Equilibrium Propagation

Equilibrium propagation (EP) is a biologically-inspired learning algorithm for neural networks that avoids the computationally expensive backpropagation method by leveraging a system's natural relaxation to an energy minimum to estimate gradients. Current research focuses on extending EP to various architectures, including quantum systems, spiking neural networks, and coupled phase oscillators, and on addressing challenges like weight asymmetry and the need for infinitesimal perturbations. This approach holds significant promise for energy-efficient neuromorphic computing and offers a potentially more robust alternative to traditional deep learning methods, particularly in the face of adversarial attacks.