NDLI: Robustness of power laws in degree distributions for spiking neural networks

Content Provider	IEEE Xplore Digital Library
Author	Piekniewski, F.
Copyright Year	2009
Description	Author affiliation: Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Torún, Poland (Piekniewski, F.)
Abstract	Power law graphs are an actively studied branch of random graph theory, motivated by a number of recent empirical discoveries which revealed power law degree distributions in a variety of networks. Power laws often coexist with some degree of self-organization either based on growth and preferential attachment (which seems to be the case in sociological/technological networks) or duplication (which seems to be the case for biological/methabolic networks). Quite recently a power law graph with exponent γ ≈ 2 has been observed in fMRI brain studies of correlations of functional centers of activity. We study the model we introduced previously to explore possible mechanisms existing in large neural networks that might lead to power law connectivity. The model (referred to as the spike flow model) resembles a kind of spiking neural network and yields a power law graph with exactly γ = 2 as a byproduct of its dynamical behavior. In this paper we investigate whether the power law is robust under certain changes to the model's dynamics. In particular we study the effect of merging the model with a random Erdős-Rényi graph which can be interpreted as an addition of long range myelinated connections. Our numerical results indicate that as long as the density of Erdős-Rényi fraction is bounded by a constant, the power law is preserved in systems of appropriate size.
Starting Page	2541
Ending Page	2546
File Size	1550399
Page Count	6
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178633
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Neural networks Biological neural networks Neurons Power system modeling Hopfield neural networks Graph theory Biological system modeling Merging Information processing
Content Type	Text
Resource Type	Article

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