Computational approaches to sensorimotor transformations.
Behaviors such as sensing an object and then moving your eyes or your hand toward it require that sensory information be used to help generate a motor command, a process known as a sensorimotor transformation. Here we review models of sensorimotor transformations that use a flexible intermediate representation that relies on basis functions. The use of basis functions as an intermediate is borrowed from the theory of nonlinear function approximation. We show that this approach provides a unifying insight into the neural basis of three crucial aspects of sensorimotor transformations, namely, computation, learning and short-term memory. This mathematical formalism is consistent with the responses of cortical neurons and provides a fresh perspective on the issue of frames of reference in spatial representations.
Pouget
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Department of Brain and Cognitive Sciences, University of Rochester, New York 14627, USA. alex@bcs.rochester.edu
Snyder
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LH
D000818:Animals; D001921:Brain / Q000166:cytology / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology; D016014:Linear Models; D008570:Memory, Short-Term / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology / Q000502:physiology; D009474:Neurons / Q000166:cytology / Q000502:physiology*; D017711:Nonlinear Dynamics; D011597:Psychomotor Performance / Q000502:physiology*; D013028:Space Perception / Q000502:physiology