An Evolving Artificial Neural Network for the Investigation of Rat Exploratory Behavior

The elevated plus-maze is widely used as a tool for neurobiological studies of anxiety and defense in rodents. In a previous work, an artificial neural network (ANN) with weights adjusted by a genetic algorithm (GA) was used to investigate the behaviour of rats in an elevated plus-maze. The study of the ANN´s architecture, which was fixed in the previous work, can provide insights about the role of sensory inputs and memory in models employed to investigate the behaviour of rats. In this paper, we propose an evolving ANN for this problem. The architecture of the recurrent ANN is evolved by the GA together with its weights. The experiments indicate that the evolving ANN produces better results than the fixed architecture previously investigated. Besides, the experiments indicate that only three of the six sensory units and only two of the four hidden units are used in the evolved ANN. This result is useful to understand how the rat uses the sensory information and memory while navigating in the elevated plus-maze.