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S G Lisberger

Publications and source records attributed to S G Lisberger.

At least 19 recordsLinked to original sources
PubMed2002

Partial ablations of the flocculus and ventral paraflocculus in monkeys cause linked deficits in smooth pursuit eye movements and adaptive modification of the VOR.

The vestibuloocular reflex (VOR) generates compensatory eye movements to stabilize visual images on the retina during head movements. The amplitude of the reflex is calibrated continuously throughout life and undergoes adaptation, also called motor learning, when head movements are persistently associated with image motion. Although the floccular-complex of the cerebellum is necessary for VOR adaptation, it is not known whether this function is localized in its anterior or posterior portions, which comprise the ventral paraflocculus and flocculus, respectively. The present paper reports the effects of partial lesions of the floccular-complex in five macaque monkeys, made either surgically or with stereotaxic injection of 3-nitropropionic acid (3-NP). Before and after the lesions, smooth pursuit eye movements were tested during sinusoidal and step-ramp target motion. Cancellation of the VOR was tested by moving a target exactly with the monkey during sinusoidal head rotation. The control VOR was tested during sinusoidal head rotation in the dark and during 30 degrees/s pulses of head velocity. VOR adaptation was studied by having the monkeys wear x2 or x0.25 optics for 4-7 days. In two monkeys, bilateral lesions removed all of the flocculus except for parts of folia 1 and 2 but did not produce any deficits in smooth pursuit, VOR adaptation, or VOR cancellation. We conclude that the flocculus alone probably is not necessary for either pursuit or VOR learning. In two monkeys, unilateral lesions including a large fraction of the ventral paraflocculus produced small deficits in horizontal and vertical smooth pursuit, and mild impairments of VOR adaptation and VOR cancellation. We conclude that the ventral paraflocculus contributes to both behaviors. In one monkey, a bilateral lesion of the flocculus and ventral paraflocculus produced severe deficits smooth pursuit and VOR cancellation, and a complete loss of VOR adaptation. Considering all five cases together, there was a strong correlation between the size of the deficits in VOR learning and pursuit. We found the strongest correlation between the behavior deficits and the size of the lesion of the ventral paraflocculus, a weaker but significant correlation for the full floccular complex, and no correlation with the size of the lesion of the flocculus. We conclude that 1) lesions of the floccular complex cause linked deficits in smooth pursuit and VOR adaptation, and 2) the relevant portions of the structure are primarily in the ventral paraflocculus, although the flocculus may participate.

Adaptation, Physiological↗
PubMed2001

Shifts in the population response in the middle temporal visual area parallel perceptual and motor illusions produced by apparent motion.

We recorded behavioral, perceptual, and neural responses to targets that provided apparent visual motion consisting of a sequence of stationary flashes. Increasing the flash separation degrades the quality of motion, but for some separations evoked larger smooth pursuit responses from both humans and monkeys than did smooth motion. The same flash separations also produced an increase in perceived speed in humans. Recordings from single neurons in the middle temporal visual area (MT) of awake monkeys revealed a potential basis for the illusion in the population response. Apparent motion produced diminished neural responses relative to smooth motion. However, neurons with slow preferred speeds were more affected than were those with fast preferred speeds. Increasing the flash separation thus caused the population response to become diminished in amplitude and to shift so that the most active neurons had higher preferred speeds. The entire constellation of effects of apparent motion on the magnitude and latency of the initial pursuit response was accounted for if the MT population response was decoded by (1) creating an opponent motion signal for each neuron by treating its preferred and opposite direction responses as those of a pair of oppositely tuned neurons and (2) computing the vector average of these opponent motion signals. Other ways of decoding the population response recorded in MT failed to account for one or more aspects of behavior. We conclude that the effects of apparent motion on both pursuit and perception can be accounted for if target speed is estimated from the MT population response by a neural computation that implements a vector average based on opponent motion.

Action Potentials↗
PubMed2001

Reconstruction of target speed for the guidance of pursuit eye movements.

