Emergent Self-Adaptation in an Integrated Photonic Neural Network for Backpropagation-Free Learning.

ABSTRACT: Plastic self-adaptation, nonlinear recurrent dynamics and multi-scale memory are desired features in hardware implementations of neural networks, because they enable them to learn, adapt, and process information similarly to the way biological brains do. In this work, these properties occurring in arrays of photonic neurons are experimentally demonstrated. Importantly, this is realized autonomously in an emergent fashion, without the need for an external controller setting weights and without explicit feedback of a global reward signal. Using a hierarchy of such arrays coupled to a backpropagation-free training algorithm based on simple logistic regression, a performance of 98.2% is achieved on the MNIST task, a popular benchmark task looking at classification of written digits. The plasti

SUBMITTER: Lugnan A 

PROVIDER: S-EPMC11906216 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature