Emergence of Memory-Driven Command Neurons in Evolved Artificial Agents

Abstract

Using evolutionary simulations we develop autonomous agents controlled by articial neural networks (ANNs). In simple life-like tasks of foraging and navigation, high performance levels are attained by agents equipped with fully-recurrent ANN controllers. In a set of experiments sharing the same behavioural task but diering in the sensory input available to the agents, we nd a common structure of a command neuron switching the dynamics of the network between radically dierent behavioural modes. When sensory position information is available the command neuron reects a map of the environment, acting as a location-dependent cell sensitive to the location and orientation of the agent. When such information is unavailable the command neuron's activity is based on a spontaneously evolving short-term memory mechanism, which underlies its apparent place-sensitive activity. A two-parameter stochastic model for this memory mechanism is proposed. We show that the parameter value..