How Is a Sensory Map Read Out? Effects of Microstimulation in Visual Area MT on Saccades and Smooth Pursuit Eye Movements

Abstract

To generate behavioral responses based on sensory input, motor areas of the brain must interpret, or "read out," signals from sensory maps. Our experiments tested several algorithms for how the motor systems for smooth pursuit and saccadic eye movements might extract a usable signal of target velocity from the distributed representation of velocity in the middle temporal visual area (MT or V5). Using microstimulation, we attempted to manipulate the velocity information within MT while monkeys tracked a moving visual stimulus. We examined the effects of the microstimulation on smooth pursuit and on the compensation for target velocity shown by saccadic eye movements. Microstimulation could alter both the speed and direction of the motion estimates of both types of eye movements and could also cause monkeys to generate pursuit even when the visual target was actually stationary. The pattern of alterations suggests that microstimulation can introduce an additional velocity signal into MT and that the pursuit and saccadic systems usually compute a vector average of the visually evoked and microstimulation-induced velocity signals (pursuit, 55 of 122 experiments; saccades, 70 of 122). Microstimulation effects in a few experiments were consistent with vector summation of these two signals (pursuit, 6 of 122; saccades, 2 of 122). In the remainder of the experiments, microstimulation caused either an apparent impairment in motion processing (pursuit, 47 of 122; saccades, 41 of 122) or had no effect (pursuit, 14 of 122; saccades, 9 of 122). Within individual experiments, the effects on pursuit and saccades were usually similar, but the occasional striking exception suggests that the two eye movement systems may perform motion computations somewhat independently.

Vector Averaging for Smooth Pursuit Eye Movements Initiated by Two Moving Targets in Monkeys

“…In the pursuit system, microstimulation of visual area MT at the onset of motion of a visual target had effects on both pursuit eye movements and saccades that were most consistent with the use of vector averaging to convert the distributed representation of image motion in MT into commands for these movements (Groh et al, 1997). Although they used dynamic random dot patterns and humans rather than single spots and monkeys, Watamaniuk and Heinen (1994) showed that the initial smooth eye movements evoked by this stimulus reflect a vector combination of the motion of all the dots with precision equivalent to precision of perceptual decisions based on the same stimulus.…”

“…The computations include "winner-take-all," in which the label on the neuron with the largest response determines the output of the map; "vector summation," in which the activities of all active neurons are summed with weights that are determined by their individual labels; and "vector averaging," in which the vector sum is normalized according to the number of neurons that are active. Re-cently, Groh et al (1997) showed that vector averaging can account for the majority of the effects of microstimulation in area MT on the smooth and saccadic eye movements evoked by moving visual targets.…”

Cortical pooling algorithms for judging global motion direction

“…The representation of motion direction in MT is topographically organized and superimposed on a coarse retinotopic map: Columns of neurons oriented perpendicularly to the pial surface respond to similar directions of motion and retinal positions (12,13). Cortical networks downstream from MT access its neural representation of visual motion to direct many forms of behavior, including saccadic and smooth pursuit eye movements (14) and perceptual judgements of motion direction (15,16) and speed (17).…”

“…Numerous physiological studies have attempted to distinguish between two population coding algorithms for reading out directionally tuned activity in MT: ''vector average'' (population vector of stimulus direction and magnitude calculated from average, direction-tuned neural responses weighted in proportion to their magnitude) and ''winnertake-all'' (most active directionally tuned column of MT neurons). In a series of pioneering studies combining visual stimulation with electrical microstimulation in monkeys, Newsome and colleagues (14,18,20) found that the algorithm deployed by cortical networks to decode the motion representation in MT depends on both task demands and the behavioral objective of the monkey. When discriminating very different visual motion directions, the perceptual system employs a winner-take-all algorithm (18), whereas the saccadic and smooth-pursuit eye movement systems read off the vector average of competing velocity signals in MT (14,19).…”

“…In a series of pioneering studies combining visual stimulation with electrical microstimulation in monkeys, Newsome and colleagues (14,18,20) found that the algorithm deployed by cortical networks to decode the motion representation in MT depends on both task demands and the behavioral objective of the monkey. When discriminating very different visual motion directions, the perceptual system employs a winner-take-all algorithm (18), whereas the saccadic and smooth-pursuit eye movement systems read off the vector average of competing velocity signals in MT (14,19). Moreover, monkeys' performance on a free report task that incorporated elements of both of these experiments was mediated by a vector average algorithm when the direction preferences of visually and microstimulated neurons differed by Ͻ140°and by a winner-take-all algorithm when they differed by more than this value (20).…”