Synaptic plasticity model of a spiking neural network for reinforcement learning

Abstract

This paper presents a reward-related synaptic modification method of a spiking neuron model. The proposed algorithm determines which synapse is eligible for reinforcement by a reward signal. According to the proposed algorithm, a synapse is determined to be eligible when a presynaptic spike occurs shortly before a postsynaptic spike. A pre- and postsynaptic spike correlator (PPSC) is defined and used to determine synaptic eligibility, and to modify synaptic efficacy in cooperation with a reward signal. A simulation is conducted to demonstrate how the interaction between the PPSC and the reward signal influences synaptic plasticity.