Letters: Synaptic plasticity model of a spiking neural network for reinforcement learning

Authors:
Kyoobin Lee;Dong-Soo Kwon
Affiliations:
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), ME 3042, 373-1 GuSung Dong YuSung Gu, Daejon 305-701, Republic of Korea;Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), ME 3042, 373-1 GuSung Dong YuSung Gu, Daejon 305-701, Republic of Korea
Venue:
Neurocomputing
Year:
2008

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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.