We studied how object speed is reconstructed from the responses of motion-selective cells for the generation of a behavior that is tightly linked to the speed of visual motion. In theory, the speed of an object could be estimated either from the speed tuning of the active population of motion-selective cells or from the rate of displacement of activation across the cortical map of visual space. We measured the pursuit eye movements evoked by stimuli containing two conflicting motion components: a local component designed to excite motion-selective cells with a particular speed tuning and a displacement component designed to excite cells with a sequence of spatial receptive fields. Pursuit eye movements were driven primarily by the local-motion component and were affected to only a small degree by the rate of target displacement across visual space. Extracellular single-unit recordings using the same stimuli revealed that the responses of cells in the middle temporal visual area (MT) depended primarily on the local-motion component but were influenced by the displacement component to the same degree as were pursuit eye movements. We conclude that the initiation of pursuit is consistent with a reconstruction of target speed based on the speed tuning of the active population of MT cells.

Acceleration↗
PubMed2001

Linked target selection for saccadic and smooth pursuit eye movements.

In natural situations, motor activity must often choose a single target when multiple distractors are present. The present paper asks how primate smooth pursuit eye movements choose targets, by analysis of a natural target-selection task. Monkeys tracked two targets that started 1.5 degrees eccentric and moved in different directions (up, right, down, and left) toward the position of fixation. As expected from previous results, the smooth pursuit before the first saccade reflected a vector average of the responses to the two target motions individually. However, post-saccadic smooth eye velocity showed enhancement that was spatially selective for the motion at the endpoint of the saccade. If the saccade endpoint was close to one of the two targets, creating a targeting saccade, then pursuit was selectively enhanced for the visual motion of that target and suppressed for the other target. If the endpoint landed between the two targets, creating an averaging saccade, then post-saccadic smooth eye velocity also reflected a vector average of the two target motions. Saccades with latencies >200 msec were almost always targeting saccades. However, pursuit did not transition from vector-averaging to target-selecting until the occurrence of a saccade, even when saccade latencies were >300 msec. Thus, our data demonstrate that post-saccadic enhancement of pursuit is spatially selective and that noncued target selection for pursuit is time-locked to the occurrence of a saccade. This raises the possibility that the motor commands for saccades play a causal role, not only in enhancing visuomotor transmission for pursuit but also in choosing a target for pursuit.

Animals↗
PubMed2001

Regulation of the gain of visually guided smooth-pursuit eye movements by frontal cortex.

In studies of the neural mechanisms giving rise to behaviour, changes in the neural and behavioural responses produced by a given stimulus have been widely reported. This 'gain control' can boost the responses to sensory inputs that are particularly relevant, select among reflexes for execution by motoneurons or emphasize specific movement targets. Gain control is also an integral part of the smooth-pursuit eye movement system. One signature of gain control is that a brief perturbation of a stationary target during fixation causes tiny eye movements, whereas the same perturbation of a moving target during the active state of accurate pursuit causes large responses. Here we show that electrical stimulation of the smooth-pursuit eye movement region in the arcuate sulcus of the frontal lobe ('the frontal pursuit area', FPA) mimics the active state of pursuit. Such stimulation enhances the response to a brief perturbation of target motion, regardless of the direction of motion. We postulate that the FPA sets the gain of pursuit, thereby participating in target selection for pursuit.

Animals↗
PubMed2001

Experimental and computational analysis of monkey smooth pursuit eye movements.

