A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks.

ABSTRACT: As computational models inspired by the biological neural system, spiking neural networks (SNN) continue to demonstrate great potential in the landscape of artificial intelligence, particularly in tasks such as recognition, inference, and learning. While SNN focuses on achieving high-level intelligence of individual creatures, Swarm Intelligence (SI) is another type of bio-inspired models that mimic the collective intelligence of biological swarms, i.e., bird flocks, fish school and ant colonies. SI algorithms provide efficient and practical solutions to many difficult optimization problems through multi-agent metaheuristic search. Bridging these two distinct subfields of artificial intelligence has the potential to harness collective behavior and learning ability of biological systems. In

SUBMITTER: Fang Y 

PROVIDER: S-EPMC6700359 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature