Neural structure of a sensory decoder for motor control

Abstract

We seek to understand the neural mechanisms that perform sensory decoding for motor behavior, advancing the field by designing decoders based on neural circuits. A simple experiment produced a surprising result that shapes our approach. Changing the size of a target for smooth pursuit eye movements changes the relationship between the variance and mean of the evoked behavior in a way that contradicts the regime of "signal-dependent noise" and defies traditional decoding approaches. A theoretical analysis leads us to conclude that sensory decoding circuits for pursuit include multiple parallel pathways and multiple sources of variation. Behavioral and neural responses with biomimetic statistics emerge from a biologically-motivated circuit model with noise in the pathway that is dedicated to flexibly adjusting the strength of visual-motor transmission. Flexible adjustment of transmission strength applies much more broadly to issues in sensory-motor control such as Bayesian integration and control strategies to optimize motor behavior.