Evolution of neural controllers for salamander-like locomotion

This paper presents an experiment in which evolutionary algorithms are used for the development of neural controllers for salamander locomotion. The aim of the experiment is to investigate which kind of neural circuitry can produce the typical swimming and trotting gaits of the salamander, and to develop a synthetic approach to neurobiology by using genetic algorithms as design tool. A 2D bio-mechanical simulation of the salamander's body is developed whose muscle contraction is determined by the locomotion controller simulated as continuous-time neural networks. While the connectivity of the neural circuitry underlying locomotion in the salamander has not been decoded for the moment, the general organization of the designed neural circuits corresponds to that hypothesized by neurobiologists for the real animal. in particular, the locomotion controllers are based on a body central pattern generator (CPG) corresponding to a lamprey-like swimming controller as developed by Ekeberg,(1) and are extended with a limb CPG for controlling the salamander's body. A genetic algorithm is used to instantiate synaptic weights of the connections within the limb CPG and from the limb CPG to the body CPG given a high level description of the desired gaits. A set of biologically plausible controllers are thus developed which can produce a neural activity and locomotion gaits very similar to those observed in the real salamander. By simply varying the external excitation applied to the network, the speed, direction and type of gait can be varied.