Gammell, J.
, Nam, S.
and McCaughan, A.
(2021),
Layer-Skipping Connections Improve the Effectiveness of Equilibrium Propagation on Layered Networks, Frontiers in Neuroscience, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930142
(Accessed November 29, 2025)
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Layer-Skipping Connections Improve the Effectiveness of Equilibrium Propagation on Layered Networks
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Author(s)
, Sae Woo Nam,
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.Citation
Frontiers in Neuroscience
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Created May 17, 2021, Updated September 29, 2025