Self-configuring feedback loops for sensorimotor control

TL;DR

This paper demonstrates that feedback control in a biologically plausible neural model can learn to perform reaching movements, revealing emergent properties of sensorimotor control such as directional tuning and oscillatory dynamics.

Contribution

It introduces a minimal, biologically plausible neural model with plastic feedback loops that learns sensorimotor tasks from scratch within minutes.

Findings

Model learns reaching movements in less than 10 minutes of simulated time.

Neural populations exhibit directional tuning and oscillations.

Spinal cord creates additive force fields leading to ataxic movements.

Abstract

How dynamic interactions between nervous system regions in mammals performs online motor control remains an unsolved problem. In this paper we show that feedback control is a simple, yet powerful way to understand the neural dynamics of sensorimotor control. We make our case using a minimal model comprising spinal cord, sensory and motor cortex, coupled by long connections that are plastic. It succeeds in learning how to perform reaching movements of a planar arm with 6 muscles in several directions from scratch. The model satisfies biological plausibility constraints, like neural implementation, transmission delays, local synaptic learning and continuous online learning. Using differential Hebbian plasticity the model can go from motor babbling to reaching arbitrary targets in less than 10 minutes of in silico time. Moreover, independently of the learning mechanism, properly configured feedback control has many emergent properties: neural populations in motor cortex show directional tuning and oscillatory dynamics, the spinal cord creates convergent force fields that add linearly, and movements are ataxic (as in a motor system without a cerebellum).