A Developmental Method for Evolving Large-Scale Spiking Neural Networks

Abstract

The brains contain billions of neurons and trillions of connections through natural evolution,and appear rather complex intelligent behavior.Researchers in developmental encoding,which is a branch of evolutionary neural networks motivated by the evolution and development of biological brains,often point out that genetic reuse allows searching the large-scale neural networks through a lower dimensional genotypic space.Using the artificial genome model as a framework for describing genetic regulatory networks,the dynamics of gene expression can be treated as a model for cell fate specification.We propose a developmental method for evolving large-scale spiking neural networks.The advantage of this method is that it can facilitate fast and efficient development of spiking neurons,neural connections,and synaptic plasticities.The corresponding evolutionary experiment shows that the intelligent behavior emergences for the neurally-driven autonomous agents in a food gathering task.Additionally,it also shows that due to the efficiency of the proposed method,large-scale spiking neural networks can be easily managed thereby making it suitable for long durational evolutionary experiments.