%0 Book Section %A Yusoff, Nooraini %E Tingwen, Huang %I Springer %D 2012 %T Learning anticipation through priming in spatio-temporal neural networks %U http://repo.uum.edu.my/12488/ %U http://dx.doi.org/10.1007/978-3-642-34475-6_21 %X 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.