Download Neural Activity in Dorsolateral Pontine Nucleus of Alert Monkey

JOURNAL
OF NEUROPIWSIOL~GY
Vol. 67, No. 3, March
1992.
Printed in U.S.A.
Neural Activity in Dorsolateral Pontine Nucleus of Alert Monkey
During Ocular Following Responses
K. KAWANO,
M. SHIDARA,
AND S. YAMANE
Neuroscience Section, Electrotechnical Laboratory, Tsukubashi, Ibaraki 305, Japan
SUMMARY
AND
CONCLUSIONS
1. Movements of the visual scene evoke short-latency ocular
following responses. To study the neural mediation of the ocular
following responses, we investigated neurons in the dorsolateral
pontine nucleus (DLPN) of behaving monkeys. The neurons discharged during brief, sudden movements of a large-field visual
stimulus, eliciting ocular following. Most of them ( 1OO/ 112) responded to movements of a large-field visual stimulus with directional selectivity.
2. Response amplitude was measured in two components of
the neural response: an initial transient component and a late
sustained component. Most direction-selective DLPN neurons
showed their strongest responses at high stimulus speeds (80160” /s), whether their response components were initial (63 / 87,
72%) or sustained (63/87, 72%). The average firing rates of 87
DLPN neurons increased as a linear function of the logarithm of
stimulus speed up to 40” /s for both initial and sustained responses.
3. Not only the magnitude but also the latency of the neural
and ocular responses were dependent on stimulus speed. The latencies of both neural and ocular responses were inversely related
to the stimulus speed. As a result, the time difference between the
response latencies for neural and ocular responses did not vary
much with changes of stimulus speed.
4. Response latency was measured when a large-field random
dot pattern was moved in the preferred direction and at the preferred speed of each neuron. Seventy-three percent (56/77) of the
neurons were activated ~50 ms after the onset of the stimulus
motion. In most cases (67/77, 87%), their increase of firing rate
started before the eye movements, and 34% of them (26/77)
started > 10 ms before the eye movements.
5. Blurring of the random dot pattern by interposing a sheet of
ground glass increased the latency of both neural responses and
eye movements. On the other hand, the blurred images did not
change the timing of the effect of blanking the visual scene on the
responses of the neurons or eye movements.
6. When a check pattern was used instead of random dots, both
neural and ocular responses began to decrease rapidly when the
temporal frequency of the visual stimulus exceeded 20 Hz. When
the temporal frequency of the visual stimulus approached 40 Hz,
the neurons showed a distinctive burst-and-pause firing pattern.
The eye movements recorded at the same time showed signs of
oscillation, and their temporal patterns were closely correlated to
those of the firing rate.
7. The results, that most DLPN neurons changed their activities before eye movements and that their dependence on visual
properties of the stimulus was similar to that of ocular responses,
suggest that they may play a role in the mediation of visual information eliciting ocular following.
8. Responses during smooth pursuit eye movement were studied in 41 neurons, which responded to a moving large-field visual
stimulus that elicited ocular following. Twenty-eight ( 68%) of
them were activated during smooth pursuit in the dark. Latencies
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0022-3077192
were long in neurons with opposite directional preferences in
smooth pursuit and ocular following (> 120 ms) but short in neurons with the same directional preferences in both situations (6090 ms) . For the latter, the latency of the response to the target spot
was always longer than that of the response to the large-field visual
stimulus, and the neural response latency was shorter than the
ocular response latency. They may play a role in both ocular following and smooth pursuit.
INTRODUCTION
Movements of the visual scene evoke tracking movements of the eyes, termed “ocular following” (Miles et al.
1986). Experiments using monkeys have revealed many
interesting features of these responses (Kawano and Miles
1986; Miles and Kawano, 1986, 1987; Miles et al. 1986).
The most interesting feature is that the ocular following
responses commonly have latencies as short as 50 ms. Considering the delays introduced in the retina and ocular motor plant, the intervening neural elements must be limited
in number, suggesting that this system may be amenable to
characterization at all stages from sensory input to motor
output. Although previous studies have suggested that cortical (Dtirsteler and Wurtz 1988; Diirsteler et al. 1987; Erickson and Dow 1989; Kawano and Sasaki 198 1, 1984;
Kawano et al. 1984; Komatsu and Wurtz 1988a, 1989;
Newsome et al. 1985, 1988; Sakata et al. 1983), pontine
(May et al. 1988; Mustari et al. 1988; Suzuki and Keller
1984; Suzuki et al., 1990; Thier et al., 1988), and cerebellar
(Btittner and Waespe 1984; Kase et al. 1979; Lisberger and
Fuchs 1978; Miles and Fuller 1975; Miles et al. 1980; Noda
and Suzuki 1979a,b; Noda and Warabi 1986, 1987; Stone
and Lisberger 1990; Suzuki and Keller 1988; Suzuki et al.
1981; Waespe et al. 1981, 1985; Zee et al. 1981) structures
are involved in the genesis of other slow tracking eye movements (i.e., smooth pursuit eye movements and optokinetic responses), nothing is known about the neural correlates of ocular following responses. As a first step toward
understanding the neural pathway that mediates these responses, the present paper reports experiments on the dorsolateral pontine nucleus (DLPN) .
Recent single unit (Mustari et al. 1988; Suzuki and
Keller 1984; Suzuki et al. 1990; Thier et al. 1988) and chemical lesion studies (May et al. 1988 ) suggest a major role for
the DLPN in the mediation of smooth pursuit eye movements and initial optokinetic responses (OKR). Although
a close correlation between ocular following responses and
initial OKR has been suggested (e.g., Miles et al. 1986)) in
their lesion study May et al. ( 1988) failed to elicit short-la-
$2.00 Copyright 0 1992 The American
Physiological
Society
DLPN
AND
OCULAR
tency ocular following responses, even when they could observe the initial OKR. Thus the role of the DLPN in the
generation of the ocular following responses remains unknown.
To elicit ocular following responses, we adopted a similar
experimental situation to that of Miles et al. ( 1986). First,
we used unexpected ramp movements of large-field visual
stimuli instead of illuminating a rotating striped drum,
which is the commonly used optokinetic stimulation. Second, the screen on which the visual pattern was back-projected was set close to the animal, because it has been reported that the amplitude of ocular following responses is
closely correlated to the viewing distance and that vigorous
responses are evoked by movements of nearer scenes
(Schwartz et al. 1989). Finally, we used Macaca fuscata,
which is known to have better initial OKR than Macaca
fascicularis (Buttner et al. 1983; Kato et al. 1986, 1988).
With this approach, we were able to elicit short-latency ocular following responses, confirming the experiments of
Miles et al. ( 1986), and to undertake further experiments
on the DLPN.
Preliminary results have been presented elsewhere (Kawano et al. 1989, 1990a,b, unpublished observations).
METHODS
Animal preparation
Data were collected from three adolescent monkeys (1M. fuscata), weighing 5-9 kg. All animals had been previously trained to
fixate a small target spot to obtain a fluid reward (Wurtz 1969).
Under Nembutal (pentobarbital sodium) anesthesia and aseptic
conditions, each monkey was implanted with a cylinder for microelectrode recording and fitted with a head holder that allowed the
head to be fixed in the standard stereotaxic position during the
experiments. Scleral search coils for measuring eye movements
were implanted with the use of the technique of Judge et al.
( 1980). Electrophysiological recording sessions generally began 1
wk after surgery.
Behavioral paradigms and visual stimuli
The behavioral paradigms and visual stimuli used in the present
study are similar to those of Miles et al. ( 1986). Animals faced a
translucent, tangent screen of white paper on which moving patterns could be backprojected. The screen was 235 mm in front of
the eyes and subtended 85 Oalong the vertical and horizontal meridia. The visual patterns were projected via an x-y galvanometer
mirror system under negative feedback control. Ramp inputs to
the galvanometer controllers were provided by analog hardware
under computer control (NEC PC980 1). Two paradigms were
employed: one for ocular following and one for smooth pursuit.
Large-field visual stimuli for the former and a target spot for the
latter each had independent galvanometer systems.
In the ocular following paradigm, each ramp started 50 ms after
the end of a saccadic eye movement, except in some casesin which
longer postsaccadic delay intervals of 100-300 ms were used. This
arrangement allowed the collection of data to be free of saccades
for 2150 ms after the onset of the stimulus. The ramp usually
lasted 150 or 300 ms, after which the screen was blanked for 0.5-2
s by a mechanical shutter while the animal sat in the dark. Closure
of the shutter took 4 ms. After the blank period, the shutter opened
again with the pattern at its initial position, ready for the next
ramp. Five speeds ( 10, 20, 40, 80, and 160” /s) and eight (right,
left, up, down, and four diagonal) directions were used as stimulus
FOLLOWING
681
ramps. Some tests involved only a subset of these ramps. To encourage the monkeys to remain alert, they were given an occasional drop of fruit juice for making fast saccades.
In the standard test situation, the projected scene was a photograph of a random dot pattern of Julesz ( 197 1) . The smallest dots
subtended - 15 min of arc, and the luminance ranged from 5.4
cd/m2 in the lighted areas to 0.6 cd/m2 in the dark areas. In some
cases, the image of the random dot pattern was low-pass filtered
(“blurred”)
by interposing a diffusing screen of ground glass
(Miles et al. 1986). The use of the ground glass “filter” reduced
both the spatial frequency and contrast of the visual stimulus. To
get stimuli of limited temporal frequency, we used check patterns
of black and white, of which the spatial frequencies were 0.5 and
0.25 c/O.
In the smooth pursuit paradigm, two small targets (0.8’ diam
each) were used. Each target was projected from a light-emitting
diode with the use of a lens optical system. One of the targets was
directly projected onto the center of the screen. The pursuit target
was projected via the galvanometer mirror system. The step-ramp
paradigm for initiation and maintenance of pursuit was used (Lisberger and Westbrook 1985). Each presentation of the pursuit
target began with the monkey fixating the stationary target on the
center. At a randomized time, the fixation point went out, and the
monkey was required to change fixation and track the pursuit
target, which moved at a constant velocity. The monkey was rewarded for pursuing the target and detecting its dimming.
Visual receptive field mapping was done by two methods. In
one, a visual stimulus (a spot or slits of light) was projected onto
the tangent screen while the monkey looked at the fixation spot.
The size of the spot was 1O, and the slits were 3-20” in length and
l-3’ in width. The other method involved moving the random
dot pattern while the projected area on the screen was restricted
and the eye position was monitored.
Recording technique
At the beginning of each day’s recording session, the monkey
was moved from its cage to a custom-made acrylic chair where its
head was fixed to the chair frame by the implanted head holder. A
hydraulic microdrive (Narishige MO-~) was mounted on the recording cylinder, and glass-coated tungsten microelectrodes were
used for the initial, identification and the mapping of the DLPN
and neighboring structures. Visual responses in the lateral geniculate nucleus and burst-tonic activities related to eye movements in
the oculomotor nucleus were useful guides for finding the DLPN.
In some cases, after finding the DLPN, a stainless steel guide tube
was introduced through the dura and held in place by cementing it
to the side of the recording cylinder under ketamine hydrochloride
anesthesia. The tip of the guide tube was positioned 3-4 mm
above the DLPN. Flexible tungsten electrodes were used to record
through the guide tube.
