Dual exploration strategies using artificial spiking neural networks in a robotic learning task

Abstract

Spatial information can be valuable, but new environments may be perceived as risky and thus often evoke fear responses and risk-averse exploration strategies such as thigmotaxis or wall-following behavior. Individual differences in risk-taking (boldness) and thigmotaxis have been reported in natural taxa, which may benefit their survival. In neurorobotic, the common approach is to reproduce cognitive phenomena with multiple levels of bio-inspiration into robotic scenarios. Since autonomous robots may benefit from these different behaviors in exploration tasks, this study aims at simulating two exploration strategies in a virtual robot controlled by a spiking neural network. The experimental context consists in a visual learning task solved through an operant conditioning procedure. Results suggest that the proposed neural architecture sustains both behaviors, switching from one to the other by external cues. This original bio-inspired model could be used as a first step toward further investigations of neurorobotic personality modulated by learning and complex exploration contexts.