Increased swimming control with evolved lamprey CPG controllers

This paper shows that the lamprey's neural swimming controller (central pattern generator (CPG)) is not a unique solution and that improved performance can be obtained by evolving the CPG's neural parameters and connection weights. Propulsion in the lamprey, an eel-like fish, is governed by activity in its spinal neural network. This CPG is simulated, in accordance with Ekeberg's model, and then evolved using genetic algorithm techniques to explore the domain of alternative configurations. Results indicate several suitable controllers exist. The best controller from forty experiments produces a controllable frequency range of 1.42 - 12.16 Hz, a substantial increase on the biological network frequency range (1.74 - 5.56 Hz). Internal connectivity of the evolved network is 16 connections, lower than the 26 connections of Ekeberg's model. Thus, evolved networks can operate over a wider frequency range (than their biological prototype and controllers evolving weights alone) whilst maintaining low system complexity. Evolving advanced yet simple controllers provides solutions which are more attainable in silicon (VLSI), improves performance for task specific control and determines the extent to which nature's solutions are unique.