Hardware Implementation of On-Chip Hebbian Learning Through Integrated Neuromorphic Architecture.

ABSTRACT: The von Neumann bottleneck and growing energy demands of conventional computing systems require innovative architectural solutions. Although neuromorphic computing is a promising alternative, implementing efficient on-chip learning mechanisms remains a fundamental challenge. Herein, a novel artificial neural platform is presented that integrates three synergistic components: modulation-optimized presynaptic transistors, threshold switching memristor-based neurons, and adaptive feedback synapses. The platform demonstrates real-time synaptic weight modification through correlation-based learning, effectively implementing Hebbian principles in hardware without requiring extensive peripheral circuitry. Stable device operation and successful implementation of local learning rules are confirmed

SUBMITTER: Kim S 

PROVIDER: S-EPMC12464636 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature