Robotic implementation of classical and Operant Conditioning as a single STDP learning process

摘要

A robot is presented whose behavior is based on two fundamental types of learning in the animal world: Classical Conditioning (CC) and Operant Conditioning (OC). It is shown how both share Spike-Timing-Dependent-Plasticity (STDP) as learning process for a Spiking Neural Network (SNN). STDP was implemented on a Field-Programmable Gate Array (FPGA) with very low-demanding resources, using an adaptation of the Synapto-dendritic Kernel Adapting Neuron (SKAN) model. Moreover, it is shown how a 3-way version of STDP is needed to allow for OC. The robot was designed to use the CC and OC neuronal architectures proposed in this paper and was tested in a dynamic environment, which consisted of a maze with changing features. It was successful in presenting both types of learning. This paper thus validates an architecture with an important potential for very large scale time-dependent parallel data analysis, with high capacity of adaptation in a dynamic environment.