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On a Gaussian neuronal field model

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Lu, Wenlian, Rossoni, Enrico and Feng, Jianfeng (2010) On a Gaussian neuronal field model. NeuroImage, Vol.52 (No.3). pp. 913-933. doi:10.1016/j.neuroimage.2010.02.075 ISSN 1053-8119.

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Official URL: http://dx.doi.org/10.1016/j.neuroimage.2010.02.075

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Abstract

Can we understand the dynamic behaviour of leaky integrate-and-fire (LIF) networks, which present the major, and possibly the only, analytically tractable tool we employ in computational neuroscience? To answer this question, here we present a theoretical framework on the spike activities of LIF networks by including the first order moment (mean firing rate) and the second order moment statistics (variance and correlation), based on a moment neuronal network (MNN) approach. The spike activity of a LIF network is approximated as a Gaussian random field and can reduce to the classical Wilson-Cowan-Amari (WCA) neural field if the variances vanish. Our analyses reveal several interesting phenomena of LIF networks. With a small clamped correlation and strong inhibition, the firing rate response function could be non-monotonic (not sigmoidal type), which can lead to interesting dynamics. For a feedforward and recurrent neuronal network, our setup allows us to prove that all neuronal spike activities rapidly synchronize, a well-known fact observed in both experiments and numerical simulations. We also present several examples of wave propagations in this field model. Finally, we test our MNN with the content-dependent working memory setting. The potential application of this random neuronal field idea to account for many experimental data is also discussed. (C) 2010 Elsevier Inc. All rights reserved.

Item Type: Journal Article
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
R Medicine
Divisions: Faculty of Science, Engineering and Medicine > Science > Centre for Scientific Computing
Faculty of Science, Engineering and Medicine > Science > Computer Science
Journal or Publication Title: NeuroImage
Publisher: Elsevier
ISSN: 1053-8119
Official Date: September 2010
Dates:
DateEvent
September 2010Published
Volume: Vol.52
Number: No.3
Number of Pages: 21
Page Range: pp. 913-933
DOI: 10.1016/j.neuroimage.2010.02.075
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: National Natural Sciences Foundation of China, Shanghai Pujiang Program, Engineering and Physical Sciences Research Council (EPSRC), European Commission
Grant number: 60804044, 08PJ14019, 213219

Data sourced from Thomson Reuters' Web of Knowledge

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