Yusoff, Nooraini and Kabir Ahmad, Farzana (2013) Stimulus-stimulus association via reinforcement learning in spiking neural network. In: 2013 13th International Conference on Intelligent Systems Design and Applications (ISDA), 8-10 Dec. 2013, Selangor, Malaysia.

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Abstract

In this paper, we propose an algorithm that performs stimulus-stimulus association via reinforcement learning.In particular, we develop a recurrent network with dynamic properties of Izhikevich spiking neuron model and train the network to associate a stimulus pair using reward modulated spike-time dependent plasticity.The learning algorithm associates a prime stimulus, known as the predictor, with a second stimulus, known as the choice, comes after an inter-stimulus interval.The influence of the prime stimulus on the neural response after the onset of the later stimulus is then observed.A series of probe trials resemble the retrospective and prospective activities in human response processing

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Print ISBN:978-1-4799-3515-4
Uncontrolled Keywords:	component;associative learning,spiking neural network, reinforcement learning, spike-time dependent plasticity, priming effect
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	College of Arts and Sciences
Depositing User:	Dr. Nooraini Yusoff
Date Deposited:	26 Oct 2014 06:50
Last Modified:	26 Oct 2014 06:50
URI:	https://repo.uum.edu.my/id/eprint/12504

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