Equilibrium propagation: a road for physics-based learning

Neuromorphic computing seeks to emulate the brain's efficiency in hardware, enabling sophisticated information processing with minimal energy consumption. Despite these advances, dynamically training neuromorphic systems—whether electronic or photonic—to undertake new tasks directly within the hardware remains a formidable challenge. This is largely due to the nature of conventional training algorithms like backpropagation. They are not well-suited for hardware implementation because they rely on non-local computations. In this presentation, I will introduce a solution to this challenge through the application of Equilibrium Propagation, an algorithm rooted in physics, first proposed by Scellier and Bengio in 2017. This innovative approach leverages the inherent dynamics of physical systems for learning, capitalizing on how these systems can be gently guided ('nudged') towards a desired outcome through their natural behavior. I will elucidate the foundational principles of Equilibrium Propagation, discuss the obstacles encountered in its implementation, and demonstrate its versatility through applications across a variety of physical systems.