigate through our environment while moving our eyes and head. But how is this dynamic adaptation achieved?
There appear to be at least two possibilities. First, for a neuron to
remain tuned for a central FOE in the world during rightward gaze
rotation, the retinal tuning must become more responsive to rightward
foci and less responsive to central foci (Fig. 1). This dynamic
adaptation could occur in the projections from area MT, which extracts
unidirectional motion within small receptive fields, to area MSTd.
Dynamically strengthening connections from some MT cells, while
weakening connections from others, effectively changes the preferred
visual-motion pattern of the afferent MSTd neuron. It is plausible that
extraretinal gaze-rotation signals selectively strengthen and weaken
these connections in a pattern appropriate for the current gaze
rotation. As recognized by Perrone and Stone (1994,
1998
), incorporating this type of dynamic adaptation into a
template model of MSTd should reduce drastically the total number of
neurons needed to accurately encode heading during gaze rotations.
A second possibility is that dynamic adaptation occurs within MSTd. In
this scheme, MT-to-MSTd connectivity remains constant, and each
compensating (heading) MSTd neuron receives input from at least two
noncompensating (retinal) MSTd neurons as we recently proposed
(Bradley et al. 1996). In this architecture, a heading cell sums the output of retinal cells, the preferred focus positions of
which are mutually offset. Extraretinal gaze-rotation signals selectively enhance (gain modulate up) and suppress (gain modulate down) the output of retinal cells in a pattern appropriate for the
current gaze rotation. For example, enhancing retinal cells tuned for
rightward foci and suppressing retinal cells tuned for central foci
effectively shifts the heading cell's focus tuning rightward as needed
during rightward gaze rotation. Although our data certainly do not rule
out the "MT-to-MSTd" mechanism, it does appear consistent with
the "within-MSTd" mechanism because two important, predicted MSTd
response features were observed: the existence of compensating (Fig. 8)
and noncompensating (Fig. 11) neurons and the existence of
gain-modulated retinal cells (Fig. 14, A and C).
The heading experiment also demonstrates that neurons can use retinal
cues, not just pursuit and VORC cues, to compensate at least partly. We
did not find this to be the case in our previous heading study, which
used large optic-flow stimuli and a higher pursuit speed
(Bradley et al. 1996). There are at least three possible
reasons for this discrepancy. First, comparing the mean simulated
gaze-rotation compensation for the 18 × 18° stimulus frame
(12.47°/24° = 52.0%; Fig. 9B) with the mean
compensation for a 50 × 50° stimulus frame (
1°/30° =
3.3%) (Bradley et al. 1996
) suggests that
retinal-based compensation may increase as the stimulus frame becomes
more visible to the receptive field. In other words, the stimulus edges
in our experiments are well within the receptive field and could
provide an accurate cue as to the direction and speed of gaze rotation.
Larger stimuli provide less of a cue because the edges fall on the
lower sensitivity periphery of the receptive field. Second, it is
possible that the faster rotation rate used in the previous experiment
(15.7 vs. 9.2°/s) led to lower levels of compensation because the
faster visual motion was beyond the neurons' optimal speed range,
though this seems unlikely given the broad speed tuning in MSTd.
Finally, Komatsu and Wurtz (1988b) found that the
preferred direction of visual motion reverses as the size of the
stimulus field increases. Moreover, the size of the stimulus field
leading to reversal of the preferred direction increases as the speed of visual-motion increases. Last, the vast majority of cells have preferred directions of motion for large stimulus fields opposite to
the preferred-pursuit directions. Taken together, it is possible that
compensation is greater in the present experiment because the choice of
rotation rate and visual-stimulus size caused most MSTd neurons to have
their preferred-motion direction aligned opposite to the preferred
gaze-rotation direction, which is necessary for retinal and
pursuit/VORC cues to constructively combine and drive compensation,
whereas the previous experiment (Bradley et al. 1996
)
operated in the opposite regime. Preliminary data from 30 neurons in
monkey DAL indicate that most MSTd cells do have oppositely
aligned preferred visual-motion directions and preferred-pursuit directions with stimulus size and rotation rates similar to those used
in the current experiments (K. V. Shenoy, J. A. Crowell, and
R. A. Andersen, unpublished observation).
In addition to the strong compensatory shifts in the population, many individual MSTd neurons have similar degrees of VORC- and pursuit-induced compensation. A subset of neurons also have similar preferred- and null-direction shifts and therefore report heading regardless of the type of gaze rotation and regardless of the direction of rotation (Fig. 10B, tie lines). However, not all neurons have well-correlated shifts, and the space of VORC compensation versus pursuit compensation can be sectioned roughly into four groups as shown in Fig. 10B. It is possible that either the entire MSTd population is read out by down stream cortical areas or that subpopulations serve different roles in various behavioral conditions. Human psychophysical studies may help interpret the role of MSTd and its subpopulations in visual navigation (see Relationship among physiology, psychophysics, and models).
Gain modulation
In the population, preferred-direction pursuit and VORC
significantly modulate visual responses. These results bear close resemblance to gain modulation in PPC caused by gaze position signals
(Andersen and Mountcastle 1983; Andersen et al.
1985
, 1990
; Brotchie et al. 1995
; Snyder
et al. 1998
). It is possible that the brain uses a common
mechanism (gain modulation) for combining similar types of information.
Figure 15 illustrates two systems, with
a common gain mechanism, for combining gaze-rotation and visual-motion
information (gaze-rotation gain field system) and for combining
gaze-position and visual-position information (gaze-position gain field
system). Figure 15, top, illustrates the analogy between the
addition of eye and head rotation information to retinal motion
information to encode motion in the world (A) and the
addition of eye and head position information to retinal position
information to encode position in the world (B). The signals
in the two systems are related simply by a single temporal derivative.
After the retinal effects of gaze rotation have been taken into
account, the absolute position of the focus of expansion, which can be
thought of as a visual object, must be encoded. The open arrow passing
information from the motion system to the position system in Fig. 15
suggests such a processing hierarchy (e.g., MSTd projects to areas 7A
and VIP, which encode target locations). It is also possible that MSTd
simultaneously encodes the position of motion patterns in the world by
employing eye position signals, which have recently been reported, and
head position signals, which also may exist (Bremmer et al.
1997; Squatrito and Maioli 1996
).
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Figure 15, bottom, illustrates the common gain mechanism by
which the motion system combines retinal motion signals with gaze rotation signals and by which the position system combines retinal position signals and gaze position signals. In the motion system (A), a retinal focus tuning curve is modulated by a function
of eye or head rotation. The pursuit and VORC gain data are the mean gains from retinal cells (see Table 2) and have slopes of 5.10 and
3.49%/(°/s), respectively, in the preferred direction. Whereas gain
appears to be a half-wave rectified version of the rotation rate, data
from intermediate rotation speeds are required to determine the true
gain curve. The position system (B) has been well
characterized and retinotopic visual responses are modulated by
monotonic functions of eye or head position. Typical gain slopes are
3-5%/° and enhance as well as suppress activity relative to the
levels present in the forward gaze position (Brotchie et al.
1995; Snyder et al. 1998
). Gain fields enable a
population of neurons to encode, in a distributed representation, the
focus of expansion regardless of gaze rotation (A,
gaze-rotation gain field) and absolute position information could
readily be added to this representation by the position system
(B, gaze-position gain field). It is also possible that
individual MSTd heading neurons, which have removed the retinal effects
of gaze rotation, either project to the positional system or also
include gaze-position effects.
Relationship among physiology, psychophysics, and models
Accurate heading perception depends on our ability to
correctly interpret visual motion even while rotating our gaze. A
recent modeling study (Lappe 1998) successfully
reproduced human psychophysical and monkey neurophysiological data
(Bradley et al. 1996
) during pursuit by incorporating an
extraretinal pursuit signal. Based on results from this model,
Lappe (1998)
proposed that extraretinal compensation for
eye movements does not need to be perfect in single neurons to estimate
heading accurately. With the present findings that VORC compensation is
nearly as complete as pursuit compensation and that individual cells
compensate for both types of gaze rotation, we would expect this model
to easily extend to include head turns.
Similar results also should be possible with template models if
extraretinal signals are allowed to dynamically adapt motion templates
(Perrone and Stone 1994, 1998
). Finally a model that gain-modulates retinotopic visual responses with pursuit velocity signals, similar to models of the PPC position system, successfully has
described a distributed representation of heading (Beintema and
van den Berg 1998
; Bradley et al. 1996
;
van den Berg and Beintema 1997
; Zipser and
Andersen 1988
).
New considerations are how MSTd encodes heading during eye and/or head
rotations and how this representation is read out. This study and
recent human psychophysical investigations suggest that MSTd plays a
central role in heading perception but that MSTd may not be the final
perceptual locus. Humans correctly report their self-motion during
simulated translations over ground planes during fixation, pursuit, and
active head pursuit, which uses a VORC paradigm (Crowell et al.
1998a; Royden et al. 1992
). Although a VORC
signal is present during active head pursuit, and potentially contributes to heading judgments, humans make large heading errors when
only a VORC signal is present, measured in a passive VORC condition
that matches our physiology paradigm (Crowell et al. 1998a
,b using wall displays). Performance also is reduced when the VORC signal is absent during active head pursuit, measured by
passively rotating the body but actively counterrotating the head so as
to keep it stationary in space (mean compensation of 68%)
(Crowell et al. 1998a
). Together these results imply
that the VORC signal is necessary but not sufficient for accurate
heading estimates.
These psychophysical experiments indicate that while heading perception
is nearly perfect during pursuit, perceptual accuracy is significantly
reduce during passive VORC (Crowell et al. 1998a). One
hypothesis is that the lower level of gain modulation observed during
passive VORC, as compared with the level of gain modulation during
pursuit, reflects this reduced perceptual accuracy. Additional gain
from proprioceptive and efference signals could raise the level of gain
modulation and thereby account for the excellent performance during
active head pursuit.
Another, and possibly more likely, hypothesis is that even though shift compensation is similar in individual neurons during pursuit and passive VORC conditions, MSTd subpopulations (Fig. 10B) may be selectively read out and thereby account for psychophysical performance. One possibility is that only neurons that compensate exclusively during pursuit (region d) contribute to perception. This seems unlikely because there are few neurons in this category (10%) and because there is a similar fraction of neurons that compensate only during passive VORC (region c), which have no known perceptual role. A more likely possibility is that a downstream area selects which MSTd subpopulation to read out based on a more complete set of extraretinal signals (e.g., proprioceptive and efference signals). For example, although VORC and pursuit signals create the entire representation shown in Fig. 10B, the compensating subpopulation (region b) may be read out only during eye pursuit and active head turns, whereas the noncompensating subpopulation (region a) may be read out during passive head turns. This suggests that the brain may have adopted the strategy of using a small, possibly minimal, number of gaze rotation signals (e.g., pursuit and VORC) to create a neural representation but a larger, possibly complete, set of extraretinal signals to selectively read out, or interpret, the representation.
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ACKNOWLEDGMENTS |
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We thank Drs. K. L. Grieve, L. H. Snyder, J. A. Crowell, and M. S. Banks for scientific discussions; Dr. K. L. Grieve and B. Gillikin for technical assistance; Drs. L. H. Snyder and K. L. Grieve for designing and helping test the vestibular chair; M. Sahani for developing the real-time control software HYDRA; Drs. J. A. Crowell, L. H. Snyder, and Y. E. Cohen for valuable comments on this manuscript; and C. Reyes for administrative assistance.
This work was supported in part by National Eye Institute (NEI) Grant EY-07492, NEI postdoctoral grant EY-06752 to K. V. Shenoy, the Sloan Foundation for Theoretical Neurobiology at the California Institute of Technology, the Office of Naval Research, and the Human Frontiers Scientific Program.
Present address of D. C. Bradley: Dept. of Psychology, University of Chicago, Chicago, IL 60637.
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FOOTNOTES |
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Address for reprint requests: R. A. Andersen, Mail Code 216-76, Division of Biology, California Institute of Technology, Pasadena, CA 91125.
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 15 June 1998; accepted in final form 22 January 1999.
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REFERENCES |
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