The Use of Genetic Algorithms to Explore Neural Mechanisms that Optimize Rhythmic Behaviors: Quasi-Realistic Models of Feeding Behavior in Aplysia

One important function of the modulatory mechanisms present in the feeding circuits of Aplysia and other animals is to dynamically adjust the parameters of the neurons and of muscles, so as to optimize the functioning of the system. One way in which we have been exploring this hypothesis is to design elementary neural networks that perform feeding-like functions. and then investigate how the performance of the circuits varies when the system is exposed to various “challenges”. We used a genetic algorithm to “evolve ” the synaptic parameters that permit a two neuron system to generate rhythmic behavior that results in a net gain of energy. The system performs poorly and unpredictably when challenged with internal or external variability. perhaps because it lacks modulatory mechanisms of the type that occur in the real system.