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The emergence of connectivity in neuronal networks: From bootstrap percolation to auto-associative memory
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- Turova, Tatyana (author)
- Lund University,Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
- (creator_code:org_t)
- Elsevier BV, 2012
- 2012
- English.
- In: Brain Research. - : Elsevier BV. - 1872-6240 .- 0006-8993. ; 1434, s. 277-284
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Neurosciences (hsv//eng)
Keyword
- Integrate-and-fire network
- Random graph
- Storage capacity
- Percolation
Publication and Content Type
- art (subject category)
- ref (subject category)
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