Smooth pursuit eye movements are guided by visual feedback and are surprisingly accurate despite the time delay between visual input and motor output. Previous models have reproduced the accuracy of pursuit either by using elaborate visual signals or by adding sources of motor feedback. Our goal was to constrain what types of signals drive pursuit by obtaining data that would discriminate between these two modeling approaches, represented by the "image motion model" and the "tachometer feedback" model. Our first set of experiments probed the visual properties of pursuit with brief square-pulse and sine-wave perturbations of target velocity. Responses to pulse perturbations increased almost linearly with pulse amplitude, while responses to sine wave perturbations showed strong saturation with increasing stimulus amplitude. The response to sine wave perturbations was strongly dependent on the baseline image velocity at the time of the perturbation. Responses were much smaller if baseline image velocity was naturally large, or was artificially increased by superimposing sine waves on pulse perturbations. The image motion model, but not the tachometer feedback model, could reproduce these features of pursuit. We used a revision of the image motion model that was, like the original, sensitive to both image velocity and image acceleration. Due to a saturating nonlinearity, the sensitivity to image acceleration declined with increasing image velocity. Inclusion of this nonlinearity was motivated by our experimental results, was critical in accounting for the responses to perturbations, and provided an explanation for the unexpected stability of pursuit in the presence of perturbations near the resonant frequency. As an emergent property, the revised image motion model was able to reproduce the frequency and damping of oscillations recorded during artificial feedback delays. Our second set of experiments replicated prior recordings of pursuit responses to multiple-cycle sine wave perturbations, presented over a range of frequencies. The image motion model was able to reproduce the responses to sine wave perturbations across all frequencies, while the tachometer feedback model failed at high frequencies. These failures resulted from the absence of image acceleration signals in the tachometer model. We conclude that visual signals related to image acceleration are important in driving pursuit eye movements and that the nonlinearity of these signals provides stability. Smooth pursuit thus illustrates that a plausible neural strategy for combating natural delays in sensory feedback is to employ information about the derivative of the sensory input.

Animals↗
PubMed2000

Apparent motion produces multiple deficits in visually guided smooth pursuit eye movements of monkeys.

We used apparent motion targets to explore how degraded visual motion alters smooth pursuit eye movements. Apparent motion targets consisted of brief stationary flashes with a spatial separation (Deltax), temporal separation (Deltat), and apparent target velocity equal to Deltax/Deltat. Changes in pursuit initiation were readily observed when holding target velocity constant and increasing the flash separation. As flash separation increased, the first deficit observed was an increase in the latency to peak eye acceleration. Also seen was a paradoxical increase in initial eye acceleration. Further increases in the flash separation produced larger increases in latency and resulted in decreased eye acceleration. By varying target velocity, we were able to discern that the visual inputs driving pursuit initiation show both temporal and spatial limits. For target velocities above 4-8 degrees /s, deficits in the initiation of pursuit were seen when Deltax exceeded 0.2-0.5 degrees, even when Deltat was small. For target velocities below 4-8 degrees /s, deficits appeared when Deltat exceeded 32-64 ms, even when Deltax was small. Further experiments were designed to determine whether the spatial limit varied as retinal and extra-retinal factors changed. Varying the initial retinal position of the target for motion at 18 degrees /s revealed that the spatial limit increased as a function of retinal eccentricity. We then employed targets that increased velocity twice, once from fixation and again during pursuit. These experiments revealed that, as expected, the spatial limit is expressed in terms of the flash separation on the retina. The spatial limit is uninfluenced by either eye velocity or the absolute velocity of the target. These experiments also demonstrate that "initiation" deficits can be observed during ongoing pursuit, and are thus not deficits in initiation per se. We conclude that such deficits result from degradation of the retino-centric motion signals that drive pursuit eye acceleration. For large flash separations, we also observed deficits in the maintenance of pursuit: sustained eye velocity failed to match the constant apparent target velocity. Deficits in the maintenance of pursuit depended on both target velocity and Deltat and did not result simply from a failure of degraded image motion signals to drive eye acceleration. We argue that such deficits result from a low gain in the eye velocity memory that normally supports the maintenance of pursuit. This low gain may appear because visual inputs are so degraded that the transition from fixation to tracking is incomplete.

Acceleration↗
PubMed2000

Context-dependent smooth eye movements evoked by stationary visual stimuli in trained monkeys.