Selection of neurons
Previous works centered around the DLPN described neurons
that discharged in relation to visual stimuli and eye movements
(Mustari et al. 1988; Suzuki and Keller 1984; Suzuki et al. 1990;
Thier et al. 1988). Our principal interest here was in further characterizing the properties of those neurons that discharge in relation to ocular following responses. To achieve this, we initially
selected neurons for the sensitivity of their discharge to the moving visual scene, which elicited ocular following responses. After
isolating a single unit, we observed its responses to the moving
visual scene at 80” /s in eight directions. Further studies were
carried out only with neurons the activities of which were modulated with any of these stimuli.
682
K. KAWANO,
M. SHIDAFCA,
Data collection and analysis
The presentation of stimuli and the collection, storage, and display of data were controlled by two personal computers (NEC
PC980 1). For monitoring eye movements, an electromagnetic induction technique (Fuchs and Robinson 1966) was used. For lowpass filtering of the DC voltage outputs proportional to horizontal
and vertical eye position, six-pole Bessel filters at 100 Hz were
used. Peak-to-peak noise levels were equivalent to an eye movement of -0.0 lo. The eye position signals were calibrated by having the animal fixate small target lights located at known eccentricities along the horizontal and vertical meridia. Eye position signals were subjected to analog differentiation to provide outputs
proportional to eye velocity with a bandpass of 500 Hz DC. Peakto-peak noise levels were equivalent to an eye movement of 2” /s.
Voltage signals separately encoding the horizontal and vertical
components of eye position, eye velocity, and mirror (stimulus)
velocity were digitized to a resolution of 12 bits, sampling at 500
Hz. Recording of neural activities was carried out with standard
extracellular recording techniques. A time-amplitude window discriminator was used to get spike occurrences with a time resolution of 1 ms. All data were stored on a hard disk, and, after completion of experiments each day, were transferred to a work station
(Apollo Domain) for subsequent analysis.
The methods for analyzing eye movements were the same as
those used by Miles et al. ( 1986). The eye velocity data were
averaged to get a temporal profile and used to estimate the initial
peak eye velocity, which was the maximum eye velocity achieved
by the first wave of eye acceleration. Any DC component due to
drift was subtracted. The average eye velocity profiles were lowpass filtered and differentiated with the use of a digital filter (Miles
et al. 1986) and yielded eye acceleration profiles. The latency of
the ocular following responses was determined from the eye acceleration profiles. The criterion for the onset of a response was an
eye acceleration > 100” / s2. As an index of its total response, we
averaged the firing rate of a neuron over the time interval extending from 40 to 140 ms, measured from stimulus onset. Because we
collected data that were free of saccades for 2 150 ms after the
onset of the stimulus, we could measure the average firing rate of a
neuron without contamination of saccades. As the first step in
selecting the neurons, we calculated the average firing rates of a
neuron in eight directions to evaluate its directional selectivity. In
this particular measurement, we did not remove the neuron’s spontaneous activities. After determining the neuron’s directional selectivity, we further studied its characteristics on various other visual
stimuli. For subsequent analysis, the firing rate of a neuron in the
preresponse period (O-35 ms, measured from stimulus onset) was
averaged to obtain its preresponse activity, and this was subtracted
from its response activity.
In this paper, our main interest is in analyzing quantitatively the
relation between neural responses and ocular responses that were
both elicited by the same stimulus. For this purpose, we first constructed standard peristimulus time histograms of 1 ms binwidth.
These were used to compute a spike density function (MacPherson and Aldridge 1979; Richmond et al. 1987). We used a standard deviation of the Gaussian pulse, u of 3 ms, and the effect of
this was low-pass filtering [the filter cutoff ( -3 dB) was at 47 Hz]
of the histograms without introducing a time delay.
The spike density function was also used to measure the response latency of neurons. To evaluate the variability of the spontaneous activity of a neuron, we calculated the mean and standard
deviation of its spike density during the preresponse period for a
longer duration ( 100 ms duration, between 65 ms before and 35
ms after the stimulus onset). The mean plus 1 SD was used as the
first criterion for the onset of the response. Because the spike density function may spread out and cause underestimation of the
latency measurement, we also used the standard peristimulus his-
AND
S. YAMANE
togram with 1 ms binwidth and defined a second criterion, which
was the mean plus 2 SD of the firing rate during the preresponse
period. The point in time at which both criteria had been met was
taken to be the latency of the neural response. In some neurons,
movements of the visual scene on the retina during saccadic eye
movement and/or postsaccadic drift caused increases in firing
rate. This made firing rate during the preresponse period variable
and accurate measurement of latencies impossible. In these cases,
we used longer postsaccadic delay periods ( 100-300 ms) and separated the response due to saccadic eye movement or postsaccadic
drift from the response to the test ramp. In this way, we avoided
the effects of saccade-related activity during the preresponse period and measured the latencies of these neurons.
To study the low temporal frequency components of both
neural and ocular responses, both responses were low-pass filtered
with the use of a Chebyshev optimal nonrecursive linear digital
filter [ filter length = 65 points, -3 dB at 10 Hz (Rabiner and Gold
1975)].
Histology
At the end of the experiments, monkeys were deeply anesthetized with Nembutal and were perfused through the heart with
saline followed by 10% Formalin. Frozen sections were cut at 50
pm, mounted on microscope slides, and stained for histological
study. The approximate location of each electrode penetration
was usually determined on the basis of the distance from landmarks such as the appearance and disappearance of gray matter,
thalamus, and basal ganglia or electrolytic lesions. All of the units
described in this paper were located in the DLPN. Figure 1 shows
LOG.1. Photomicrograph of a histological sectioncut in the stereotaxic
plane, showing electrode tracks and a marking lesion (+ ) in the dorsolatera1 pontine nucleus (DLPN).
oculomotor nucleus.
LGN:
lateral geniculate nucleus, OMN:
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electrode tracks and a marking lesion at a location where units that
discharged during ocular following responses were recorded.
After completion of single unit recordings, we used the animals
for other experiments. In one, the effects of administration of
chemical agents in the DLPN were studied. In the other, we put
stimulation electrodes in the identified DLPN area by cementing
it to the side of the recording cylinder under anesthesia with ketamine hydrochloride to observe the responses of neurons in the
cortical medial superior temporal (MST) area to electrical stimulation. Preliminary results of these experiments have been described
elsewhere (Kawano et al. 1989, 1990b).
RESULTS
Neuronal activity during ocular following responses
In the DLPN, we recorded 112 neurons that discharged
in response to movements of the visual scene, eliciting ocular following responses. The receptive fields of 45 neurons
were examined by moving a small test spot, a slit of light, or
a restricted random dot pattern. The sizes of the receptive
fields were generally large and ranged from 10 X 10’ to
larger than 85 X 85O (i.e., the entire tangent screen). More
than half of the neurons ( 3 l/45,69%) had receptive fields
that included the fovea, but the others did not.
Responses of neurons to ramp movements of the fullfield random dot pattern in the eight directions were studied, and a majority ( lOO/ 112, 89%) of them were considered direction selective, because their average firing rates
were 22 times greater in the preferred direction than in the
nonpreferred direction. Further studies were executed on
these direction-selective neurons.
Figure 2A shows a sample of responses of a DLPN neuron (#02A) and ocular following responses to 38 presentations of an 80° /s rightward test ramp. The responses were
aligned at stimulus onset. The average response of this neuron is represented in the peristimulus time histogram. The
superimposed eye velocity responses, the averaged eye velocity profile ( - ), and its first derivative, eye acceleration, are shown. Forty-three milliseconds after the onset of
the stimulus motion, the firing rate of this neuron increased
abruptly. This initial vigorous response ended suddenly
- 15 ms later, and then its firing rate increased again and
remained at a high level. Although there is some variability,
it is apparent in the raster format that each response has
both an initial (transient) component and a sustained one.
No habituation of the neural response was observed
throughout the 38 trials. Thirteen milliseconds after the onset of the neural response (i.e., latency of 56 ms), the eyes
began moving. The distinct initial phase of eye acceleration
observed in Macaca mulatta, lasting -20 ms (Miles et al.
FIG.
2. Response properties of a DLPN neuron (#02A) to a moving
large-field visual stimulus, eliciting ocular following. A: responses of a
neuron, recorded in the right DLPN, to multiple presentations of an 80’ /s
rightward test ramp. Records, from top to bottom, indicate impulse rasters,
peristimulus histogram ( 1 ms binwidth), superimposed horizontal eye velocity profiles (dots), average eye velocity ( ), eye acceleration, and
superimposed stimulus velocity profiles ( n = 38 ) . Arrow shows estimated
time of response onset (43 ms for neural response and 56 ms for eye
movements, measured from stimulus onset). B: direction-tuning curve of
the neuron. A random dot pattern was moved in eight directions at 80’ /s.
In each case, the firing rate was averaged over a time interval extending
from 40 to 140 ms, measured from stimulus onset (n = 12). D, down; L,
left; R, right; U, up.
K. KAWANO,
684
M. SHIDARA,
AND
S. YAMANE
within 10 ms, and then the firing rate decreased to the zero
level. After the distinct initial phase of the response, which
lasted some 18 ms, the neuron increased its firing rate again
and kept its firing rate at about 170 imp/s (sustained response). To get a smooth profile of the neural response
Direction-tuning
without introducing a time delay, we calculated the spike
density function (Fig. 4A, -).
This function does not
The directions of the ramp movement that yielded the affect the temporal structure of the neural response ~30
strongest responses in the 100 direction-selective DLPN
neurons are presented in Fig. 3A. The length of each line Hz. The initial burst response also remained in the spike
density function. However, in the late sustained response
indicates the number of units preferring movement of the there was a temporal fluctuation at -20 Hz for 60- 150 ms
full-field random dot pattern in the direction specified by
the angle of the line. No significant differences were ob- after the stimulus onset. Because the temporal fluctuation
of the spike occurrence in the sustained response very often
served between preferences for ipsilateral or contralateral
varied from trial to trial in general (cf. the firing pattern in
(or for up vs. down) movement. The averaged firing rate of the raster format in Fig. 2)) we did not attempt to analyze
a neuron to the moving visual scene was normalized with
the fluctuation in detail. To eliminate this fluctuation and
respect to the best response for each direction tested. The to characterize the sustained component of the response,
best direction for each unit was then normalized to O”, and we further low-pass filtered the spike density function (with
the averaged, normalized result for the 100 units is prestructure of its
sented in Fig. 3 B. The average half-height, full-width tun- a filter cutoff of 10 Hz) and got ).a temporal
The effect of this filter is
low-frequency component (. .
ing bandwidth was 116”, indicating a 50% reduction in re- clearly seen in Fig. 4 B with a time scale of twice that of Fig.
sponse amplitude for visual motion that was, on average, 4A, and it reveals that the late sustained component of the
58 Oaway from the best direction.
response has a peak at 90 ms after the stimulus onset. It is
known that temporal profiles of the ocular following reDependence on stimulus velocity
sponse vary from one direction to another and from one
RESPONSE MAGNITUDE.
Using the best direction for each animal to another. However, with a given direction for one
neuron, we studied the responses to the moving visual scene animal, the temporal profiles have similar structures at difat different speeds ( 10, 20,40, 80, and 160’ /s) . Figure 4A ferent speeds (Miles et al. 1986). By the use of these two
shows a standard peristimulus time histogram of 1 ms bin- different waveforms of neural responses, we studied their
width, calculated from spike occurrences of the same neu- relation to ocular responses.
