eprintid: 2396
rev_number: 7
eprint_status: archive
userid: 6
dir: disk0/00/00/23/96
datestamp: 2014-12-04 09:16:12
lastmod: 2014-12-04 09:16:12
status_changed: 2014-12-04 09:16:12
type: article
metadata_visibility: show
creators_name: Perotti, Juan I.
creators_name: Tamarit, Francisco A.
creators_name: Cannas, Sergio A.
creators_id: juanignacio.perotti@imtlucca.it
creators_id: 
creators_id: 
title: A scale-free neural network for modelling neurogenesis 
ispublished: pub
subjects: QC
divisions: EIC
full_text_status: public
keywords: Neural networks; Scale-free networks; Strong dilution
note: LAWNP 2005: IX Latin American Workshop on Nonlinear Phenomena 
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. 
date: 2006-11
date_type: published
publication: Physica A: Statistical Mechanics and its Applications
volume: 371
number: 1
publisher: Elsevier
pagerange: 71 - 75
id_number: 10.1016/j.physa.2006.04.079
refereed: TRUE
issn: 0378-4371
official_url: http://www.sciencedirect.com/science/article/pii/S0378437106005000
citation:   Perotti, Juan I. and Tamarit, Francisco A. and Cannas, Sergio A.  A scale-free neural network for modelling neurogenesis.  Physica A: Statistical Mechanics and its Applications, 371 (1).  71 - 75.  ISSN 0378-4371      (2006)  
document_url: http://eprints.imtlucca.it/2396/1/Perotti_preprint_2006.pdf