Action Selection and Operant Conditioning: A Neurorobotic Implementation

Action selection (as) is thought to represent the mechanism involved by natural agents when deciding what should be the next move or action. is there a functional elementary core sustaining this cognitive process? could we reproduce the mechanism with an artificial agent and more specifically in a neurorobotic paradigm? unsupervised autonomous robots may require a decision-making skill to evolve in the real world and the bioinspired approach is the avenue explored through this paper. we propose simulating an as process by using a small spiking neural network (snn) as the lower neural organisms,in order to control virtual and physical robots. we base our as process on a simple central pattern generator (cpg),decision neurons,sensory neurons,and motor neurons as the main circuit components. as novelty,this study targets a specific operant conditioning (oc) context which is relevant in an as process; choices do influence future sensory feedback. using a simple adaptive scenario,we show the complementarity interaction of both phenomena. we also suggest that this as kernel could be a fast track model to efficiently design complex snn which include a growing number of input stimuli and motor outputs. our results demonstrate that merging as and oc brings flexibility to the behavior in generic dynamical situations. © 2015 a. cyr and f. thériault.