A differential Hebbian framework for biologically-plausible motor control

TL;DR

This paper proposes a biologically plausible neural control architecture using differential Hebbian learning rules, capable of autonomous learning and handling complex control tasks through feedback mechanisms and reinforcement learning.

Contribution

It introduces a novel neural control framework combining differential Hebbian learning with feedback control and reinforcement learning for autonomous, biologically plausible motor control.

Findings

Feedback controllers learn to achieve desired states via error selection.

Differential Hebbian rules enable autonomous learning through environmental interaction.

The approach simplifies learning complex actions by reducing the problem complexity.

Abstract

In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive them. This selection happens through a family of differential Hebbian learning rules that, through interaction with the environment, can learn to control systems where the error responds monotonically to the control signal. We next show that in a more general case, neural reinforcement learning can be coupled with a feedback controller to reduce errors that arise non-monotonically from the control signal. The use of feedback control can reduce the complexity of the reinforcement learning problem, because only a desired value must be learned, with the controller handling the details of how it is reached. This makes the function to be learned simpler, potentially allowing learning of more complex actions. We use simple examples to illustrate our approach, and discuss how it could be extended to hierarchical architectures.