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J Neurophysiol 102: 2013-2025, 2009. First published August 5, 2009; doi:10.1152/jn.00611.2009
0022-3077/09 $8.00

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RESEARCH-ARTICLE

Spatial and Temporal Integration of Visual Motion Signals for Smooth Pursuit Eye Movements in Monkeys

¹Sloan-Swartz Center for Theoretical Neurobiology, ²Howard Hughes Medical Institute, ³W. M. Keck Foundation Center for Integrative Neuroscience, and ⁴Department of Physiology, University of California, San Francisco, California

Submitted 15 July 2009; accepted in final form 24 July 2009

ABSTRACT

To probe how the brain integrates visual motion signals to guide behavior, we analyzed the smooth pursuit eye movements evoked by target motion with a stochastic component. When each dot of a texture executed an independent random walk such that speed or direction varied across the spatial extent of the target, pursuit variance increased as a function of the variance of visual pattern motion. Noise in either target direction or speed increased the variance of both eye speed and direction, implying a common neural noise source for estimating target speed and direction. Spatial averaging was inefficient for targets with >20 dots. Together these data suggest that pursuit performance is limited by the properties of spatial averaging across a noisy population of sensory neurons rather than across the physical stimulus. When targets executed a spatially uniform random walk in time around a central direction of motion, an optimized linear filter that describes the transformation of target motion into eye motion accounted for 50% of the variance in pursuit. Filters had widths of 25 ms, much longer than the impulse response of the eye, and filter shape depended on both the range and correlation time of motion signals, suggesting that filters were products of sensory processing. By quantifying the effects of different levels of stimulus noise on pursuit, we have provided rigorous constraints for understanding sensory population decoding. We have shown how temporal and spatial integration of sensory signals converts noisy population responses into precise motor responses.