Equilibrium Propagation for Non-Conservative Systems

TL;DR

This paper extends Equilibrium Propagation to nonconservative systems with non-reciprocal interactions, enabling exact gradient computation and improved learning performance, demonstrated on MNIST.

Contribution

It introduces a novel framework that generalizes EP to arbitrary nonconservative systems, including feedforward networks, with a modified dynamics for exact gradient calculation.

Findings

Achieves better performance on MNIST

Learns faster than previous methods

Extends EP to non-reciprocal interactions

Abstract

Equilibrium Propagation (EP) is a physics-inspired learning algorithm that uses stationary states of a dynamical system both for inference and learning. In its original formulation it is limited to conservative systems, $\textit{i.e.}$ to dynamics which derive from an energy function. Given their importance in applications, it is important to extend EP to nonconservative systems, $\textit{i.e.}$ systems with non-reciprocal interactions. Previous attempts to generalize EP to such systems failed to compute the exact gradient of the cost function. Here we propose a framework that extends EP to arbitrary nonconservative systems, including feedforward networks. We keep the key property of equilibrium propagation, namely the use of stationary states both for inference and learning. However, we modify the dynamics in the learning phase by a term proportional to the non-reciprocal part of the interaction so as to obtain the exact gradient of the cost function. This algorithm can also be derived using a variational formulation that generates the learning dynamics through an energy function defined over an augmented state space. Numerical experiments using the MNIST database show that this algorithm achieves better performance and learns faster than previous proposals.