Analysis of a Dynamical Recurrent Neural Network Evolved for Two Qualitatively Different Tasks: Walking and Chemotaxis

Abstract

Living organisms perform a broad range of different behaviours during their lifetime. It is important that these be coordinated such as to perform the appropriate one at the right time. This paper extends previous work on evolving dynamical recurrent neural networks by synthesizing a single circuit that performs two qualitatively different behaviours: orientation to sensory stimuli and legged locomotion. We demonstrate that small fully interconnected networks can solve these two tasks without providing a priori structural modules, explicit neural learning mechanisms, or an external signal for when to switch between them. Dynamical systems analysis of the best-adapted circuit explains the agent’s ability to switch between the two behaviours from the interactions of the circuit’s neural dynamics, its body and environment.