The emergence of connectivity in neuronal networks: From bootstrap percolation to auto-associative memory
(2012) In Brain Research 1434. p.277-284- Abstract
- We consider a random synaptic pruning in an initially highly interconnected network. It is proved that a random network can maintain a self-sustained 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 out-connections. It is also shown that the set of parameters which admits a self-sustained activity level is rather small within the whole space of possible parameters. It is pointed out here that the threshold of connectivity for an auto-associative 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 self-sustained 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 out-connections. It is also shown that the set of parameters which admits a self-sustained activity level is rather small within the whole space of possible parameters. It is pointed out here that the threshold of connectivity for an auto-associative 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 auto-associative 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:
https://lup.lub.lu.se/record/2515255
- author
- Turova, Tatyana LU
- organization
- publishing date
- 2012
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Integrate-and-fire network, Random graph, Storage capacity, Percolation
- in
- Brain Research
- volume
- 1434
- pages
- 277 - 284
- publisher
- Elsevier
- external identifiers
-
- wos:000301559700025
- scopus:84856609728
- pmid:21875700
- ISSN
- 1872-6240
- DOI
- 10.1016/j.brainres.2011.07.050
- language
- English
- LU publication?
- yes
- id
- 73d84fcb-6818-42f6-b310-d8858ae1d051 (old id 2515255)
- date added to LUP
- 2016-04-01 10:47:10
- date last changed
- 2022-04-20 06:14:11
@article{73d84fcb-6818-42f6-b310-d8858ae1d051,
abstract = {{We consider a random synaptic pruning in an initially highly interconnected network. It is proved that a random network can maintain a self-sustained 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 out-connections. It is also shown that the set of parameters which admits a self-sustained activity level is rather small within the whole space of possible parameters. It is pointed out here that the threshold of connectivity for an auto-associative 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 auto-associative 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 = {{1872-6240}},
keywords = {{Integrate-and-fire network; Random graph; Storage capacity; Percolation}},
language = {{eng}},
pages = {{277--284}},
publisher = {{Elsevier}},
series = {{Brain Research}},
title = {{The emergence of connectivity in neuronal networks: From bootstrap percolation to auto-associative memory}},
url = {{http://dx.doi.org/10.1016/j.brainres.2011.07.050}},
doi = {{10.1016/j.brainres.2011.07.050}},
volume = {{1434}},
year = {{2012}},
}