Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29366
Title: Enhancement of signal sensitivity in a heterogeneous neural network refined from synaptic plasticity
Authors: Li, X
Small, M
Issue Date: 2010
Publisher: IOP Publishing Ltd
Source: New journal of physics, 2010, v. 12, 83045 How to cite?
Journal: New Journal of Physics 
Abstract: Long-term synaptic plasticity induced by neural activity is of great importance in informing the formation of neural connectivity and the development of the nervous system. It is reasonable to consider selforganized neural networks instead of prior imposition of a specific topology. In this paper, we propose a novel network evolved from two stages of the learning process, which are respectively guided by two experimentally observed synaptic plasticity rules, i.e. the spike-timing-dependent plasticity (STDP) mechanism and the burst-timing-dependent plasticity (BTDP) mechanism. Due to the existence of heterogeneity in neurons that exhibit different degrees of excitability, a two-level hierarchical structure is obtained after the synaptic refinement. This self-organized network shows higher sensitivity to afferent current injection compared with alternative archetypal networks with different neural connectivity. Statistical analysis also demonstrates that it has the smallworld properties of small shortest path length and high clustering coefficients. Thus the selectively refined connectivity enhances the ability of neuronal communications and improves the efficiency of signal transmission in the network.
URI: http://hdl.handle.net/10397/29366
DOI: 10.1088/1367-2630/12/8/083045
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