Learning anticipation through priming in spatio-temporal neural networks

Authors:
Nooraini Yusoff;André Grüning
Affiliations:
School of Computing, UUM College of Arts and Sciences, Universiti Utara Malaysia, UUM Sintok, Kedah, Malaysia;Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, UK
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Year:
2012

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Abstract

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.