AI RESEARCH
Scalable Equilibrium Propagation via Intermediate Error Signals for Deep Convolutional CRNNs
arXiv CS.LG
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ArXi:2508.15989v2 Announce Type: replace Equilibrium Propagation (EP) is a biologically inspired local learning rule first proposed for convergent recurrent neural networks (CRNNs), in which synaptic updates depend only on neuron states from two distinct phases. EP estimates gradients that closely align with those computed by Backpropagation Through Time (BPTT) while significantly reducing computational demands, positioning it as a potential candidate for on-chip