Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network

TL;DR

This paper introduces a biologically-inspired spiking policy network optimized via a genetic algorithm, which rewires synaptic connections instead of weights, achieving comparable performance to deep reinforcement learning with higher energy efficiency.

Contribution

The novel approach tunes synaptic connections rather than weights in a spiking neural network using a genetic algorithm, inspired by biological memory formation and rewiring.

Findings

Achieves similar performance to DRL in robotic tasks

Demonstrates significantly higher energy efficiency

Validates biological plausibility of synaptic rewiring

Abstract

Learning from interaction is the primary way that biological agents acquire knowledge about their environment and themselves. Modern deep reinforcement learning (DRL) explores a computational approach to learning from interaction and has made significant progress in solving various tasks. However, despite its power, DRL still falls short of biological agents in terms of energy efficiency. Although the underlying mechanisms are not fully understood, we believe that the integration of spiking communication between neurons and biologically-plausible synaptic plasticity plays a prominent role in achieving greater energy efficiency. Following this biological intuition, we optimized a spiking policy network (SPN) using a genetic algorithm as an energy-efficient alternative to DRL. Our SPN mimics the sensorimotor neuron pathway of insects and communicates through event-based spikes. Inspired by biological research showing that the brain forms memories by creating new synaptic connections and rewiring these connections based on new experiences, we tuned the synaptic connections instead of weights in the SPN to solve given tasks. Experimental results on several robotic control tasks demonstrate that our method can achieve the same level of performance as mainstream DRL methods while exhibiting significantly higher energy efficiency.