Figure 5 shows an example of families of response proron shown in Fig. 2 (#02A), to 20 presentations of a 160’ /s
rightward test ramp. The temporal structure of the neural files in the same neuron (#02A) shown in Figs. 2 and 4,
response is similar to that observed when an 80’ /s right- obtained with ramps of different speeds. In Fig. 5A, temporal profiles of neural responses are shown in the form of
ward test ramp was presented (Fig. 2A). Forty milliseconds
after the stimulus onset, the firing rate of the neuron in- the spike density function (top), with average eye velocity
profiles (middle), and eye act el.eration profiles (bottom)
creased vigorously. The response peaked at 400 imp/s
1986), was also observed in M. fuscata. The directional
selectivity of the neuron is shown in Fig. 2 B. The strongest
responses were for movements of the random dot pattern to
the right.
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FIG.
3. Directional selectivity of DLPN neurons to the movement of the large-field visual stimulus. A : distribution of
direction preferences for 100 DLPN neurons. B: normalized direction tuning profile for 100 DLPN neurons. The direction
tuning profile for each neuron was normalized to its best response, and the best response directions were shifted and aligned
to O” . Bars indicate SD. In each case, the firing rate was averaged over a time interval extending from 40 to 140 ms, measured
from stimulus onset.
c&g)
DLPN
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685
FOLLOWING
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FIG. 4. Temporal structure of the firing rate of a DLPN neuron (#02A). Peri-stimulus histogram with 1 ms binwidth (in
A), spike density function (---) , and temporal profile after low-pass filtering ( 10 Hz, * + * ) of the neural response obtained
with test ramps of 160” /s rightward are shown ( n = 20). Time scale in A is twice that in B.
recorded at the same time. The initial phase was distinct in
each neural response. The firing rate started to increase 4060 ms after the stimulus onset, peaked within IO- 15 ms,
and then quickly declined close to the spontaneous level.
The neural response between the onset and the first trough
was regarded as the initial phase. The initial phase of ocular
responses was also distinct in eye acceleration profiles. In
eye velocity profiles, however, a peak was not clear in all the
initial responses, especially for the slower stimulus speeds
( 10 and 40” /s). In caseswhere the eye velocity profile did
not have a clear peak, we estimated the initial peak eye
velocity as the maximum eye velocity achieved by the first
wave of eye acceleration, according to the criterion of Miles
et al. (1986).
Magnitudes of both initial and late sustained components of the neural responses were measured and plotted in
Fig. 6A with responses to other stimulus speeds (20 and
80” /s). The initial component was distinctly observed
through all stimulus speeds within the range of lo- 160” /s.
The peak value was used as an index of the initial response,
and it increased when the stimulus speed increased to 80” /s
but showed a saturation at stimulus speed of 16O”/s (Fig.
6A, 0). The best speed of the initial response for this neuron was 80” /s. The firing rate of the sustained component
increased with increases in ramp speed over the range of
lo- 160” /s. The neural response of the sustained component is more clearly seen in the low-pass filtered ( 10 Hz)
response profiles in Fig. 5B with a time scale twice that of
Fig. 5A. For quantitative measurements of the sustained
response, two methods were used. One is a stimulus-locked
measurement, in which the firing rate of the neuron over
the time interval extending from 90 to 130 ms, measured
from stimulus onset, was averaged (Fig. 6A, n ), and in the
other, the amplitude of the first peak of the low-pass filtered
( 10 Hz) response profile was measured (Fig. 6A, A). Both
values increased as a linear function of the logarithm of
stimulus velocity s 160” /s (Fig. 6A ). In the present study,
because we studied neural responses during ocular following responses, the stimulus velocity on the animal’s retina
was not exactly equal to the velocity of the stimulus on the
screen. For example, in Fig. 5, when the stimulus started
moving at 160” /s, the firing rate of the neuron started to
increase 40 ms after the stimulus onset. The latency of the
ocular response was 55 ms, which means that the eye movements changed the image motion on the retina at 55 ms,
and this might have affected the firing of the neuron 95 ms
after the stimulus motion. Thus, when we measured the
sustained response in the interval 90- 130 ms after the stimulus onset, the stimulus velocity on the retina was < 160” /s.
If we suppose that the neural responses were affected by the
stimulus on the retina preceding the time equal to the latency of the neural response (40 ms in this case), the effective stimulus velocity in the retina would be 156.7”/s on
average. When the stimulus velocities were low, the gain of
the ocular following responses (a ratio of the eye velocity to
the stimulus velocity) was higher than that with high velocities, but both the ocular and neural latencies were longer.
Thus the difference between the stimulus velocity and the
retinal slip velocity was also small in these cases,e.g., 0.3”/s
at 10” /s. We adopted the stimulus velocity on the screen as
the stimulus velocity for the following analysis.
The firing rates of 87 direction-selective DLPN neurons
were averaged over the range of lo- 160” /s, and the means
of the firing rates for the initial and sustained components
K. KAWANO,
686
M. SHIDARA,
AND
S. YAMANE
300
imp/s
I
+ s-----m-/-
c ____
JA
0'
loo/s
f’
l
--
******
0
160/s
40%
f@
/
I
0
100
TIME
0
(msec)
I
.
..I...
11..
100
TIME
I
200
(msec)
FIG. 5. Effect of changes in stimulus speed on response profiles of a DLPN neuron (#02A) and on simultaneously
recorded eye movements. Speed of test ramps ( y1= 20 for each) : 10 (-),
40 (
. ) , and 160° /s (- T -) . Time scale in A is
twice that in B. In B, all response profiles (spike density function of firing rate, eye velocity, and eye acceleration) are shown
after low-pass ( 10 Hz) filtering. Records, from top to bottom, indicate spike density functions of neural responses, average
eye velocity profiles, eye acceleration profiles, and average stimulus velocity profile (one stimulus speed).
l
at each stimulus speed are indicated by the open symbols
connected by dotted lines in Fig. 6A. Both components
increased during increase of stimulus speed 580” /s but saturated at the high speeds (80- 160’ /s). The relationship
between the firing rate and the logarithm of stimulus speed
was linear within the range of 10-40’ /s ( r = 1.OOOfor initial response, r = 0.993 for sustained response with stimulus-locked measure, r = 0.994 for sustained response after
low-pass filtering). The sensitivity of each component (i.e.,
theslopeinFig. 6A)was 114.3(imp/s)/log(O/s)forinitial
response, 67.4 for sustained response with stimulus-locked
measurement, and 74.7 for sustained response after lowpass filtering.
The best speeds of the 87 direction-selective DLPN neurons, for both initial and sustained responses, were studied
and are presented in Fig. 6 B. Most of the neurons showed
strongest responses at high stimulus speeds ( 280” / s) ,
whether their response magnitudes were measured for their
l
(63/87, 72%) or sustained components (63/87,
72%) . In about half of these neurons ( 3 1/ 63 ), however, the
initial
initial responses showed a saturation at the high stimulus
speeds, and their best speed was 80° /s. The two measures of
the sustained response agreed with each other in most cases
(71187).
Figure 6C shows the relationship between the initial and
sustained responses to stimulus speed. Each dot indicates
one neuron and is plotted in the square of the best speed for
the initial response (X axis) and of the best speed for the
sustained response (y axis). For the sustained responses,
the stimulus-locked measurement was used. Although
some neurons (2 1/ 87), the sustained responses of which
preferred 160’ /s, showed saturation in their initial responses at high stimulus speeds, it is clear that the two different components of the neural responses have the same
relationship to stimulus speed in most cases. In other
words, when the initial response of a neuron preferred faster
DLPN
AND
OCULAR
FOLLOWING
687
speeds, its sustained response also tended to prefer faster
speeds, and the sustained response preferred slower speeds
when its initial response preferred slower speeds.
TEMPORAL
2
.-E
,,.&
a
a
(3
z
.... .
•~.br.““‘~
2;
+100
.a***
..**
*
00 initial
n o sustained
AA after 1.p.f
z
STIMULUS
SPEED (deglsec)
I
I
I
I
10
20
40
80
STIMULUS
1
)
160
SPEED (degkec)
C
n
8
el60
8
p.
IO
20
40
80
160
best speed for initial resp. (degkec)
FIG. 6. A : effect of changes in stimulus speed on response magnitude of
a DLPN neuron (#02A, closed symbols connected by continuous lines)
and mean of 87 DLPN neurons (open symbols connected by dotted lines).
Initial components of responses are indicated by circles; sustained components of responses are indicated by squares (stimulus-locked
measure) and
triangles (after low-pass filtering). Note that the abscissa is on a log scale.
B: distribution of “best” speeds for initial responses ( l ), sustained responses by stimulus-locked
measure ( n ), and sustained responses after
low-pass filtering (A ). C: relationship between best speeds for the initial
and sustained responses. Each dot indicates one neuron, plotted in the
square of the best speed for the initial response (X axis) and of the best
speed for the sustained response ( y axis).
CORRELATION
BETWEEN
INITIAL
RESPONSES.
It
was evident from the superimposed profiles in Fig. 54 that
not only the magnitude but also the temporal structure of
the neural responses was dependent on stimulus speed. The
initial neural response started earlier with faster stimuli
than with those of slower speeds. The times of the peaks in
the initial responses ( peak time) of the neuron in Fig. 54
were measured and plotted against stimulus speed in Fig.
7A (A). Faster stimulus speeds also decreased both the
peak times of initial eye velocity ( q ) and of initial eye acceleration (A). Because dependence of the peak time of the
neural response on stimulus speed was similar to that of
ocular responses (eye velocity and eye acceleration), the
time difference between the neural response and the ocular
response was relatively independent of stimulus speed. The
initial peak times of the neural responses preceded the ocular responses by 18.2 t 1.9 (SD) ms for eye velocity and by
13.0 t 2.4 (SD) ms for eye acceleration (mean of 5 speeds).
It has been reported that the response latency of ocular following responses is longer for slower than for faster stimulus speeds (Miles et al. 1986)) and this was consistent with
the present result (Fig. 7A, 0). The latencies of the neural
responses were obtained with stimuli of lo- 160’ /s ( 0))
because the initial responses of the neuron were large
enough (200-280 imp/s) for reliable measurements. The
response latencies of the neuron were also inversely related
to the stimulus speeds as shown in Fig. 7A (0). Thus the
time difference between the response latency of the neuron
and the eye did not vary much [ 12 t 3.0 (SD) ms] .
Response latencies were reliably obtained with all of the
stimulus speeds ( lo- 160’ /s) for 19 neurons. The relationship between their latency of neural responses and that of
eye movements is plotted in Fig. 7C. Latencies of both
neural and ocular responses decreased in parallel when the
stimulus speed increased in all 19 neurons. The correlation
was significant for 14/19 neurons (Y > 0.878, P < 0.05).
The regression lines have slopes near 1.O [ 1.O t 0.5 (SD)].
