A comparative approach to closed-loop computation

Abstract

Neural computation is inescapably closed-loop: the nervous system processes sensory signals to shape motor output, and motor output consequently shapes sensory input. Technological advances have enabled neuroscientists to close, open, and alter feedback loops in a wide range of experimental preparations. The experimental capability of manipulating the topology-that is, how information can flow between subsystems-provides new opportunities to understand the mechanisms and computations underlying behavior. These experiments encompass a spectrum of approaches from fully open-loop, restrained preparations to the fully closed-loop character of free behavior. Control theory and system identification provide a clear computational framework for relating these experimental approaches. We describe recent progress and new directions for translating experiments at one level in this spectrum to predictions at another level. Operating across this spectrum can reveal new understanding of how low-level neural mechanisms relate to high-level function during closed-loop behavior.