Evolving a strongly recurrent neural network to simulate biological neurons

In this research we use evolutionary techniques to evolve recurrent neural networks that produce a pulsed output when triggered by a constant valued input. Networks of several different sizes and configurations are successfully evolved demonstrating that this is a robust technique. The resultant networks can be used as approximations of certain types of biological neurons or of central pattern generators.