Single SNN Architecture for Classical and Operant Conditioning using Reinforcement Learning

Etienne Dumesnil, Philippe-Olivier Beaulieu and Mounir Boukadoum
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Etienne Dumesnil: University of Quebec at Montreal, Montreal, Canada
Philippe-Olivier Beaulieu: University of Quebec at Montreal, Montreal, Canada
Mounir Boukadoum: Department of Computer Science, University of Quebec at Montreal, Montreal, Canada

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2017, vol. 11, issue 2, 1-24

Abstract: A bio-inspired robotic brain is presented where the same spiking neural network (SNN) can implement five variations of learning by conditioning (LC): classical conditioning (CC), and operant conditioning (OC) with positive/negative reinforcement/punishment. In all cases, the links between input stimuli, output actions, reinforcements and punishments are strengthened depending on the stability of the delays between them. To account for the parallel processing nature of neural networks, the SNN is implemented on a field-programmable gate array (FPGA), and the neural delays are extracted via an adaptation of the synapto-dendritic kernel adapting neuron (SKAN) model, for a low resource demanding FPGA implementation of the SNN. A custom robotic platform successfully tested the ability of the proposed architecture to implement the five LC behaviors. Hence, this work contributes to the engineering field by proposing a scalable low resource demanding architecture for adaptive systems, and the cognitive field by suggesting that both CC and OC can be modeled as a single cognitive architecture.

Date: 2017
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