Achieving Swarm Intelligence with Spiking Neural Oscillators

Abstract

Mimicking the collaborative behavior of biological swarms, such as bird flocks and ant colonies, Swarm Intelligence algorithms provide efficient solutions for various optimization problems. On the other hand, a computational model of the human brain, spiking neural networks, has been showing great promise in recognition, inference, and learning, due to recent emergence of neuromorphic hardware for high-efficient and low-power computing. Through bridging these two distinct research fields, we propose a novel computing paradigm that implements the swarm intelligence with a population of coupled spiking neural oscillators in basic leaky integrate-and-fire (LIF) model. Our model behaves as a meta-heuristic searching conducted by multiple collaborative agents. In this design, the oscillating neurons serve as agents in the swarm, search for solutions in frequency coding and communicate with each other through spikes. The firing rate of each agent is adaptive to other agents with better solutions and the optimal solution is rendered as the swarm synchronization is reached. We apply the proposed method to the parameter optimization in several test objective functions and demonstrate its effectiveness and efficiency. Our new computing paradigm expands the computational power of coupled spiking neurons in the field of solving optimization problem and brings opportunities for the connection between individual intelligence and swarm intelligence.