Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.

ABSTRACT: The pursuit of artificial neural networks that mirror the accuracy, efficiency and low latency of biological neural networks remains a cornerstone of artificial intelligence (AI) research. Here, we incorporated recent neuroscientific findings of self-inhibiting autapse and neuron heterogeneity for innovating a spiking neural network (SNN) with enhanced learning and memorizing capacities. A bi-level programming paradigm was formulated to respectively learn neuron-level biophysical variables and network-level synapse weights for nested heterogeneous learning. We successfully demonstrated that our biologically inspired neuron model could reproduce neural statistics at both individual and group levels, contributing to the effective decoding of brain-computer interface data. Furthermore, the he

SUBMITTER: Wang B 

PROVIDER: S-EPMC11697980 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature