Layer-Skipping Connections Improve the Effectiveness of Equilibrium Propagation on Layered Networks
Jimmy I. Gammell, Sae Woo Nam, Adam McCaughan
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.
, Nam, S.
and McCaughan, A.
Layer-Skipping Connections Improve the Effectiveness of Equilibrium Propagation on Layered Networks, Frontiers in Neuroscience
(Accessed September 23, 2023)