AUTHOR=Cyr André , Boukadoum Mounir , Thériault Frédéric TITLE=Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot's controller JOURNAL=Frontiers in Neurorobotics VOLUME=8 YEAR=2014 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2014.00021 DOI=10.3389/fnbot.2014.00021 ISSN=1662-5218 ABSTRACT=

In this paper, we investigate the operant conditioning (OC) learning process within a bio-inspired paradigm, using artificial spiking neural networks (ASNN) to act as robot brain controllers. In biological agents, OC results in behavioral changes learned from the consequences of previous actions, based on progressive prediction adjustment from rewarding or punishing signals. In a neurorobotics context, virtual and physical autonomous robots may benefit from a similar learning skill when facing unknown and unsupervised environments. In this work, we demonstrate that a simple invariant micro-circuit can sustain OC in multiple learning scenarios. The motivation for this new OC implementation model stems from the relatively complex alternatives that have been described in the computational literature and recent advances in neurobiology. Our elementary kernel includes only a few crucial neurons, synaptic links and originally from the integration of habituation and spike-timing dependent plasticity as learning rules. Using several tasks of incremental complexity, our results show that a minimal neural component set is sufficient to realize many OC procedures. Hence, with the proposed OC module, designing learning tasks with an ASNN and a bio-inspired robot context leads to simpler neural architectures for achieving complex behaviors.