Experimental and Computational Analysis of Monkey Smooth Pursuit Eye Movements

2001 ◽  
Vol 86 (2) ◽  
pp. 741-759 ◽  
Author(s):  
Mark M. Churchland ◽  
Stephen G. Lisberger
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Sine Wave ◽  
Visual Signals ◽  
Image Motion ◽  
Motion Model ◽  
Image Velocity ◽  
Pursuit Eye Movements ◽  
Feedback Model ◽  
Baseline Image

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.

scholarly journals Illusory bending of a rigidly moving line segment: Effects of image motion and smooth pursuit eye movements

10.1167/7.6.9 ◽  
2007 ◽  
Vol 7 (6) ◽  
pp. 9 ◽  
Author(s):  
Lore Thaler ◽  
James T. Todd ◽  
Miriam Spering ◽  
Karl R. Gegenfurtner
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Line Segment ◽  
Image Motion ◽  
Smooth Pursuit Eye Movements ◽  
Pursuit Eye Movements ◽  
Moving Line

A Control Systems Model of Smooth Pursuit Eye Movements with Realistic Emergent Properties

1989 ◽  
Vol 1 (1) ◽  
pp. 116-122 ◽  
Author(s):  
R. J. Krauzlis ◽  
S. G. Lisberger
Keyword(s):  
Eye Movements ◽  
Visual Tracking ◽  
Smooth Pursuit ◽  
Oculomotor System ◽  
Difficult Problem ◽  
Visual Signals ◽  
Emergent Properties ◽  
Smooth Pursuit Eye Movements ◽  
Pursuit Eye Movements ◽  
Systems Model

Visual tracking of objects in a noisy environment is a difficult problem that has been solved by the primate oculomotor system, but remains unsolved in robotics. In primates, smooth pursuit eye movements match eye motion to target motion to keep the eye pointed at smoothly moving targets. We have used computer models as a tool to investigate possible computational strategies underlying this behavior. Here, we present a model based upon behavioral data from monkeys. The model emphasizes the variety of visual signals available for pursuit and, in particular, includes a sensitivity to the acceleration of retinal images. The model was designed to replicate the initial eye velocity response observed during pursuit of different target motions. The strength of the model is that it also exhibits a number of emergent properties that are seen in the behavior of both humans and monkeys. This suggests that the elements in the model capture important aspects of the mechanism of visual tracking by the primate smooth pursuit system.

scholarly journals Partial Ablations of the Flocculus and Ventral Paraflocculus in Monkeys Cause Linked Deficits in Smooth Pursuit Eye Movements and Adaptive Modification of the VOR

2002 ◽  
Vol 87 (2) ◽  
pp. 912-924 ◽  
Author(s):  
H. Rambold ◽  
A. Churchland ◽  
Y. Selig ◽  
L. Jasmin ◽  
S. G. Lisberger
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Large Fraction ◽  
Head Rotation ◽  
Head Movements ◽  
Image Motion ◽  
Smooth Pursuit Eye Movements ◽  
Pursuit Eye Movements ◽  
Vor Adaptation ◽  
Ventral Paraflocculus

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°/s pulses of head velocity. VOR adaptation was studied by having the monkeys wear ×2 or ×0.25 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.

Smooth pursuitlike eye movements evoked by microstimulation in macaque nucleus reticularis tegmenti pontis

1996 ◽  
Vol 76 (5) ◽  
pp. 3313-3324 ◽  
Author(s):  
T. Yamada ◽  
D. A. Suzuki ◽  
R. D. Yee
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Current Intensity ◽  
Image Motion ◽  
Eye Position ◽  
Smooth Pursuit Eye Movements ◽  
Pontine Nucleus ◽  
Nucleus Reticularis Tegmenti Pontis ◽  
Pursuit Eye Movements ◽  
Nucleus Reticularis

