Variation as a neural code

University Of California, San Francisco, San Francisco CA

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Linked publications & trials

Abstract

Visually guided reaching is a centrally important behavior in both human and non-human primates.[unreadable] Reaching is a highly stereotyped behavior, with similar movement paths and velocity profiles[unreadable] observed across a wide range of conditions, across subjects, and even across species. Yet[unreadable] another hallmark of visually guided reaching is trial by trial variability in the details of the movement[unreadable] and in the movement endpoint. The goal of this project is to characterize the nature of this[unreadable] variability and its neural origins and to test two hypotheses about the role that variability plays in[unreadable] such sensorimotor circuits. There are three specific aims. 1) We will characterize the relationship[unreadable] between variability in visually guided reaching and neural activity in Macaque ventral premotor[unreadable] cortex (PMv) and primary motor cortex (M1). We will perform a detailed analysis of the trial by trial[unreadable] movement variability, using a number of novel analysis techniques. In addition to yielding new[unreadable] insight into the nature of movement variability, this analysis will provide a rich set of behavioral[unreadable] variables that can be compared to neural activity. We will record from PMv and M1 in Macaque[unreadable] monkeys while they perform a reaching task. The trial by trial neural activity will be decomposed[unreadable] into variability that can be explained by the behavior and a residual variability that unrelated to[unreadable] behavioral variability. 2) We will test our hypothesis that reach variability is under central control by[unreadable] attempting to alter variability using surreptitious manipulations of movement feedback. With human[unreadable] subjects, we will either reshape the mapping from reach endpoint to reward feedback or introduce[unreadable] time-varying changes in the visual feedback of hand position in order to reshape the variability of[unreadable] reaching. We will then employ these behavioral techniques with Macaque while we record from[unreadable] PMv and M1, and we will correlate the changes in movement variability along each spatial axis with[unreadable] changes in the explained and residual variability of neural activity. 3) We will test our hypothesis[unreadable] about the relationship between neural variability and learning. Specifically, we propose that[unreadable] explained variability in a neural circuit is aids learning in that circuit, since it provides a richer[unreadable] sampling of the input-output space, while residual variability is detrimental for learning, since it[unreadable] degrades the quality of the error signal. We will test this hypothesis by studying a particular form of[unreadable] sensorimotor learning: the rapid adaptation that follows exposure to shifted visual feedback. By[unreadable] comparing the learning rate of shift adaptation to the level of behavioral and neural variability, we[unreadable] will determine whether either type of variability affects sensorimotor learning.