Evolving Neural Circuits for Behavior: C. elegans Locomotion

One of the grand scientific challenges of this century is to understand how behavior is grounded in the interaction between an organism’s brain, its body, and its environment. Although a lot of attention and resources are focused on understanding the human brain, I will argue that the study of simpler organisms are an ideal place to begin to address this challenge. I will introduce the nematode worm Caernohabditis elegans, with just 302 neurons, the only fully-reconstructed connectome at the cellular level, and a rich behavioral repertoire that we are still discovering. I will describe a computational approach to address such grand challenge. I will lay out some of the advantages of expressing our understanding in equations and computational models rather than just words. I will describe our unique methodology for exploring the unknown biological parameters of the model through the use of evolutionary algorithms. We train the neural networks on what they should do, with little or no instructions on how to do it. The effort is then to analyze and understand the evolved solutions as a way to generate novel, often unexpected, hypotheses. As an example, I will focus on how the rhythmic pattern is both generated and propagated along the body during locomotion.