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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 680 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: DLPN ,400 : - w k qn : u o Q) CM z\ b-l Q 300 : -200 : - UE H8f-l -100 lLw . -0 .: . . .. *. : ‘&*.*.-.; . -*.. .Y _ . . .- .*.,..t;*.. . .- .-a. 43ms \ -25 - . - - . . . . Z. ; -i * -* \’ : .. ;‘;. : . ‘.% . . . .-.*..;. 1,s , 0 . . . . . 100 . . I AND I 200 TIME (msec) R imp/see OCULAR FOLLOWING 683 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. l UP con* N - 100 ipsi r-a down J Il~~llJl~lllllIJ -100 -90 NORMALIZED 180 DIiECTIO; 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 AND OCULAR 685 FOLLOWING imp/s B 400 spike -**m-... after density function Low-pass filtering 300 200 100 - +-------------- O- SJI I 0 100 TIME (msec) ------t 0 . . -----. . I . . . 100 . I. 200 * ., TIME (msec) 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 ”I J . . l . . .I . I * ‘w ‘. . . :’ . . . * . .I imp/s 7400 ,.... t 49ms 5ims ’ . . . . . ..“...... ’ 45ms *-. l....l....J 0 0 100 la...l....J 100 0 200 100 <msec> TIME B 49ms 47ms 0 w - I..,.1 41.,*.1,,..) .l..J 0 100 0 100 0 250 100 I.---l-.--J l....l....j 0 100 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 .. . =-- l ... 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 one of the possible streams of information related to short- REFERENCES latency ocular following responses may originate from the P. The corticopontine projection in the rhesus monkey: origin cortical area MST and be relayed by the DLPN neurons to BRODAL, and principles of organization. Brain 10 1: 25 l-283, 1978. the oculomotor centers via floccular and/ or parafloccular BRODAL, P. 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