A scale-free neural network for modelling neurogenesis

In this work we introduce a neural network model for associative memory based on a diluted Hopfield model, which grows through a neurogenesis algorithm that guarantees that the final network is a small-world and scale-free one. We also analyze the storage capacity of the network and prove that its performance is larger than that measured in a randomly dilute network with the same connectivity.

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Year of publication:	2006
Authors:	Perotti, Juan I. ; Tamarit, Francisco A. ; Cannas, Sergio A.
Published in:	Physica A: Statistical Mechanics and its Applications. - Elsevier, ISSN 0378-4371. - Vol. 371.2006, 1, p. 71-75
Publisher:	Elsevier
Subject:	Neural networks \| Scale-free networks \| Strong dilution

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010874959