1. Smooth pursuitlike eye movements were evoked with low current microstimulation delivered to rostral portions of the nucleus reticularis tegmenti pontis (rNRTP) in alert macaques. Microstimulation sites were selected by the observation of modulations in single-cell firing rates that were correlated with periodic smoothpursuit eye movements. Current intensities ranged from 10 to 120 microA and were routinely < 40 microA. Microstimulation was delivered either in the dark with no fixation, 100 ms after a fixation target was extinguished, or during maintained fixation of a stationary or moving target. Evoked eye movements also were studied under open-loop conditions with the target image stabilized on the retina. 2. Eye movements evoked in the absence of a target rapidly accelerated to a constant velocity that was maintained for the duration of the microstimulation. Evoked eye speeds ranged from 3.7 to 23 deg/s and averaged 11 deg/s. Evoked eye speed appeared to be linearly related to initial eye position with a sensitivity to initial eye position that averaged 0.23 deg.s-1.deg-1. While some horizontal and oblique smooth eye movements were elicited, microstimulation resulted in upward eye movements in 89% of the sites. 3. Evoked eye speed was found to be dependent on microstimulation pulse frequency and current intensity. Within limits, evoked eye speed increased with increases in stimulation frequency or current intensity. For stimulation frequencies < 300–400 Hz, only smooth pursuit-like eye movements were evoked. At higher stimulation frequencies, accompanying saccades consistently were elicited. 4. Feedback of retinal image motion interacted with the evoked eye movements to decrease eye speed if the visual motion was in the opposite direction as the evoked, pursuit-like eye movements. 5. The results implicate rNRTP as part of the neuronal substrate that controls smooth-pursuit eye movements. NRTP appears to be divided functionally into a rostral, pursuit-related portion and a caudal, saccade-related area. rNRTP is a component of a corticopontocerebellar circuit that presumably involves the pursuit area of the frontal eye field and that parallels the middle and medial superior temporal cerebral cortical/dorsalateral pontine nucleus (MT/MST-DLPN-cerebellum) pathway known to be involved also with regulating smooth-pursuit eye movements.

scholarly journals Temporal dynamics of retinal and extraretinal signals in the FEFsem during smooth pursuit eye movements

2017 ◽  
Vol 117 (5) ◽  
pp. 1987-2003 ◽  
Author(s):  
Leah Bakst ◽  
Jérome Fleuriet ◽  
Michael J. Mustari
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Clustering Algorithm ◽  
Critical Role ◽  
Image Motion ◽  
Frontal Eye Field ◽  
Smooth Pursuit Eye Movements ◽  
Pursuit Eye Movements ◽  
Eye Field ◽  
Eye Velocity

Neurons in the smooth eye movement subregion of the frontal eye field (FEFsem) are known to play an important role in voluntary smooth pursuit eye movements. Underlying this function are projections to parietal and prefrontal visual association areas and subcortical structures, all known to play vital but differing roles in the execution of smooth pursuit. Additionally, the FEFsem has been shown to carry a diverse array of signals (e.g., eye velocity, acceleration, gain control). We hypothesized that distinct subpopulations of FEFsem neurons subserve these diverse functions and projections, and that the relative weights of retinal and extraretinal signals could form the basis for categorization of units. To investigate this, we used a step-ramp tracking task with a target blink to determine the relative contributions of retinal and extraretinal signals in individual FEFsem neurons throughout pursuit. We found that the contributions of retinal and extraretinal signals to neuronal activity and behavior change throughout the time course of pursuit. A clustering algorithm revealed three distinct neuronal subpopulations: cluster 1 was defined by a higher sensitivity to eye velocity, acceleration, and retinal image motion; cluster 2 had greater activity during blinks; and cluster 3 had significantly greater eye position sensitivity. We also performed a comparison with a sample of medial superior temporal neurons to assess similarities and differences between the two areas. Our results indicate the utility of simple tests such as the target blink for parsing the complex and multifaceted roles of cortical areas in behavior. NEW & NOTEWORTHY The frontal eye field (FEF) is known to play a critical role in volitional smooth pursuit, carrying a variety of signals that are distributed throughout the brain. This study used a novel application of a target blink task during step ramp tracking to determine, in combination with a clustering algorithm, the relative contributions of retinal and extraretinal signals to FEF activity and the extent to which these contributions could form the basis for a categorization of neurons.

Smooth Pursuit Eye Movements to Isoluminant Targets

2008 ◽  
Vol 100 (3) ◽  
pp. 1287-1300 ◽  
Author(s):  
D. I. Braun ◽  
N. Mennie ◽  
C. Rasche ◽  
A. C. Schütz ◽  
M. J. Hawken ◽  
...  
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Small Difference ◽  
Luminance Contrast ◽  
Sine Wave ◽  
Smooth Pursuit Eye Movements ◽  
Pursuit Eye Movements ◽  
Pursuit Initiation ◽  
All Speeds ◽  
Sine Wave Gratings

At slow speeds, chromatic isoluminant stimuli are perceived to move much slower than comparable luminance stimuli. We investigated whether smooth pursuit eye movements to isoluminant stimuli show an analogous slowing. Beside pursuit speed and latency, we studied speed judgments to the same stimuli during fixation and pursuit. Stimuli were either large sine wave gratings or small Gaussians blobs moving horizontally at speeds between 1 and 11°/s. Targets were defined by luminance contrast or color. Confirming prior studies, we found that speed judgments of isoluminant stimuli during fixation showed a substantial slowing when compared with luminance stimuli. A similarly strong and significant effect of isoluminance was found for pursuit initiation: compared with luminance targets of matched contrasts, latencies of pursuit initiation were delayed by 50 ms at all speeds and eye accelerations were reduced for isoluminant targets. A small difference was found between steady-state eye velocities of luminance and isoluminant targets. For comparison, we measured latencies of saccades to luminance and isoluminant stimuli under similar conditions, but the effect of isoluminance was only found for pursuit. Parallel psychophysical experiments revealed that different from speed judgments of moving isoluminant stimuli made during fixation, judgments during pursuit are veridical for the same stimuli at all speeds. Therefore information about target speed seems to be available for pursuit eye movements and speed judgments during pursuit but is degraded for perceptual speed judgments during fixation and for pursuit initiation.