The appearance of a stationary but irrelevant cue triggers a smooth eye movement away from the position of the cue in monkeys that have been trained extensively to smoothly track the motion of moving targets while not making saccades to the stationary cue. We have analyzed the parameters that regulate the size of the cue-evoked smooth eye movement and examined whether presentation of the cue changes the initiation of pursuit for subsequent steps of target velocity. Cues evoked smooth eye movements in blocks of target motions that required smooth pursuit to moving targets, but evoked much smaller smooth eye movements in blocks that required saccades to stationary targets. The direction of the cue-evoked eye movement was always opposite to the position of the cue and did not depend on whether subsequent target motion was toward or away from the position of fixation. The latency of the cue-evoked smooth eye movement was near 100 ms and was slightly longer than the latency of pursuit for target motion away from the position of fixation. The size of the cue-evoked smooth eye movement was as large as 10 degrees /s and decreased as functions of the eccentricity of the cue and the illumination of the experimental room. To study the initiation of pursuit in the wake of the cues, we used bilateral cues at equal eccentricities to the right and left of the position of fixation. These evoked smaller eye velocities that were consistent with vector averaging of the responses to each cue. In the wake of bilateral cues, the initiation of pursuit was enhanced for target motion away from the position of fixation, but not for target motion toward the position of fixation. We suggest that the cue-evoked smooth eye movement is related to a previously postulated on-line gain control for pursuit, and that it is a side-effect of sudden activation of the gain-controlling element.

Animals↗
PubMed2000

Changes in the responses of Purkinje cells in the floccular complex of monkeys after motor learning in smooth pursuit eye movements.

We followed simple- and complex-spike firing of Purkinje cells (PCs) in the floccular complex of the cerebellum through learned modifications of the pursuit eye movements of two monkeys. Learning was induced by double steps of target speed in which initially stationary targets move at a "learning" speed for 100 ms and then change to either a higher or lower speed in the same direction. In randomly interleaved control trials, targets moved at the learning speed in the opposite direction. When the learning direction was the ON direction for simple-spike responses, learning was associated with statistically significant changes in simple-spike firing for 10 of 32 PCs. Of the 10 PCs that showed significant expressions of learning, 8 showed changes in simple-spike output in the expected direction: increased or decreased firing when eye acceleration increased or decreased through learning. There were no statistically significant changes in simple-spike responses or eye acceleration during pursuit in the control direction. When the learning direction was in the OFF direction for simple-spike responses, none of 15 PCs showed significant correlates of learning. Although changes in simple-spike firing were recorded in only a subset of PCs, analysis of the population response showed that the same relationship between population firing and eye acceleration obtained before and after learning. Thus learning is associated with changes that render the modified population response appropriate to drive the changed behavior. To analyze complex-spike firing during learning we correlated complex-spike firing in the second, third, and fourth 100 ms after the onset of target motion with the retinal image motion in the previous 100 ms. Data were largely consistent with previous evidence that image motion drives complex spikes with a direction selectivity opposite that for simple spikes. Comparison of complex-spike responses at different times after the onset of control and learning target motions in the learning direction implied that complex spikes could guide learning during decreases but not increases in eye acceleration. Learning caused increases or decreases in the sensitivity of complex spikes to image motion in parallel with changes in eye acceleration. Complex-spike responses were similar in all PCs, including many in which learning did not modify simple-spike responses. Our data do not disprove current theories of cerebellar learning but suggest that these theories would have to be modified to account for simple- and complex-spike firing of floccular Purkinje cells reported here.

Action Potentials↗
PubMed1999

Vector averaging occurs downstream from learning in smooth pursuit eye movements of monkeys.