The relation between the initial peak time of neural responses and that of eye movements on the same 19 neurons
is plotted in Fig. 7, D (initial peak eye acceleration) and E
(initial peak eye velocity). It is evident from Fig. 7, D and E
that the peak time of the initial ocular responses also correlated with that of the neural responses. Fourteen out of the
19 neurons had significant correlation ( r > 0.878, P < 0.05)
for the initial peak eye acceleration, and 12/ 19 for initial
peak velocity, and average slopes were 0.9 t 0.5 (SD) and
1.1 + 0.8 (SD), respectively. These trends are also evident
fromthe population averages plotted in Fig. 7 B. Each point
in Fig. 7B is-an average value taken from the 19 neurons,
shown in Fig. 7, C-E. Neural and ocular responses are both
dependent on stimulus speed, and they are temporally
correlated with each other. In the 19 neurons, the onset
latency of the neural response always preceded that of the
ocular responses by 9.7 ms on average, ranging between 4.2
and 16.0 ms (average of 5 speeds). The initial peak of the
neural response also preceded the initial peak of the ocular
responses (average 10.6 ms, range 5.2-l 5.6 ms for eye
K. KAWANO,
688
M. SHIDARA,
AND
l
latency (N)
o latency (E)
A init peak (N)
7
80 ‘,
40 ;
1
’
20
STIMULUS
;
90 ‘in’ g80
L
cc
-
I
,111
1
10
50
:
a
l50
-
1111
111
J
-200
100
10
1
100
200
SPEED (degkec)
; D
90 ‘vl‘
-:
E
c
~60 -
40 -
-80
3
w
-
;70
a
w
:
rn
a60
Ir
:
k
:
z50
-
z50
-
40 -
40 -
1~~~~~~1111111111111J
40
50
60
70
2li-Few
LATENCY
I
50
20
STIMULUS
SPEED (degkec)
gr70 is
S. YAMANE
(N) (ms)
80
INIT PEAK (N) (ms)
l~~~~~‘~lllllllllll*J
40
50
60
70
80
INIT PEAK (N) (ms)
FIG. 7. Effect of changes in stimulus speed on temporal profiles of both neural and ocular responses. A: example of
responses of a DLPN neuron ( #02A, cf. Fig. 5 ) . B: mean values of 19 DLPN neurons. Filled circles indicate latencies of
neural responses, open circles indicate latencies of ocular responses, filled triangles indicate initial peak times of neural
responses, open triangles indicate initial peak times of eye acceleration profiles, and open squares indicate initial peak times
of eye velocity profiles. C, D, and E: temporal correlation between initial component of neural and ocular responses in 19
neurons. C: correlation between response latencies. D: correlation between initial peak times of neural responses and eye
acceleration profiles. E: correlation between initial peak times of neural responses and eye velocity profiles.
acceleration; average 16.6 ms, range 11.4-2 1.2 ms for eye
velocity).
Temporal responsestructures
LATENCY.
We acquired a reliable measurement
of the latency for 77 direction-selective DLPN neurons (including 19 neurons in Fig. 7) that had initial responses large
enough to be distinguished from firing levels during the
preresponse period for at least one stimulus velocity. When
the firing rate of a neuron during the preresponse period
was variable, we sometimes increased the number of trials
at the preferred stimulus speed or changed the postsaccadic
delay interval (see METHODS).
The distributions of latency
measure are shown in Fig. 8. The visual stimulus used was a
random dot pattern moving in the preferred direction and
RESPONSE
at the preferred speed (best speed for the initial response in
most cases). Figure 8A shows neuronal latency from the
onset of the visual stimulus. Mean latency for the 77 DLPN
neurons was 49.0 t 8.0 (SD) ms. Response latency is plotted relative to the onset of eye movements in Fig. 8B. On
average, neurons started responding 5.7 t 8.1 (SD) ms before eye movement. Seventy-three percent (56/77) of the
DLPN neurons were activated ~50 ms after the onset of the
stimulus motion. In most cases (67/ 77, 87%), their increase of firing rate started before the eye movements, and
34% of them (26/77) started > 10 ms before the eye movements.
Theinitialresponsehas
rising and falling phases. The falling phase varies from neuron to neuron and is not always clear in eye velocity profiles
INITIALCOMPONENTOFRESPONSES.
DLPN
50
B
.A
60
LATENCY
initiation
--+-onset
0
-20
-10
0
70
(msec)
AND
80
of
0cuLar
foLLowin
of
neural,
discharge
10
20
TIME (msec)
30
OCULAR
90
40
FIG. 8. Distribution
of latencies for 77 DLPN neurons. For the visual
stimulus of this graph, the random dot pattern moving in the preferred
direction and preferred speed of each neuron was used. A: number of
neurons was plotted against latency from onset of the visual stimulus. B:
number of neurons was plotted against time from the onset of eye movements.
as shown in Fig. 5A. Thus the attempt to compare the temporal structures of the initial responses of firing rate and eye
movement was made only on the rising phases. The initial
phases of the neural and ocular responses to 160’ /s test
ramps in Fig. 5A have been superimposed and are shown in
Fig. 9A. To facilitate comparison of the temporal structures
of the initial responses, the vertical scales were adjusted.
The timing of the eye acceleration profile was advanced 13
ms, and that of the eye velocity 19 ms. It is evident that the
initial rising phases of the eye acceleration profile and of the
neural response have a similar temporal pattern. It takes
more time to reach the peak in the eye velocity profile.
As an index of the time course of the rising phase of the
initial response, the time point at which the amplitude
reached from half-the-peak to the peak amplitude was measured and called the “half-time” of the initial response. This
index is less sensitive to slight changes in the preresponse
period (because of the spontaneous activity of a neuron or
ocular drifts) than is measuring duration between the response onset and the peak time. The half-times of the initial
responses in Fig. 9A were 4.2, 4.9, and 6.7 ms for neural
response, eye acceleration, and eye velocity, respectively.
The half-time was measured in the 346 neural responses for
which peak amplitudes were >50 imp/s (out of 435 responses from 87 neurons at 5 speeds). A correlation between stimulus speed and half-time was calculated for 40
neurons (out of 87) for which initial peaks were >50 imp/s
at all five speeds. Three neurons ( 8%) had significant positive correlations (r > 0.878, P < 0.05)) i.e., initial responses
of a long half-time to higher speed, and one ( 3%) had a
significant negative correlation (Y < -0.878, P < 0.05), i.e.,
initial responses of a short half-time to higher speeds. In the
remaining neurons (90%)) no significant correlation was
observed. In general, no significant correlations were observed between the half-times of initial neural responses
and those of ocular responses (eye acceleration and eye velocity) across changes in stimulus speed. On the other hand,
when half-times were measured in the 346 neural responses
FOLLOWING
689
and in the 368 ocular responses for which initial peak eye
acceleration was > 180’ / s2, their distribution, as shown in
Fig. 9 B, indicated that half-times of the initial phases of the
neural and ocular responses were in a similar range (>80%
between 2 and 10 ms). The mean half-times were 5.9 t 2.5
(SD) ms for neural responses, 5.0 t 2.1 (SD) ms for eye
acceleration, and 7.1 t 3.3 ( SD) ms for eye velocity. The
initial phases of the neural responses have a half-time of
duration intermediate between that of eye acceleration and
that of eye velocity.
The falling phase of the initial neural response was different from neuron to neuron. The firing rate of the neuron
shown in Fig. 5A (#02A) decreased almost to the spontaneous firing level after the initial peak in responses to three
different stimulus speeds. On the other hand, there were
neurons for which the firing rate after the initial peak
stopped at a level far above that of spontaneous firing. To
/
/
A
400
imp/s
I
I
I
F rate
.. ... ... Ea -1 3ms
-
300
--
Ev
600=
5
-19ms
200
100
0
100
TIME (msec)
m
kz120
is
n
response
acceleration
100
ayel
VeLocity
m
so
+
0
2
60
I
05
i+
40
z
20
0
FIG. 9. A : comparison between the temporal structure of the firing rate
of a DLPN neuron (#02A, -)
and simultaneously recorded eye movements (. . indicates eye acceleration profile and - - - indicates eye velocity profile). Scales for response amplitudes were adjusted to obtain equal
heights for initial peak responses. Sample profiles were obtained with test
ramps of 160’ /s rightward. The same profiles are shown by interrupted
lines in Fig. 5 but on different scales. B: distribution of half-times of initial
responses. Numbers of half-times of neural responses are indicated by
black bars, those of eye acceleration profiles by white bars, and those of eye
velocity profiles by cross-hatched bars.
l
690
K. KAWANO,
M. SHIDARA,
quantify the differences in the falling phase of the initial
neural responses, the amplitude from the initial peak to the
following trough was measured, and the ratio to the initial
peak was calculated and named “dip ratio” [i.e., dip ratio =
(Peak - trough)/peak] . The dip ratio of the neural responses in Fig. 9A (neuron #02A) was 1.02. The dip ratio
of the 435 responses (87 neurons, 5 speeds) was distributed
between 0.00 and 1.93, and the mean was 0.5 1 t 0.39
(SD). The mean of the period between the peak and the
trough was 7.4 t 4.6 (SD) ms.
SUSTAINED
COMPONENT
OF RESPONSES.
Peak times were
also measured in the low-pass filtered ( 10 Hz) profiles of
the responses (examples are shown in Fig. 5 B) as an index
of the temporal structure of the sustained component. The
number of responses for which the peak time of the lowpass filtered profiles was within 150 ms from the onset of
the visual stimulus was 425 /435 for neural responses, 82/
435 for eye velocity, and 435/435 for eye acceleration.
Their means were 85.3 t 16.7, 136.0 t 11.3, and 88.7 t
14.2 (SD) ms, respectively (peak times within 150 ms).
Unlike the peaks of the initial response, there was no overall
correlation between the peak times of sustained components of neural and ocular responses (examples are shown
in Fig. 7). But their mean values and the distribution
shown in Fig. 10 indicate that there is a similarity between
the temporal profile of sustained component of neural responses and that of the eye acceleration profiles but not of
eye velocity profiles. Both the temporal profiles of neural
responses and eye acceleration reach a peak between 60 and
120 ms after stimulus onset and start decaying ( >90%). In
contrast, the eye velocity profile does not reach a peak until
2 150 ms after stimulus onset in most cases (>80%).
Correlation between amplitude of neural
and ocular responses
It has been reported that the magnitude of ocular following responses elicited by a moving random dot pattern depends on stimulus speed (Miles et al. 1986). This was confirmed when we analyzed ocular responses recorded during
the experiment on speed tuning of neurons (5 speeds, 87
neurons). As an index of the initial ocular response, the
m
120
neural.
ii
s
100
%
g
response
cl
eye
acceLeration
-m
eye
velocity
80
+
0
60
fY
#
40
.
60
0-1
.II(80
.
100
120
140
PEAK TIME of SUSTAINED
RESPONSE (ms>
10. Distribution of peak times of sustained responses after lowpass ( 10 Hz) filtering. Numbers of peak times of neural responses are
indicated by black bars, those of eye acceleration profiles by white bars,
and those of eye velocity profiles by cross-hatched bars.
FIG.
AND
S. YAMANE
peak value was used, and as a stimulus-locked measurement of the sustained response, the eye velocity (or acceleration) was averaged over a time interval from 100 to 140 ms,
measured from stimulus onset. When the stimulus speed
was low ( 1O-40’ / s) , ocular responses, both initial and sustained, increased with increase in stimulus speed in all
cases. However, when the stimulus speed was high (80160’ /s), there were some cases in which saturation was
observed, especially in the initial response. As a result, high
correlations (r > 0.878, P < 0.05) between the logarithm of
stimulus speed and ocular response were observed in about
half of the initial responses (39% for eye velocity, 56% for
eye acceleration) and in the most of the sustained responses
(94% for eye velocity, 89% for eye acceleration).
As shown in Fig. 6C, more than one-half of the DLPN
neurons (63/87, 72%) preferred high stimulus speeds (80,
160’ /s) . Correlation coefficients between the amplitudes
of neural and ocular responses were calculated in the 63
neurons preferring high speeds. In -40% of the initial responses (40% for eye velocity, 37% for eye acceleration)
and in ~70% of the sustained responses (67% for eye velocity, 70% for eye acceleration), the correlation was high (r >
0.878, P < 0.05) between neural and ocular responses.
