Topic: local learning

We introduce a statistical physics framework for learning in neural architectures composed of single or interconnected asymmetric attractor networks. These systems can exhibit a manifold of global fixed points capable of implementing sophisticated input-output mappings, which we characterize analytically. Learning from extensive datasets is achieved through the stabilization of fixed points via a fully distributed and local learning process, implemented at the single-neuron level. This simple mechanism yields performance comparable to that of conventional feedforward deep neural networks train

In modern deep neural networks, the intricate learning dynamics of individual neurons often remain obscured due to global optimization methods. This contrasts with biological neural networks, which achieve robustness and efficiency through self-organized, local learning processes using limited global information. To bridge this gap, a local learning framework inspired by the self-organizing principles of biological neurons has been proposed [1]. This framework allows for the investigation of how local information-processing goals can drive the emergence of complex network-level functions. The