How are sensory-motor transformations organized in a cortical motor system? In general, sensory information is transformed through a variety of signal processing operations in the context of distinct coordinate frameworks. We studied the interaction of two distinct operations in pursuit eye movements, learning and vector-averaging, to gain insight into their underlying coordinate frameworks and their sequence in sensory-motor processing. Learning was induced in the initiation of pursuit eye movements by targets that moved initially at one speed for 100 msec and then increased or decreased to a sustained final speed. Vector averaging was studied by comparing the initial eye acceleration evoked by the simultaneous motion of two targets with that evoked by each target singly. Learning caused specific effects on the direction of the vector-averaged responses to two-target stimuli that included one target moving in the direction used to induce learning. Learned increases or decreases in eye acceleration caused the direction of the responses to two-targets to rotate toward or away from the learning direction. Learning also caused nonspecific changes in the responses to two-target stimuli. After any learning protocol, two-target responses usually became smaller, and their directions rotated away from the axis of the target motion used for learning. Quantitative analysis showed that the specific effects of learning were predicted most closely by a model in which vector averaging occurs downstream from the site(s) of learning. We suggest that the pursuit system creates parallel commands for potential movements to each of the targets in two-target stimuli, and that learning occurs in the coordinates of the potential movements.

Animals↗
PubMed1999

Visual motion analysis for pursuit eye movements in area MT of macaque monkeys.

We asked whether the dynamics of target motion are represented in visual area MT and how information about image velocity and acceleration might be extracted from the population responses in area MT for use in motor control. The time course of MT neuron responses was recorded in anesthetized macaque monkeys during target motions that covered the range of dynamics normally seen during smooth pursuit eye movements. When the target motion provided steps of target speed, MT neurons showed a continuum from purely tonic responses to those with large transient pulses of firing at the onset of motion. Cells with large transient responses for steps of target speed also had larger responses for smooth accelerations than for decelerations through the same range of target speeds. Condition-test experiments with pairs of 64 msec pulses of target speed revealed response attenuation at short interpulse intervals in cells with large transient responses. For sinusoidal modulation of target speed, MT neuron responses were strongly modulated for frequencies up to, but not higher than, 8 Hz. The phase of the responses was consistent with a 90 msec time delay between target velocity and firing rate. We created a model that reproduced the dynamic responses of MT cells using divisive gain control, used the model to visualize the population response in MT to individual stimuli, and devised weighted-averaging computations to reconstruct target speed and acceleration from the population response. Target speed could be reconstructed if each neuron's output was weighted according to its preferred speed. Target acceleration could be reconstructed if each neuron's output was weighted according to the product of preferred speed and a measure of the size of its transient response.

Acceleration↗
PubMed1998

Neural learning rules for the vestibulo-ocular reflex.

Mechanisms for the induction of motor learning in the vestibulo-ocular reflex (VOR) were evaluated by recording the patterns of neural activity elicited in the cerebellum by a range of stimuli that induce learning. Patterns of climbing-fiber, vestibular, and Purkinje cell simple-spike signals were examined during sinusoidal head movement paired with visual image movement at stimulus frequencies from 0.5 to 10 Hz. A comparison of simple-spike and vestibular signals contained the information required to guide learning only at low stimulus frequencies, and a comparison of climbing-fiber and simple-spike signals contained the information required to guide learning only at high stimulus frequencies. Learning could be guided by comparison of climbing-fiber and vestibular signals at all stimulus frequencies tested, but only if climbing fiber responses were compared with the vestibular signals present 100 msec earlier. Computational analysis demonstrated that this conclusion is valid even if there is a broad range of vestibular signals at the site of plasticity. Simulations also indicated that the comparison of vestibular and climbing-fiber signals across the 100 msec delay must be implemented by a subcellular "eligibility" trace rather than by neural circuits that delay the vestibular inputs to the site of plasticity. The results suggest two alternative accounts of learning in the VOR. Either there are multiple mechanisms of learning that use different combinations of neural signals to drive plasticity, or there is a single mechanism tuned to climbing-fiber activity that follows activity in vestibular pathways by approximately 100 msec.

Animals↗
PubMed1998

Physiologic basis for motor learning in the vestibulo-ocular reflex.