Because both initial and sustained responses were highly
correlated with ocular responses in some neurons, the relationships between the two were studied. As a measurement
of the responses, the stimulus-locked measurement was
chosen for the sustained response. For the correlation with
eye acceleration, the relationships between the initial and
sustained responses were studied in 25 neurons, and for the
correlation with eye velocity they were studied in 18 neurons. An example is the relationship of the neuron #02A
shown by closed symbols in Fig. 11 (A : eye acceleration, B :
eye velocity). The data for eye acceleration (Fig. 11A ) are
in a similar range of amplitude ( lOO-500’ /s2) for both
initial responses ( l ) and sustained responses ( n ) . However, for this neuron, the firing rate at the peak of the initial
responses ( l ) is almost twice that of the sustained responses ( n ). Although the relationship between firing rate
and eye acceleration was often different for initial and sustained responses in each neuron, it was similar on average.
The firing rates and eye accelerations were averaged at each
stimulus speed for 87 direction-selective DLPN neurons to
study the relationship as a population (Fig. 11A, open symbols connected by dotted lines). The slopes of the linear
regression between firing rate and eye acceleration were
close [initial response: 4.2 t 0.3 (SE), sustained response:
3.4 t 0.6 (SE) ( “/s2)/(imp/s)]
and the two linear regression lines overlapped. The correlation coefficient of 10
averaged responses (initial and sustained responses at 5
speeds) was 0.99 [slope = 4.2 ( “/s2)/(imp/s)].
As shown in Fig. 11B, eye velocities were generally large
during the sustained portion of the responses (D) than the
initial peak ( l ) . For neuron #02A, the sensitivity of the eye
velocity to the firing rate for sustained responses tends to be
larger [ 0.16 t 0.07 (SD) (O/s)/( imp/s)] than that for initial responses [0.03 t 0.02 (SD) (“/s)/(imp/s)]
(significantly different by paired t test, P < 0.0 1). For the 87 neurons (Fig. 11B, open symbols connected by dotted lines),
the slopes of the linear regression between firing rate and
eye velocity were 0.032 t 0.002 (SE) for initial response
DLPN
AND
OCULAR
600 -
FOLLOWING
691
20 -
-500 cv
m
ii
<400 ii?
=300
-m
.
0
0
a 200 w
>
WIOO-
za
3
=101
w
>
w
>
w
P
OI
0
200
FIRING RATE (impkec)
100
300
L
-
000
100
200
FIRING RATE (imp/set)
300
FIG. 11. Relationships
between firing rate and eye movement (A: eye acceleration, B: eye velocity) for neuron #O2A
(indicated by closed symbols connected by continuous lines), and mean of 87 neurons (indicated by open symbols connected by dotted lines) for initial ( l o ) and sustained responses (m ) . Test ramps were 10, 20, 40, 80, and 160’ /s.
is indicated by the stimulus trace in Fig. 12B). The firing
rate of the neuron always started decaying -45 ms after
blanking (indicated by an arrow), in spite of the degree of
blur. Thus blurring affected responses to the onset of the
stimulus motion but did not affect responses to the disappearance of the stimulus. The ocular responses are also
Responseto blurred images
shown in middle (eye velocity) and bottom (eye acceleraIt has been reported by Miles et al. ( 1986) that moderate tion) of Fig. 12B. As we saw with the neural responses in
low-pass spatial filtering (blurring) of a random dot pattern the upper panel, blurring the visual scene increased the laprogressively increases the latency of ocular following re- tency of the ocular following responses but did not change
sponses. To investigate the relationship between the laten- the timing of the appearance of the effects of blanking (indities of neural responses and eye movements, the effects of cated by arrows). The time difference between neural reblurring a random dot pattern with a diffusing screen were sponses and ocular responses to blanking the visual scene
studied. To get different amounts of blur, the ground sur- was - 10 ms, and this was consistent with those of the iniface and the paper screen were separated by distances of 1, tial responses. Similar results were observed in five other
3, and 5 cm. Figure 12A shows the superimposed responses neurons examined.
of a DLPN neuron (#12A, top), the average eye velocity
About 55 ms after blanking the visual scene ( 115 ms after
profiles (middle), and the eye acceleration profiles (bot- the stimulus onset), OFF responses were sometimes obtom). It is evident that blurring of moving images delayed served in the neural responses (Fig. 12B). The OFF rethe latency not only of the eye movements but also of the sponses were especially conspicuous for the visual stimulus
neural responses. Furthermore, the peak time of both without blurring (Fig. 12B, -)
, seen as a pulse-like firing
neural and ocular initial responses (either eye velocity or pattern 10 ms in duration. In the eye velocity traces, OFF
responses were also observed with a delay of - 10 ms, sugeye acceleration) was also delayed in parallel by blurring.
The relationship between the latencies of neural re- gesting a correlation between the neural responses and eye
sponses and those of eye movements is plotted in Fig. 13A movements.
( l ) . It reveals that the neural responses preceded the eye
Blurring affected not only the latency of the responses
movements by - 10 ms in each case. These changes were but also the amplitude. The effect was clearly seen both in
very similar in all eight DLPN neurons for which response the initial and sustained responses for ocular responses, eslatencies were reliably obtained at at least two different de- pecially in eye velocity traces (Fig. 12A, middle). On the
grees of blur (Fig. 13A, lines without symbols). Whereas other hand, although the effect was clearly seen in the initial
the latencies of the responses were delayed by blurred
neural responses, in this neuron it was not obvious in the
images, the timing of the effect of blanking the visual scene sustained responses (Fig. 12A, top). The effects of blur on
did not change as a function of the degree of blur. Figure the amplitude of the responses were studied in 15 neurons
12B shows the effect of blanking the visual scene with and (the effects of blurring at separation of 1 and 5 cm were
without blurring. The experiment was executed on the studied in the 15 neurons and those at 3 cm were studied in
same neuron (# 12A) as in Fig. 12A and employed the same 1O/ 15 ) and is shown in Fig. 13, B and C. On average, blurvisual stimulus, but the moving visual scene was blanked 60 ring the visual scene reduced both neural and ocular rems after the onset of stimulus motion by a mechanical shut- sponses, especially in their initial responses, but affected
ter, turning off the visual stimulus on the screen (the timing
ocular responses more than neural responses.
and 0.204 t 0.019 (SE) (“/s)/(imp/s)
for sustained response. Thus the relationship between firing rate and eye
velocity was different for initial and sustained responses not
only in each neuron, but also on average.
K. KAWANO,
692
M. SHIDARA,
AND
S. YAMANE
400
imp/s
-•*
--
control
1cm
3cm
5cm
l
1
I
0
100
TIME (msec)
I
1
0
100
TIME
(msec)
FIG. 12. Effects of blurring a random dot pattern, moving rightward at 80’ /s, on the responses of a DLPN neuron
(#12A). Blurred images were obtained with ground glass and paper screen separated by distances of 1
), 3 (- - -), and 5
). Control responses were obtained with the random dot pattern projected directly on the screen (-).
The
cm ( moving scene was blanked at 200 (A) and 60 ms (B) after stimulus onset, and then the animal sat in the dark. Records, from
top to bottom, indicate spike density functions of neural responses, average eye velocity profiles, eye acceleration profiles,
and average stimulus velocity profile (A: it = 60; B: rt = 60). In B, the end of the stimulus velocity profile indicates the time
when the visual screen was blanked, and arrows indicate the time when the effects of blanking were observed.
(
Responseto check pattern
The correlation between the neural responses and eye
movements was further examined by substituting check
patterns for the usual random dots. Figure 14 shows effects
of changes in stimulus speed on neural responses (neuron
#14A) and on ocular following by the use of two different
visual stimuli, the random dot pattern (A) and the check
pattern (B). In Fig. 14A, responses were recorded when a
random dot pattern was projected and moved at speeds of
10,20,40,80, and 160’ /s. As stimulus speed increased, the
amplitude of the ocular following response increased and
the firing rate of the neuron increased up to saturation at
stimulus speeds of 80- 160’ /s. When a check pattern of 0.5
c/ Owas used as a visual stimulus in place of the random dot
pattern (Fig. 14B), a different relationship between the re-
l
l
l
sponses of the same neuron and stimulus speed was observed. The firing rate of the neuron increased with increasesin stimulus speed from 10 to 40’ / s. When the stimulus speed was 80° /s, however, the neuron showed a
prominent burst-and-pause firing pattern, and its overall
firing rate was lower than that at 40’ /s. At 160’ /s, the
neuron responded with a latency of 43 ms and stopped suddenly after 20 ms, and at the same time the eyes moved for
only 20 ms and stopped. About 60 ms later, the firing rate of
the neuron increased again, and the eyes also started moving. Thus the firing rate of the neuron decreased from 80 to
160’ /s. At the same time, the amplitude of the ocular following response decreased too. The peculiar firing patterns
of the neuron observed with stimulus speeds of 80 and
160’ /s were observed in every trial, as shown in the raster
format. The same experiment was performed on 15 DLPN
DLPN
AND
OCULAR
A
500
40
FOLLOWING
693
C
50
LATENCY
60
[neuron]
70
(msec)
DISTANCE
(cm)
DISTANCE
FIG. 13. A : relation between the latencies of neural responses and those of eye movements when blurred or unblurred
images were used as visual stimulus. Closed symbols indicate data points measured from responses in Fig. 12 (neuron
#12A). Lines without symbols indicate data taken from other seven neurons. B: sensitivity of the amplitude of initial
component of neural response (A ) and ocular following ( l indicate effects on eye velocity and o indicate effects on eye
acceleration) to blurring of the image. Data points are means for 15 neurons ( 10 neurons for 3-cm separation). C: sensitivity
of the amplitude of the sustained component of neural response (A ) and ocular following ( l indicate effects on eye velocity
and o indicate effects on eye acceleration) to blurring of the image. Data points are means for 15 neurons ( 10 neurons for
3-cm separation). Bars indicate SD.
neurons, and all their responses decreased when the check
pattern of 0.5 c/ O moved faster than 80’ /s. In order to
measure neural responses as a whole, we averaged the firing
rate over a time interval extending from 40 to 140 ms, measured from stimulus onset. The averaged firing rate for each
stimulus was normalized with respect to the best response
to the random dot pattern. Averages of responses to each
stimulus were calculated for the 15 neurons and plotted in
Fig. 15A ( 0 indicate the means of the responses to the random dot pattern and 0 indicate the means of the responses
to the check pattern of 0.5 c/ O) . To evaluate the total ocular responses, mean changes in eye position from 50 to 150
ms, measured from stimulus onset, were also calculated
and normalized with respect to the best response and are
plotted in Fig. 15B. It is evident that both neural and ocular
responses decreased with stimulus speeds 280” / s when a
check pattern of 0.5 c/ O was used. Not only the general
decrease in response amplitude, but also the burst-andpause firing patterns at a stimulus speed of 80’ /s and transient firing for -20 ms at 160° /s, which were seen in the
neuron in Fig. 14B, were observed in the 15 neurons.
