• N. Yusoff School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia
  • F. Kabir Ahmad School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia
  • N. ChePa School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia
  • A. Ab Aziz School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia




Associative learning, stimulus-stimulus association, spatio-temporal neural networks, spike-timing dependent plasticity


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. 


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How to Cite

LEARNING STIMULUS-STIMULUS ASSOCIATION IN SPATIO-TEMPORAL NEURAL NETWORKS. (2015). Jurnal Teknologi, 77(5). https://doi.org/10.11113/jt.v77.6126