In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Yusoff, N., & Grüning, A. (2010). Supervised associative learning in spiking neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6352 LNCS, pp. 224–229). https://doi.org/10.1007/978-3-642-15819-3_30
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