Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/5268
Title: Self-organization of a neural network with heterogeneous neurons enhances coherence and stochastic resonance
Authors: Li, Xiumin
Zhang, Jie
Small, Michael
Subjects: Neural nets
Stochastic processes
Topology
Issue Date: Mar-2009
Publisher: American Institute of Physics
Source: Chaos: an interdisciplinary journal of nonlinear science, Mar. 2009, v. 19, no. 1, 013126, p. 1-6.
Abstract: Most network models for neural behavior assume a predefined network topology and consist of almost identical elements exhibiting little heterogeneity. In this paper, we propose a self-organized network consisting of heterogeneous neurons with different behaviors or degrees of excitability. The synaptic connections evolve according to the spike-timing dependent plasticity mechanism and finally a sparse and active-neuron-dominant structure is observed. That is, strong connections are mainly distributed to the synapses from active neurons to inactive ones. We argue that this self-emergent topology essentially reflects the competition of different neurons and encodes the heterogeneity. This structure is shown to significantly enhance the coherence resonance and stochastic resonance of the entire network, indicating its high efficiency in information processing.
Rights: © 2009 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in X. Li, J. Zhang & M. Small, Chaos: an interdisciplinary journal of nonlinear science 19, 013126 (2009) and may be found at http://link.aip.org/link/?cha/19/013126
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/5268
DOI: 10.1063/1.3076394
ISSN: 1054-1500 (print)
1089-7682 (online)
Appears in Collections:EIE Journal/Magazine Articles

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