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Journal of Robotics
Volume 2015, Article ID 643869, 10 pages
Research Article

Action Selection and Operant Conditioning: A Neurorobotic Implementation

Département d’Informatique, Université du Québec à Montréal (UQAM), Succursale Centre-Ville, Case Postale 8888, Montreal, QC, Canada H3C 3P8

Received 23 January 2015; Revised 7 April 2015; Accepted 14 May 2015

Academic Editor: Maki K. Habib

Copyright © 2015 André Cyr and Frédéric Thériault. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.