International Symposium on Nonlinear Theory and its Applications


Session Number:A3L-B



Effect of heterogeneity for the self-organization of a neural network

Xiumin Li,  Jie Zhang,  Michael Small,  


Publication Date:2008/9/7

Online ISSN:2188-5079


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In this paper, a self-organized neural network consisting of neurons with heterogeneous dynamics is proposed. The heterogeneity is introduced into the network by choosing the key parameter from a uniform distribution covering a wide variety of neuronal behavior. In particular, the synaptic matrix evolves according to the spike-timing dependent plasticity (STDP) mechanism and finally leads to synchronous spiking and a sparse connection. We argue that the self-emergent topology with active individuals having strong out-degree synapses essentially reflects the competition of different neurons and encodes the heterogeneity. And in order to test the efficiency of this self-organized network in signal processing, we have made comparisons to three other networks of different topologies in terms of coherence resonance (CR) and stochastic resonance (SR), which have been analyzed in various neural networks recently. It is shown that the network obtained from STDP learning can enhance the CR and SR of the entire network, indicating its high efficiency in information processing.