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18-Aug-2003

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Emergence of Scale-free Properties in Hebbian Networks, arXiv
 









Abstract: The fundamental `plasticity' of the nervous system (i.e high
adaptability at different structural levels) is primarily based on Hebbian
learning mechanisms that modify the synaptic connections. The modifications rely
on neural activity and assign a special dynamic behavior to the neural networks.
Another striking feature of the nervous system is that spike based information
transmission, which is supposed to be robust against noise, is noisy in itself:
the variance of the spiking of the individual neurons is surprisingly large
which may deteriorate the adequate functioning of the Hebbian mechanisms. In
this paper we focus on networks in which Hebbian-like adaptation is induced only
by external random noise and study spike-timing dependent synaptic plasticity.
We show that such `HebbNets' are able to develop a broad range of network
structures, including scale-free small-world networks. The development of such
network structures may provide an explanation of the role of noise and its
interplay with Hebbian plasticity. We also argue that this model can be seen as
a unification of the famous Watts-Strogatz and preferential attachment models of
small-world nets.
Source: Emergence of Scale-free Properties in Hebbian Networks[
http://arXiv.org/abs/nlin.AO/0308013 ], Gabor Szirtes, Zsolt Palotai, Andras
Lorincz, DOI: nlin.AO/0308013, arXiv, 2003-08-11

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