A Computational Model of Match Decision-Making Problem Using Spiking SHESN with Reward-Modulated Reinforcement Learning

Match decision-making problem is one of the hot topics in the field of computational neuroscience. In this paper, we propose a spiking SHESN model with reward-modulated reinforcement learning so as to conduct computational modeling and prediction of such an open problem in a manner that has more neurophysiological characteristics. Neural coding of two sequentially-presented stimuli is read out from a collection of clustered neural populations in state reservoir through reward-modulated reinforcement learning. To evaluate match decision-making performance of our computational model, we set up three kinds of test datasets with different spike timing trains and present a criterion of maximum correlation coefficient for assessing whether match/nonmatch decision-making is successful or not. Finally, extensive experimental results show that the proposed model has strong robustness on interval of both spike timings and spike shift, which is consistent with monkey’s behavior records exhibited in match decision-making experiment [1].