Layer-Skipping Connections Improve the Effectiveness of Equilibrium Propagation on Layered Networks.

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Abstract

Equilibrium propagation is a learning framework that marks a step forward in the search for a biologically-plausible implementation of deep learning, and could be implemented efficiently in neuromorphic hardware. Previous applications of this framework to layered networks encountered a vanishing gradient problem that has not yet been solved in a simple, biologically-plausible way. In this paper, we demonstrate that the vanishing gradient problem can be mitigated by replacing some of a layered network’s connections with random layer-skipping connections in a manner inspired by small-world networks. This approach would be convenient to implement in neuromorphic hardware, and is biologically-plausible.
Copyright © 2021 Gammell, Buckley, Nam and McCaughan. The U.S. Government is authorized to reproduce and distribute reprints for governmental purpose notwithstanding any copyright annotation thereon.