The emergence of connectivity in neuronal networks: From bootstrap percolation to autoassociative memory
(2012) In Brain Research19660101+01:00 1434. p.277284 Abstract
 We consider a random synaptic pruning in an initially highly interconnected network. It is proved that a random network can maintain a selfsustained activity level for some parameters. For such a set of parameters a pruning is constructed so that in the resulting network each neuron/node has almost equal numbers of in and outconnections. It is also shown that the set of parameters which admits a selfsustained activity level is rather small within the whole space of possible parameters. It is pointed out here that the threshold of connectivity for an autoassociative memory in a Hopfield model on a random graph coincides with the threshold for the bootstrap percolation on the same random graph. It is argued that this coincidence... (More)
 We consider a random synaptic pruning in an initially highly interconnected network. It is proved that a random network can maintain a selfsustained activity level for some parameters. For such a set of parameters a pruning is constructed so that in the resulting network each neuron/node has almost equal numbers of in and outconnections. It is also shown that the set of parameters which admits a selfsustained activity level is rather small within the whole space of possible parameters. It is pointed out here that the threshold of connectivity for an autoassociative memory in a Hopfield model on a random graph coincides with the threshold for the bootstrap percolation on the same random graph. It is argued that this coincidence reflects the relations between the autoassociative memory mechanism and the properties of the underlying random network structure. This article is part of a Special Issue entitled "Neural Coding". (C) 2011 Elsevier B.V. All rights reserved. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/2515255
 author
 Turova, Tatyana ^{LU}
 organization
 publishing date
 2012
 type
 Contribution to journal
 publication status
 published
 subject
 keywords
 Integrateandfire network, Random graph, Storage capacity, Percolation
 in
 Brain Research19660101+01:00
 volume
 1434
 pages
 277  284
 publisher
 Elsevier
 external identifiers

 wos:000301559700025
 scopus:84856609728
 ISSN
 18726240
 DOI
 10.1016/j.brainres.2011.07.050
 language
 English
 LU publication?
 yes
 id
 73d84fcb681842f6b310d8858ae1d051 (old id 2515255)
 date added to LUP
 20120507 12:37:02
 date last changed
 20180211 03:14:30
@article{73d84fcb681842f6b310d8858ae1d051, abstract = {We consider a random synaptic pruning in an initially highly interconnected network. It is proved that a random network can maintain a selfsustained activity level for some parameters. For such a set of parameters a pruning is constructed so that in the resulting network each neuron/node has almost equal numbers of in and outconnections. It is also shown that the set of parameters which admits a selfsustained activity level is rather small within the whole space of possible parameters. It is pointed out here that the threshold of connectivity for an autoassociative memory in a Hopfield model on a random graph coincides with the threshold for the bootstrap percolation on the same random graph. It is argued that this coincidence reflects the relations between the autoassociative memory mechanism and the properties of the underlying random network structure. This article is part of a Special Issue entitled "Neural Coding". (C) 2011 Elsevier B.V. All rights reserved.}, author = {Turova, Tatyana}, issn = {18726240}, keyword = {Integrateandfire network,Random graph,Storage capacity,Percolation}, language = {eng}, pages = {277284}, publisher = {Elsevier}, series = {Brain Research19660101+01:00}, title = {The emergence of connectivity in neuronal networks: From bootstrap percolation to autoassociative memory}, url = {http://dx.doi.org/10.1016/j.brainres.2011.07.050}, volume = {1434}, year = {2012}, }