Learning stimulus-stimulus association in spatiotemporal neural networks

We propose a stimulus-stimulus association learning by coupling firing rate and precise spike timing encoding for spatio-temporal neural networks. we simulate a generic recurrent network with random and sparse connectivity consisting of izhikevich spiking neurons. the magnitude of weight adjustment in learning is dependent on pre- and postsynaptic spikes based on their spikes count and time correlation. as a result of learning,synchronisation of activity among inter- and intra-subpopulation neurons demonstrates association between two stimuli. the associations show in spill-over of activity between the two stimuli involved. © 2015 penerbit utm press. all rights reserved.