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Learning anticipation through priming in spatio-temporal neural networks

Yusoff, Nooraini and Grüning, André (2012) Learning anticipation through priming in spatio-temporal neural networks. In: Neural Information Processing. Lecture Notes in Computer Science, 7663 (7663). Springer, pp. 168-175. ISBN 978-3-642-34474-9

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In this paper, we propose a reward-based learning model inspired by the findings from a behavioural study and biologically realistic properties of spatio-temporal neural networks.The model simulates the cognitive priming effect in stimulus-stimulus-response association.Synaptic plasticity is dependent on a global reward signal that enhances the synaptic changes derived from spike-timing dependent plasticity (STDP) process.We show that by priming a network with a cue stimulus can facilitate the response to a later stimulus.The network can be trained to associate a stimulus pair (with an inter-stimulus interval) to a response, as well as to recognise the temporal sequence of the stimulus presentation.

Item Type: Book Section
Additional Information: Book Subtitle: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part I
Uncontrolled Keywords: Reward-based learning, Spiking neural networks, Priming effect, Associative learning
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences
Depositing User: Dr. Nooraini Yusoff
Date Deposited: 26 Oct 2014 02:29
Last Modified: 26 Oct 2014 02:29
URI: http://repo.uum.edu.my/id/eprint/12488

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