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 level, contributing to the effective decoding of brain-computer interface (BCI) data. Furthermore, the heterogeneous SNN showed higher accuracy (1–10% improvement), superior efficiency (maximal 17.83-fold reduction in energy) and lower latency (maximal 5-fold improvement) in performing several AI tasks. For the first time, we benchmarked SNN for conducting cell type identification from scRNA-seq data. The proposed model correctly identified very rare cell types associated with severe brain diseases where typical SNNs failed.