Experiments using a sine wave grating pattern as a visual
stimulus have revealed that the ocular following response
increases in association with increasing temporal frequency
of the visual stimulus but decreases rapidly when the temporal frequency of the visual stimulus exceeds 20 Hz (Miles
et al. 1986). To see if the decreases in neural and ocular
responses to the check pattern at higher speeds is due to the
upper limit of the bandwidth in the temporal frequency of
the ocular following system, we replaced the check pattern
with a different spatial frequency. Five neurons were tested
with a check pattern of 0.25 c/ O.Averages of responses are
indicated by open triangles in Fig. 15, A and B. It is evident
that both neural and ocular responses started decreasing
with the stimulus speed at 160’ /s. When a check pattern of
0.25 c/O was used as a stimulus, the stimulus speed that
gave the best response was 8O”/s. With 0.5 c/O checks, the
(cm)
best response was elicited by 4O”/s speed. Although the
check patterns contain a wide range of spatial frequencies
in terms of a Fourier decomposition of the visual stimulus,
in both casesthe main temporal frequency of the stimulus
was20Hz(0.25~/”
X80”/s,0.5c/”
X40”/s).Theresults
support the idea that the decrement in both neural and
ocular responses when check patterns moved at high speeds
is due to the stimulus exceeding the temporal frequency
bandwidth of the ocular following system.
Experi ments using a sine wave grati ng pattern as a visual
stimulus have also revealed that the ocular following response shows signs of oscillation when the temporal frequency of the visual stimulus approached 40 Hz (Miles et
al. 1986). Both the characteristics of the oscillation and the
marked decrement in response amplitude indicated that
the temporal frequency of the visual stimulus was beyond
the upper limit of the bandwidth of the ocular following
system. It is evident in Fig. 14B that the neuron showed a
distinctive burst-and-pause firing pattern when the stimulus speed was 80’ /s, and at the same time the eyes also
moved and stopped repeatedly. The burst-and-pause firing
pattern was observed not only when a check pattern of 0.5
c/O was moved at 8O”/s, but also when the 0.25 c/O check
pattern was moved at 160’ /s in the five neurons tested. In
other words, the firing rate of these neurons was always
modulated in the burst-and-pause pattern when the main
temporal frequency of the visual stimulus was 40 Hz. Figure 16 shows an example. When a check pattern of 0.5 c/ O
was used as a stimulus, the burst-and-pause firing pattern
was observed in the neuron # 16A at stimulus speed of 80’ /
s (continuous line), and when a check pattern of 0.25 c/ O
was used, a similar firing pattern was observed at stimulus
speed of 160° /s (dotted line). In both cases,the profiles of
eye movements showed similar temporal modulations (eye
velocity profiles in middle, eye acceleration profiles in bottom), resembling the ocular responses observed when the
temporal frequency of a sinusoidal grating pattern ap-
694
K. KAWANO,
M. SHIDARA,
AND
S. YAMANE
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45ms
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0
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<msec>
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0
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0
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0
TIME
100
200
Cmsec>
FIG.
14. Effects of changes in stimulus velocity of the random dot pattern (A) and a check pattern of 0.5 c/O (B).
Stimulus velocity, from left to right, was 10,20,40, 80, and 160° /s in A and B. In each case, records, from top to bottom,
indicate impulse rasters, peristimulus histogram ( 1 ms binwidth), superimposed horizontal eye velocity profiles (dots), eye
velocity ( ), eye acceleration, and superimposed stimulus velocity profiles (A: n = 40; B: yt = 60). Direction of ramps
was rightward. Note that scales are different between A and B for both eye velocity ( O/s) and eye acceleration ( O/s2).
DLPN
AND
OCULAR
FOLLOWING
695
(3
Z
a
I
l
STIMULUS
random
60
dot
SPEED (degkec)
STIMULUS
SPEED (degkec)
FIG. 15. Effect of the use of check patterns in place of the random dot pattern. A: sensitivity of neural responses to
changes in stimulus speed. Firing rate was averaged over a time interval extending from 40 to 140 ms, measured from
stimulus onset, and was normalized with respect to best response to random dot pattern of each neuron. Averaged data from
15 neurons for responses to a random dot pattern ( l ), a check pattern of 0.5 c/ O(o ), and data from five neurons to a check
pattern of 0.25 c/O (A) and SD (error bars) are shown. B: sensitivity of ocular responses to changes in stimulus speed. The
mean of changes in eye position from 50 to 150 ms from stimulus onset was normalized with respect to best response to
random dot pattern of each response. Data were obtained from the ocular responses simultaneously recorded with the data in
A and the symbols used are the same in A.
proached 40 Hz (Miles et al. 1986). In Fig. 16, the firing
pattern of another neuron, which was recorded in the same
animal and which had the same preferred direction as the
neuron #16A (continuous line) to the same stimulus (the
check pattern of 0.5 c/ O moving downward at 80° /s), is
also shown (indicated by an interrupted line, neuron
#16B). It is evident that although the amplitude of each
peak of the burst-and-pause firing pattern of DLPN neurons is different from neuron to neuron, the firing patterns
of neurons of a given animal in response to a stimulus in a
given direction have similar temporal structures, suggesting
that synchronization of these neural activities may cause
the oscillation in ocular responses.
Responsesto a small target (smooth pursuit)
It has been reported that some DLPN neurons show
smooth-pursuit-related responses (Mustari et al. 1988; Suzuki and Keller 1984; Suzuki et al. 1990; Thier et al. 1988).
Responses during smooth pursuit eye movement were studied in 4 1 neurons that discharged to movements of the random dot pattern. Twenty-eight (68%) were activated when
the animal pursued a target moving against a dark background. Examples are shown in Fig. 17. The neuron shown
in Fig. 17A preferred downward movement of the random
dot pattern (#16A, cf. Fig. 16A). When a small target
started moving downward from the center of the screen at
30° /s, the firing rate of the neuron increased before the
animal started to pursue. At - 100 ms after the onset of the
target motion, the firing rate reached a peak and then
started to decrease. The firing rate came to a steady level at
-300-400 ms after the stimulus onset. Because the target
started moving from the center of the screen, the animal
made catch-up saccades with latency of 180-400 ms (dotted line in the eye velocity trace). However, the temporal
structure of the neural response did not differ even when
the target movement of a step-ramp was used to avoid the
contamination of saccades(Fig. 19, below). Of the 28 neurons that were responsive to movements both of a small
spot and of large-field random dot patterns, 15 ( 54%) neurons had the same directional preferences for both the spot
and the large-field visual motion. The remaining 13 (46%)
neurons responded to target movements in directions opposite to the preferred directions for large-field pattern
movements. An example is shown in Fig. 17B. The neuron
(#17B) preferred rightward movement of a random dot
pattern but preferred leftward movement of a small target.
The increase in the firing rate of the neuron (# 17B) started
with a longer latency than the neuron in Fig. 17A (#16A).
To avoid visual effects during catch-up saccades, a stepramp movement of the target was used in studying these
long-latency neurons. A target projected at 6’ right from
the center of the screen was moved leftward at 30’ /s. The
animal pursued the target without saccadesfor 700 ms. The
firing rate of the neuron increased gradually from 150 to
300 ms and came to a steady level at -300-400 ms after
stimulus onset. As shown in Fig. 17A (#16A), when the
directional preferences to small target movements and
large-field pattern movements were the same, the neurons
started to increase their firing rate before the onset of pursuit eye movements. On the other hand, as the neuron
shown in Fig. 17B demonstrates, when the directional preferences to target movements and large-field pattern movements were opposite, the neurons always increased their
firing rate with long latencies. Furthermore, the temporal
patterns of the neural responses were different. Those neu-
K. KAWANO,
696
l
I
I
#16A
**----- #16A
-#16B
I
.
M. SHIDARA,
0.5cP, 80%)
0.25~/~,1609's)
0.5cP, 803/s)
.
0
1
.
.
100
TIME
(msec)
FIG.
16. Superimposed response profiles using check patterns. Response of a DLPN neuron (#16A) when a check pattern of 0.5 c/ O was
moved at 80’ /s is indicated by a continuous line (n = 20) and that of 0.25
c/ Ocheck pattern at 160’ /s by a dotted line ( yt = 20). Direction of ramps
was downward. Response of another DLPN neuron (# 16B) in the same
animal to the same stimulus (a check pattern of 0.5 c/ Oat 800/s) is indicated by an interrupted line (n = 32). The ocular responses simultaneously
recorded are also shown. Records, from top to bottom, indicate spike density functions of neural responses, average eye velocity profiles, eye acceleration profiles, and the average stimulus velocity profile.
rons with opposite directional preferences for smooth pursuit and ocular following increased their firing rate gradually, and their onset was not clear, making it difficult to
measure latencies by the usual method. To estimate their
response latencies, peristimulus histograms of 10 ms binwidth were constructed (histograms shown in Fig. 17 ) . The
spontaneous firing rate was measured in five bins from the
stimulus onset (O-49 ms), and a threshold for latency detection was set at the mean plus 1 SD. When the firing rates
of two successive bins were above the threshold, we defined
the response onset as being in the first bin of the two. This
criterion was met by 24 neurons, for each of which more
than 10 successful pursuits were performed, and these are
plotted in Fig. 18. It is obvious that neurons with the same
preferred directions ( l ) have shorter latencies than those
with opposite preferred directions (0). If we assume that
the latency of a neuron is the center value of the bin, (for
example, the latency of the neuron is 65 ms when its response onset was in the 60-69 ms bin), the mean latency
was 7 1.4 t 6.3 (SD) ms for neurons with the same preferred
directions and 2 16.0 t 76.3 (SD) ms for those with ODX)O-
AND
S. YAMANE
site preferences. Because neural responses to small targets
and large-field stimuli were analyzed only within 150 ms
after the stimulus onset, only neurons with the same directional preference to both visual stimuli will be further considered.
Low-pass filtering ( 10 Hz), which was used to quantify
the sustained component of the ocular following responses
(cf. Figs. 4 and 5B), was also applied to the neural responses to the small target (shown as dotted lines in Fig. 17,
top) to quantify the temporal structure of the responses.
Filtered response profiles of one neuron (#16A) to stepramp movements of a small target at three speeds ( 10,20,
and 30’ /s) are shown in Fig. 19. In each case, the neuron’s
response peaked within 150 ms of stimulus onset. The initial peak time of the low-pass filtered profiles was measured
in 11 neurons (in 9 neurons at 30’ /s and 2 neurons at
20’ /s) . Saccades were avoided by the use of a step-ramp
target movement. The neural responses peaked within 79149 ms [ 112.8 t 34.4 (SD)] from stimulus onset, and eye
acceleration peaked within 106- 190 ms [ 149 t 28.5 ( SD)].
In contrast, the peak of the eye velocity profile was later
than 400 ms in three cases, and was very late in all cases
[317.9 t 37.2 (SD) ms, n = 81. These results were similar
to those obtained from low-pass filtered profiles of responses to the large-field visual stimulus (Fig. lo), though
peak times in smooth pursuit were 30-40 ms later than
those in ocular following in general. After the peak, the
responses decayed for 200-300 ms and reached a steady
level at ~400 ms after stimulus onset both in the neural
and eye acceleration profiles. At that time, eye acceleration
was near zero and the firing rate (mean, during 400-500 ms
after stimulus onset) was -40% of the peak value [ 38 t 24
(SD)%, n = 111. Effects of different speeds ( 10, 20, and
30° /s) of a small target on the neural responses were studied in four neurons. The amplitude of the peak of the lowpass filtered response increased with increasing stimulus
speed in three neurons, but the remaining neuron (#16A,
Fig. 19) showed saturation at 30’ /s. The temporal structure of the eye acceleration data was similar to that of the
firing rate (Fig. 19). Effects of different speeds on the amplitude of the later response (400-500 ms) were studied in
three neurons with the use of step-ramp movements to
avoid saccadic contamination, and the amplitudes increased with increasing stimulus speed in all cases.
WITH OCULAR FOLLOWING.
Figure 20 compares the responses of neuron # 16A during ocular following
(
) and smooth pursuit eye movement ( -).
When the
random dot pattern was moved downward at 30’ /s, the
firing rate of the neuron increased with a latency of 49 ms
). The latency of the ocular following observed at
(top
the same time was 58 ms. The response of the same neuron
during smooth pursuit is shown by the continuous line.
Both responses, neural and ocular, are the same as those
shown in Fig. 17A, though the time scale is different and
the firing rate is shown by the regular spike density function
in Fig. 20 and by a 10 ms bin histogram and by a low-pass
filtered ( 10 Hz) profile in Fig. 17A. In Fig. 20, the early part
of the responses in Fig. 17A is shown. The response latency
to a small target was 67 ms for the neural response and 83
ms for the ocular resDonse. Increases in the firing rate of the
COMPARISON
l
l
l
l
l
l
DLPN
AND
OCULAR
300
imp/s
FOLLOWING
697
B
,loo
imp/s
s J----------------------------------
1....1....l,...l....l....l....l...~
I....I....l....I....I....I.,,.L,..,
0
s J--------*-------------------------
100
200
300
400
500
600
0
200
100
300
400
500
600
TIME (msec)
TIME (msec)
FIG. 17. Discharge patterns of two DLPN neurons when the animal was pursuing a small target. A : discharge pattern of a
neuron (# 16A) that displayed the same directional preference to target movements and large-field pattern movements. The
target was projected at the center and moved downward at 30’ /s. B: discharge pattern of a neuron (# 17B) that displayed the
opposite directional preference to target movements and large-field pattern movements. The target was projected at 6’ right
from the center and moved leftward at 30’ /s. Records, from top to bottom, indicate peristimulus histograms ( 10 ms
binwidth) of neural responses, average eye velocity profiles, eye acceleration profiles, and average stimulus velocity profile
(A: n = 42; B: n = 20). Dotted lines in top indicate the neural responses after low-pass ( 10 Hz) filtering. A dotted line in the
eye velocity panel (A ) indicates the period when average eye velocity profile is not available because of catch-up saccades.
neuron preceded the onset of eye movements with both
types of stimulation.
Using the standard method, we measured reliable latenties of response to a small target in 9 neurons out of 16 that
displayed the same directional preference to target movements and large-field pattern movements. The relationship
between the latencies of eye movements and neural responses is shown in Fig. 2 1A. The latency of responses to
the spot (0) was always longer than that of the response to
the large-field visual pattern ( 0). The mean difference in
latency was 17.7 t 5.2 ( SD) ms ( n = 9 ) . Neural responses
preceded smooth pursuit eye movement by 15.Ot 5.9 ( SD)
ms (n = 9).
The temporal patterns of the responses of these neurons
to large-field and small spot stimuli were different. As
shown by the responses in Fig. 20, a transient component of
the response, which was usually observed at 45-65 ms after
stimulus onset when the large-field visual stimulus was
used, was not seen when the small spot was used. Because
the temporal firing patterns and latencies were different, we
measured the peak amplitude of the firing rate in both cases
after low-pass filtering, for a quantitative comparison, in-
stead of using other stimulus-locked measurements. Amplitudes of the firing rate and eye acceleration at the peak of
the low-pass filtered responses in Fig. 19 were measured
and plotted in Fig. 2 1B (A) with those of responses to a
8-
2
7 es
3 _
5
f+LLJ
M f 27 _
o-,
50
0
00
00
00
00
00
0.0
1 1
60
70
l
same
0
OPPO.
0
0
1
1
80
90
0
0
00000
1 1 1
0
1
1
J
100 150 200 250 300 350
LATENCY(ms)
18. Distribution of latencies for 24 DLPN neurons with the use of
a small spot as a target of pursuit. Neurons that displayed the same directional preference to target movements and large-field pattern movements
are indicated by closed circles, and those that displayed the opposite by
open circles.
FIG.
K. KAWANO,
698
M. SHIDARA,
large-field visual stimulus (at stimulus speeds lo- 160° /s,
A). The amplitudes of the peak of both neural and ocular
responses did not vary much when the visual stimulus was a
small spot (Figs. 2 1B and 19), ranging between 160 and
200 imp/s for the neural responses and between 80 and
120’ / s2 for eye acceleration. On the other hand, much
higher eye acceleration ( 2350°/s2) was observed for the
same range of firing rate when the visual stimulus was a
large-field random dot pattern. It is evident that the relationship between firing rate and eye acceleration differed
between ocular following and smooth pursuit. This result is
consistent with those in the same analysis on the other two
neurons, the responses of which to a small target were studied at three speeds (circles and squares).
The correlation between the response amplitudes to a
small target and a large-field stimulus moving at a same
speed was studied in 15 neurons. The response to the same
speed for each stimulus was selected for comparison, and its
amplitude was measured at the initial peak of the low-pass
filtered profile. It is evident from Fig. 2 1C that response
amplitudes to a small spot and to a large-field stimulus did
not correlate (r = 0.07).
,.‘.
*/
.
-.
-.
-. 44
-.
\
-.
-.
-.-. \
AND
S. YAMANE
300
Imp/s
-
- 200
imp/s
l.
‘.
l .
l.
-.
-\
-.
- .. . . . . .
\
l .. .
=--
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l.
-.
- 100
\
--a
-. - . . . . . . . .*-•--0-...
I
I
100
0
TIME
/
..
..
smooth w-suit
t ____-_-----------------
loo/s
.. .... .. 2(fys
el
/<;;;;
-3o"/s
-'O
-20
- O/s
.-
-10
(msec)
FIG. 20. Comparison of response patterns of a DLPN neuron (#16A)
to a large-field visual stimulus (a . . , n = 40) and to a small spot (-,
y1=
42). The eye movement elicited was ocular following (. . . ) and smooth
pursuit (-),
respectively. In each case, visual stimulus was moved
downward at 30° 1s. Records, from top to bottom, indicate spike density
functions of neural responses, eye velocity profiles, eye acceleration profiles, and average stimulus velocity profile.
DISCUSSION
-0
0
2
100
..
e
:
0
.
S J ----------------------I.,,
0
I
I.,
100
I
I
Short-latency neural activities in the DLPN
I
200
TIME
I,
I1
I
300
I1
L
I
I
I1
I
400
(msec)
FIG. 19. Effects of changes in speed of a small target on response profiles of a DLPN neuron (#16A) and on simultaneously recorded smooth
pursuit eye movements. Speed of test ramps (n = 42 for each) : 10 (-),
20(*
l ), and 3O”/s (- - -). All response profiles (spike density function
of the firing rate, eye velocity, and eye acceleration ) are shown after lowpass ( 10 Hz) filtering. Records, from top to bottom, indicate spike density
functions of neural responses, eye velocity profiles, eye acceleration profiles, and average stimulus velocity profile (one stimulus speed).
l
The results of the present study are consistent with the
idea that the DLPN is involved in the generation of shortlatency ocular following responses. First, we have shown
that sudden movements of the visual scene elicit short-latency neural responses in the DLPN that preceded the onset
of eye movements. Second, we have found that the firing
patterns of DLPN neurons have properties similar to ocular
following responses simultaneously elicited by various visual stimuli.
Previous studies showed that neurons in the DLPN respond to the movement of a large-field visual stimulus
(Mustari et al. 1988; Suzuki and Keller 1984; Suzuki et al.
1990; Thier et al. 1988). Response latencies of the DLPN
neurons in these studies, however, did not seem short
enough to elicit short-latency ocular following responses.
Thier et al. ( 1988) reported that the latency for the directional response of DLPN neurons varied between 70 and 80
ms. Suzuki et al. ( 1990) reported that response latencies
averaged 98 ms and ranged from 40 to 200 ms, with most
DLPN
.*.*.*
.*
:
:
.*.*..*
.*
.*.*
‘O”: A
AND
OCULAR
FOLLOWING
-
*om
A00
OFR
sf’
l
0
l
0
t
OFR
0 SP
l
LATENCY
[neuron]
(msec)
0
l
FIRING
RATE
(impkec)
SMOOTH
PURSUIT
(imphec)
FIG. 2 1. A : relation between latencies of neural responses and those of eye movements for nine neurons, the responses of
which to both target movements and large-field pattern movements displayed the same directional preference. l , data points
measured from responses to a large-field visual stimulus; 0, those to a small spot. The dotted line passing through the origin
has a slope of 1. OFR, ocular following response; SP, smooth pursuit. B: relationships between firing rate and eye acceleration at the peak of the low-pass ( 10 Hz) filtered responses of three neurons (#16A, AA; #2 1B, 00; and #2 1C, m ) for ocular
following (closed symbols) and smooth pursuit (open symbols). C: comparison between response amplitudes at the peak of
the low-pass ( 10 Hz) filtered firing rate to a small target
stimulus at the same speed ( 20° /s for 5 neurons,
- and a large-field
30’ /s for 10 neurons).
(20/28 ) between 60 and 120 ms and only one before 50 ms.
Because the latency of ocular following responses is -5O60 ms in both A4. mulatta and M. fuscata, almost all neural
activities in the DLPN in these studies seem to start after
the onset of eye movement. However, in the experimental
paradigms of these previous studies, the animal always fixated a stationary target while a large-field visual stimulus
was moved. When we used experimental paradigms that
did not require the animal to fixate a stationary target during visual stimulation, we recorded very short-latency responses in DLPN neurons. About 70% of DLPN neurons
were activated ~50 ms after the onset of the stimulus motion, and 30% increased their firing rate > 10 ms before the
eye movements, raising the possibility that neurons in the
DLPN could elicit ocular following responses. It is known
that attention changes the responsiveness and selectivity of
neurons (Mountcastle et al. 1987). Besides the visual properties of the stimuli used (e.g., distance between the screen
and the animal, size of random dots), attention to the fixation point is also one of the possible reasons for the difference in response latencies.
Response properties of DLPN neurons to large-field visual motion during fixation have been studied in detail (Suzuki et al. 1990). The direction selectivity and direction
tuning of the neurons we recorded were similar to those
reported by Suzuki and colleagues. The average half-height,
full-width tuning bandwidth was 116’ in the present study
and 106’ in their study ( step response in their paper). Interestingly, it appears that there is a difference between the two
data sets in speed tuning. The speed tuning generally saturated above 20’ /s in the study of Suzuki et al. ( 1990)) but
we found that most DLPN neurons (>70%) had strongest
responses at high stimulus speeds ( 280” / s) . One possible
explanation for the difference could be our methods of initially screening neurons (i.e., using a stimulus speed of 80’ /
s). However, there may be other explanations, because the
neurons observed by Suzuki et al. ( 1990) typically responded to fast stimuli (80 and 100° /s) with firing rates
sufficient to be detected by our screening methods (cf. Fig.
15 in Suzuki et al. 1990). The difference is therefore more
likely to be due to different stimulus conditions (i.e., with
or without a fixation point, distance between the screen and
the animal, temporal frequency contents of the stimulus)
and to different methods of estimation of response amplitude. The response amplitudes were measured for 20-50
ms for the transient and for 500- 1000 ms for the step components in Suzuki et al. ( 1990). Because we did not examine responses > 150 ms after stimulus onset, so as to avoid
contamination by saccades, we did not provide measurements corresponding to the step components of Suzuki et
al. Becausethe initial component in our study started immediately after the response onset and lasted only -20 ms, the
transient component in Suzuki et al. ( 1990) probably
corresponds to the total estimation of our initial and sustained components. We have shown that the response amplitude of both initial and sustained components increased
as a linear function of the logarithm of stimulus speed
140” /s. We used the logarithm of stimulus speed because it
has been reported that the amplitude of ocular following
responses also increases in proportion to the logarithm of
stimulus speed (Miles et al. 1986). If we calculate the correlation between response amplitudes and “raw” stimulus
speeds instead of their logarithm, it is obvious that saturation (separation from a linear line) of both the firing rate of
the neuron #02A and the average firing rate of the 87 neurons will start at speeds ~40” /s (Fig. 6A). Furthermore,
Suzuki and colleagues also noted that the average response
of DLPN neurons was elevated for speeds>24” /s, in agreement with our results.
Possible source of the visual inputs to the DLPN neurons
The middle temporal area and the medial superior temporal (MST) area both send strong projections to the
DLPN (Brodal 1978; Glickstein et al. 1980, 1985; Maunsell
and van Essen 1983; May and Andersen 1986; Ungerleider
700
K. KAWANO,
M. SHIDARA,
et al. 1984). Previous studies have described neurons in the
posterior part of the posterior parietal cortex, in the bank of
the superior temporal sulcus, that are activated by movements of the entire visual field both with and without a
fixation target (Kawano and Sasaki 198 1, 1984; Kawano et
al. 1984). Results of recent studies (Desimone and Ungerleider 1986; Komatsu and Wurtz 1988a,b; Tanaka et al.
1986) suggest that this area corresponds to the MST area.
Our own recent study has revealed that most of the neurons
in the MST area increase their firing rate with very short
latencies to movements of a large-field visual stimulus (Kawano et al. 1989, 1990b, unpublished observations). Their
response properties to the moving visual scene are similar
to those of the DLPN neurons, which means that they are
also similar to those of ocular following responses. Collectively, these observations suggest that the MST area is likely
to be a prominent source of the input to DLPN neurons.
It has been reported by Mustari et al. ( 1988) that there
are neurons in the DLPN receiving eye movement (extraretinal) signals. It is possible that the DLPN neurons in our
study also receive extraretinal inputs that cause them to
discharge in relation to eye movements. However, we do
not think that extraretinal signals have a significant influence, at least on the initial responses described here, for the
following reasons. The most likely source of extraretinal
signals on the DLPN neurons has been considered the MST
area (Mustari et al. 1988 ) . The study of extraretinal signals
in the MST area has shown that the effects of eye movement generally are observed only 50 ms after the onset of
the pursuit eye movements (Newsome et al. 1988). The
latency of ocular following responses is generally -50-60
ms, and the initial neural responses start before the onset of
the eye movements and last -20 ms. Thus the initial responses of the DLPN neurons cannot be influenced by extraretinal signals derived from the MST area. On the other
hand, it is possible that extraretinal signals influence the
sustained component of the neural response, causing the
differences in speed tuning data between the current study
and that of Suzuki et al. ( 1990).
Close correlation between neural and ocular responses
Manipulation of the visual stimuli revealed that the responses of DLPN neurons have similar properties to simultaneously elicited ocular following responses.
AMPLITUDES
OF RESPONSES.
It has been reported that the
magnitude of ocular following responses depends on stimulus speed and generally increases as a linear function of the
logarithm of stimulus speed 540” /s when a random dot
pattern is used as a stimulus (Miles et al. 1986). Although
the preferred speedsin DLPN vary from neuron to neuron,
the majority of DLPN neurons prefer high stimulus speeds
( 80- 160° /s) , and the average firing rate of the 87 neurons
studied increased as a linear function of the logarithm of
stimulus speed 140” /s (Fig. 6A, dotted lines). Thus, despite the difference in speed tuning of each neuron, the
mean response amplitude of DLPN neurons had similar
dependence on stimulus speed to the ocular following responses when random dots were used.
Using sine wave gratings, Miles et al. ( 1986) found that
the amplitude of ocular following increases with the temporal frequency of the stimulus, reaches a peak at -20 Hz,
AND
S. YAMANE
and decreases with higher temporal frequency. The same
dependence on the visual stimulus has been observed in the
responses of DLPN neurons when check patterns were used
(Fig. 15 ) . Although the square-wave check patterns contain
a wide range of spatial frequencies in terms of a Fourier
decomposition of the stimulus, the consistency of temporal
frequency preferences when two different sizes of squares
were used suggeststhat the temporal frequency is the most
likely to determine the response preference.
If the responses observed in DLPN neurons during largefield visual stimulation were visual, it must be expected that
there would be differences between neural responses and
ocular responses, depending on the visual properties of
each neuron. This may be exemplified by the result that
when the visual stimuli were outside the receptive field of a
neuron, no responses were observed, despite the occurrences of ocular responses. There were some different effects on response amplitude between neural and ocular responses when blurred random dots were used. Blurring the
random dot pattern progressively reduced the amplitudes
of both ocular responses and neural responses. On the other
hand, its effect was more pronounced in ocular responses
than in neural responses ( Fig. 13). One possible explanation for this is that the spatial frequency dependence of the
neurons studied may not have corresponded to that of the
whole ocular following system.
The relationship between the amplitudes of neural and
ocular responses differed for initial and sustained responses
in each neuron (Fig. 11). If we assume that neural responses are induced by eye movements, the relationship
between firing rate and eye movement should be consistent
both in initial and sustained components. However, our
results are not consistent with this assumption, and instead
support the idea that neural responses are visual and provide inputs that cause the eyes to move. The different responsiveness in neurons during ocular following is likely to
depend on their visual properties. These differences may
cancel when they are summed together in the downstream
structures that mediate eye movements (cerebellum and/
or brain stem).
CORRELATION
BETWEEN RESPONSES.
For each
neuron there were some discrepancies between the amplitude of ocular responses and the firing rate depending on its
visual properties, e.g., receptive field, speed tuning, etc. Nevertheless, similar dependencies on properties of visual stimulus between ocular and neural response latencies were generally observed in every DLPN neuron. Increasing the stimulus speed decreased and blurring the random dot pattern
increased latencies not only of ocular following, as reported
previously (Miles et al. 1986), but also of neural responses
in the DLPN. Furthermore, time differences between
neural and ocular latency were relatively independent of
the stimulus that elicited the responses. Similar temporal
correlation was also observed between initial peak times of
ocular and neural responses.
Blanking a large-field visual stimulus reduced the firing
rates of DLPN neurons. The decay of neural responses also
preceded the decay of the ocular following responses by the
same delay that was observed at response onset. This close
correlation was also observed when we presented check patterns instead of the random dot pattern. Thus the firing
TEMPORAL
DLPN
AND
OCULAR
FOLLOWING
701
pattern of DLPN neurons is closely correlated to the eye
movement profiles over a wide range of conditions.
Lisberger et al. 1987). Our findings regarding neural responses in the DLPN to a small moving target are consistent with those of other studies (Mustari et al. 1988; Suzuki
Possible role of the DLPN neurons in the regulation of
and Keller 1984; Suzuki et al. 1990; Thier et al. 1988). We
ocular following responses
have shown that there are two groups of neurons that discharged in relation to smooth pursuit eye movements. One
It has been reported that chemical lesions in the DLPN
produce deficits in the initiation of optokinetic responses group of neurons had the same directional preference to a
(May et al. 1988 ) , which seem to be closely related to ocu- small spot and a large-field visual stimulus, and the other
had opposite preferences. Response latencies to the movelar following responses. Furthermore, lidocaine injections
in the DLPN produce a decrement in ipsilateral and verti- ment of a small target were short in the former and long in
cal ocular following responses (Kawano et al. 1990a). the latter. Because the neurons in the latter group reThese results and those from the current study suggest a sponded for opposite directions of spot and large-field morole for the DLPN neurons in the mediation of ocular fol- tion, it is possible that their increase in firing rate may be
lowing. It is still unknown why chemical lesions in the delayed by visual input from the dim background, which
DLPN affect directional preference on ipsilateral ocular fol- would provide image motion starting at the initiation of
lowing and smooth pursuit even though no significant dif- pursuit opposite to its direction. We studied responses of
these neurons in the dark, but we cannot exclude the possiferences are seen in directional preferences of the DLPN
bility that the dark-adapted animal could see the back;
neurons. Further experiments, such as investigating
whether there is a selective projection from a subset of ground. Because we are interested in neural activities that
DLPN neurons with a uniform directional preference, will are related to initiation of eye movements, we did not attempt to study neurons with long latencies further. The
be needed to answer this question.
neurons in the group that responded for the spot and largeThe DLPN sends projections to the cerebellum, mainly
field motion of the same direction increased their firing rate
into the flocculus (paraflocculus), and lobules VI and VII
of the vermis (Brodal 1979, 1982; Langer et al. 1985). In before the onset of smooth pursuit. We observed a similar
both areas, there are neural responses related to the move- relationship between their response latencies to a small tarment of large-field visual stimuli (Biittner and Waespe get and eye movements (smooth pursuit) to that observed
1984; Kase et al. 1979; Lisberger and Fuchs 1978; Miles between their response latencies to large-field visual stimuli
and Fuller 1975; Miles et al. 1980; Noda and Suzuki and eye movements (ocular following). Furthermore, the
1979a,b; Noda and Warabi 1987; Stone and Lisberger temporal structures of the firing rate of these neurons were
1990; Suzuki and Keller 1988; Suzuki et al. 198 1; Waespe similar to those of simultaneously recorded eye acceleration profiles during both smooth pursuit and ocular followand Henn 1981; Waespe et al. 1985). Floccular Purkinje
ing
(after low-pass filtering). These results suggest that their
cells are known to be active during optokinetic stimulation
activities
are probably related not only to ocular following
both with and without a fixation point (Waespe and Henn
1981; Waespe et al. 1985). Waespe et al. (1985) measured responses but also to smooth pursuit eye movements. It has
been suggested that Purkinje cells in the flocculus use visual
the response latency of floccular Purkinje cells to optokimotion inputs to form part of the command that translates
netic stimulation without a fixation point, eliciting optokichanges
in target motion into smooth eye acceleration
netic nystagmus, and obtained an average latency of 82 ms
(Stone and Lisberger 1990). The discharge pattern of these
(range 40- 120 ms). They used a rotating striped cylinder
( 124 cm diam) as the optokinetic stimulation and got an DLPN neurons might provide the visual motion inputs to
average latency of eye movements of 135 ms. When a large- the floccular / parafloccular Purkinje cells.
field random dot pattern was projected and moved on a
near viewing screen in our recent preliminary study, 70% of
We are grateful to Drs. J. Yokota and Y. Watanabe for their participaPurkinje cells in the ventral paraflocculus increased their tion in a part of these experiments. We are also grateful Drs. M. P. Young
J. H. R. Maunsell for their valuable suggestions for improving the
firing rate before the onset of eye movements, i.e., ocular and
English. We thank Drs. S. Kaji and H. Komatsu for their valuable advice,
following ( Shidara and Kawano 199 1) . Furthermore,
M. Okui for histology, and T. Ogamino for secretarial assistance.
Miles et al. ( 1986) suggested a possible role of the flocculus
This research was supported by grants from the Science and Technology
and paraflocculus, referring to their preliminary observa- Agency and Human Frontier Science Program.
Address for reprint requests: K. Kawano, Neuroscience Section, Electrotions in ablation studies. After bilateral removal of the flocculus and paraflocculus, an almost total loss of short-la- technical Laboratory, l-l-4, Umezono, Tsukubashi, Ibaraki 305, Japan.
tency ocular following responses was observed. Together Received 12 November 1990; accepted in final form 7 November 199 1.
with these previous studies, our current results suggest that
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