The vestibulo-ocular reflex has been used extensively for study of the neural mechanisms of learning that is dependent on an intact cerebellum. Anatomic, physiologic, behavioral, and computational approaches have revealed the neural circuits that are used to generate the vestibulo-ocular reflex and have identified two likely sites of plasticity within those circuits. One site of plasticity is in the vestibular inputs to floccular target neurons, which are located in the vestibular nuclei and receive monosynaptic inhibition from Purkinje cells in the floccular complex of the cerebellar cortex. The other site of plasticity is in the vestibular inputs to Purkinje cells in the floccular complex, possibly in the cerebellar cortex. After reviewing the evidence that supports these conclusions, I consider a number of observations showing that the dynamics of neural circuits or cellular mechanisms play important roles in learning in the vestibulo-ocular reflex.

Fixation, Ocular↗
PubMed1998

Postsaccadic enhancement of initiation of smooth pursuit eye movements in monkeys.

Step-ramp target motion evokes a characteristic sequence of presaccadic smooth eye movement in the direction of the target ramp, catch-up targets to bring eye position close to the position of the moving target, and postsaccadic eye velocities that nearly match target velocity. I have analyzed this sequence of eye movements in monkeys to reveal a strong postsaccadic enhancement of pursuit eye velocity and to document the conditions that lead to that enhancement. Smooth eye velocity was measured in the last 10 ms before and the first 10 ms after the first saccade evoked by step-ramp target motion. Plots of eye velocity as a function of time after the onset of the target ramp revealed that eye velocity at a given time was much higher if measured after versus before the saccade. Postsaccadic enhancement of pursuit was recorded consistently when the target stepped 3 degrees eccentric on the horizontal axis and moved upward, downward, or away from the position of fixation. To determine whether postsaccadic enhancement of pursuit was invoked by smear of the visual scene during a saccade, I recorded the effect of simulated saccades on the presaccadic eye velocity for step-ramp target motion. The 3 degrees simulated saccade, which consisted of motion of a textured background at 150 degrees/s for 20 ms, failed to cause any enhancement of presaccadic eye velocity. By using a strategically selected set of oblique target steps with horizontal ramp target motion, I found clear enhancement for saccades in all directions, even those that were orthogonal to target motion. When the size of the target step was varied by up to 15 degrees along the horizontal meridian, postsaccadic eye velocity did not depend strongly either on the initial target position or on whether the target moved toward or away from the position of fixation. In contrast, earlier studies and data in this paper show that presaccadic eye velocity is much stronger when the target is close to the center of the visual field and when the target moves toward versus away from the position of fixation. I suggest that postsaccadic enhancement of pursuit reflects activation, by saccades, of a switch that regulates the strength of transmission through the visual-motor pathways for pursuit. Targets can cause strong visual motion signals but still evoke low presaccadic eye velocities if they are ineffective at activating the pursuit system.

Animals↗
PubMed1997

Vector averaging for smooth pursuit eye movements initiated by two moving targets in monkeys.

The visual input for pursuit eye movements is represented in the cerebral cortex as the distributed activity of neurons that are tuned for both the direction and speed of target motion. To probe how the motor system uses this distributed code to compute a command for smooth eye movements, we have recorded the initiation of pursuit for 150 msec presentations of two spots moving at different speeds and/or in different directions. With equal probability, one of the two spots continued to move at the same speed and in the same direction and became the tracking target, whereas the other disappeared and served as a distractor. We measured eye acceleration in the interval from 110 to 206 msec after the onset of spot motion, within both the open-loop interval for pursuit and the interval during which eye motion was affected by the two spots. Our results demonstrate that weighted vector averaging is used to combine the responses to two moving spots. We found only a minute number of responses that were consistent with either vector summation or winner-take-all computations. In addition, our data show that it is difficult for the monkey to defeat vector averaging without extended training on the use of an explicit cue about which spot will become the target. We argue that our experiment reveals the computations done by the pursuit system in the absence of attentional bias and that vector averaging is normally used to read the distributed code of image motion when there is only one target.

Animals↗