Heterogeneous Time Constants Improve Stability in Equilibrium Propagation
Machine Learning
2026-03-05 v1 Artificial Intelligence
Abstract
Equilibrium propagation (EP) is a biologically plausible alternative to backpropagation for training neural networks. However, existing EP models use a uniform scalar time step dt, which corresponds biologically to a membrane time constant that is heterogeneous across neurons. Here, we introduce heterogeneous time steps (HTS) for EP by assigning neuron-specific time constants drawn from biologically motivated distributions. We show that HTS improves training stability while maintaining competitive task performance. These results suggest that incorporating heterogeneous temporal dynamics enhances both the biological realism and robustness of equilibrium propagation.
Cite
@article{arxiv.2603.03402,
title = {Heterogeneous Time Constants Improve Stability in Equilibrium Propagation},
author = {Yoshimasa Kubo and Suhani Pragnesh Modi and Smit Patel},
journal= {arXiv preprint arXiv:2603.03402},
year = {2026}
}