Evolving Networks Processing Signals with a Mixed Paradigm, Inspired by Gene Regulatory Networks and Spiking Neurons

In this paper we extend our artificial life platorm, called GReaNs (for Genetic Regulatory evolving artificial Networks) to allow evolution of spiking neural networks performign simple computational tasks. GReaNs has been previously used to model evolution of gene regulatory networks for processing signals, and also for controling the behaviour of unicallular animats and the devlopment of multicellular structures in two and three dimensions. The connectivity of the regulatory network in GReaNs is encoded in a linear genome. No explicit restrictions are set for the size of the genome or the size of the network. In our previous work, the way the nodes in the regulatory network worked was inspired by biological transcriptional units. In the extension to genes here we modify the equations governing the behaviour of the units so that they describe spiking neurons: either leaky integrate and fire neurons with a fixed threshold or adaptive-exponential integrate and fire neurons. As a proof-of-principle, we report the evolution of spiking networks that match desired spiking patterns.