scholarly journals Role of Arcuate Frontal Cortex of Monkeys in Smooth Pursuit Eye Movements. I. Basic Response Properties to Retinal Image Motion and Position

2002 ◽  
Vol 87 (6) ◽  
pp. 2684-2699 ◽  
Author(s):  
Masaki Tanaka ◽  
Stephen G. Lisberger
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Retinal Image ◽  
Image Motion ◽  
Smooth Pursuit Eye Movements ◽  
Pursuit Eye Movements ◽  
Wide Range ◽  
Image Position ◽  
Eye Velocity ◽  
Pursuit Neurons

Anatomical and physiological studies have shown that the “frontal pursuit area” (FPA) in the arcuate cortex of monkeys is involved in the control of smooth pursuit eye movements. To further analyze the signals carried by the FPA, we examined the activity of pursuit-related neurons recorded from a discrete region near the arcuate spur during a variety of oculomotor tasks. Pursuit neurons showed direction tuning with a wide range of preferred directions and a mean full width at half-maximum of 129°. Analysis of latency using the “receiver operating characteristic” to compare responses to target motion in opposite directions showed that the directional response of 58% of FPA neurons led the initiation of pursuit, while 19% led by 25 ms or more. Analysis of neuronal responses during pursuit of a range of target velocities revealed that the sensitivity to eye velocity was larger during the initiation of pursuit than during the maintenance of pursuit, consistent with two components of firing related to image motion and eye motion. FPA neurons showed correlates of two behavioral features of pursuit documented in prior reports. 1) Eye acceleration at the initiation of pursuit declines as a function of the eccentricity of the moving target. FPA neurons show decreased firing at the initiation of pursuit in parallel with the decline in eye acceleration. This finding is consistent with prior suggestions that the FPA plays a role in modulating the gain of visual-motor transmission for pursuit. 2) A stationary eccentric cue evokes a smooth eye movement opposite in direction to the cue and enhances the pursuit evoked by subsequent target motions. Many pursuit neurons in the FPA showed weak, phasic visual responses for stationary targets and were tuned for the positions about 4° eccentric on the side opposite to the preferred pursuit direction. However, few neurons (12%) responded during the preparation or execution of saccades. The responses to the stationary target could account for the behavioral effects of stationary, eccentric cues. Further analysis of the relationship between firing rate and retinal position error during pursuit in the preferred and opposite directions failed to provide evidence for a large contribution of image position to the firing of FPA neurons. We conclude that FPA processes information in terms of image and eye velocity and that it is functionally separate from the saccadic frontal eye fields, which processes information in terms of retinal image position.

scholarly journals Task-Dependent Modulation of the Sensorimotor Transformation for Smooth Pursuit Eye Movements

2000 ◽  
Vol 84 (6) ◽  
pp. 2725-2738 ◽  
Author(s):  
Vincent P. Ferrera
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Competitive Inhibition ◽  
Substantial Effect ◽  
Image Motion ◽  
Target Color ◽  
Pursuit Eye Movements ◽  
Winner Take All ◽  
Eye Velocity ◽  
Take All

To investigate the transformation of retinal image velocity into smooth pursuit eye velocity, eye movements were measured in the presence of two moving targets. In the first experiment, the targets were identical in all respects except for direction of motion, and the monkey was not cued to attend to either target. In this experiment, smooth pursuit eye velocity elicited by two targets was the vector average of the response evoked by each target alone. In subsequent experiments, we examined the effects of stimulus and task parameters on the selectivity of pursuit. When the targets were made different colors and monkeys were cued for the color of the rewarded target, their pursuit eye movements were biased in the direction of the rewarded target, but still showed a substantial influence of the nonrewarded target (distractor). It did not matter whether the same target color was used for an entire session or whether the color was randomized from trial to trial. Reducing uncertainty about the axis of motion of the rewarded target also had little effect. However, the pattern of image motion appeared to have a substantial effect; radial image motion favored averaging, and winner-take-all pursuit was found only with nonradial image motion. We conclude that the sensorimotor interface for pursuit uses a flexible decision rule that can vary continuously from vector averaging to winner-take-all. We present a simple recurrent network model that reflects this range of behavior. The model has allowed us to identify three computational elements (selection bias, competitive inhibition, and response normalization) that should be taken into consideration in future models of smooth pursuit.

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