Biologically realizable reward-modulated hebbian training for spiking neural networks

Abstract

Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximators. Spiking neural networks offer several advantages over sigmoidal networks, because they can approximate the dynamics of biological neuronal networks, and can potentially reproduce the computational speed observed in biological brains by enabling temporal coding. On the other hand, the effectiveness of spiking neural network training algorithms is still far removed from that exhibited by backpropagating sigmoidal neural networks. This paper presents a novel algorithm based on reward-modulated spike-timing-dependent plasticity that is biologically plausible and capable of training a spiking neural network to learn the exclusive-or (XOR) computation, through rate-based coding. The results show that a spiking neural network model with twenty-three nodes is able to learn the XOR gate accurately, and performs the computation on time scales of milliseconds. Moreover, the algorithm can potentially be verified in light-sensitive neuronal networks grown in vitro by determining the spikes patterns that lead to the desired synaptic weights computed in silico when induced by blue light in vitro.