A scale-free neural network for modelling neurogenesis

Juan I. Perotti, Francisco A. Tamarit and Sergio A. Cannas

Physica A: Statistical Mechanics and its Applications, 2006, vol. 371, issue 1, 71-75

Abstract: 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.

Keywords: Neural networks; Scale-free networks; Strong dilution (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:371:y:2006:i:1:p:71-75

DOI: 10.1016/j.physa.2006.04.079

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