Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity-dependent synaptic plasticity. Here, we model neurons as discrete-state nodes on an adaptive network following stochastic dynamics. At a threshold connectivity, this system undergoes a dynamical phase transition at which persistent activity sets in. In a low-dimensional representation of the macroscopic dynamics, this corresponds to a transcritical bifurcation. We show analytically that adding activity-dependent rewiring rules, inspired by homeostatic plasticity, leads to the emergence of an attractive steady state at criticality and present numerical evidence for the system's evolution to such a state.
%0 Journal Article
%1 Droste2013Analytical
%A Droste, Felix
%A Do, Anne-Ly
%A Gross, Thilo
%D 2013
%I The Royal Society
%J Journal of The Royal Society Interface
%K neural-networks, self\_organized\_criticality critical-phenomena
%N 78
%R 10.1098/rsif.2012.0558
%T Analytical investigation of self-organized criticality in neural networks
%U http://dx.doi.org/10.1098/rsif.2012.0558
%V 10
%X Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity-dependent synaptic plasticity. Here, we model neurons as discrete-state nodes on an adaptive network following stochastic dynamics. At a threshold connectivity, this system undergoes a dynamical phase transition at which persistent activity sets in. In a low-dimensional representation of the macroscopic dynamics, this corresponds to a transcritical bifurcation. We show analytically that adding activity-dependent rewiring rules, inspired by homeostatic plasticity, leads to the emergence of an attractive steady state at criticality and present numerical evidence for the system's evolution to such a state.
@article{Droste2013Analytical,
abstract = {{Dynamical criticality has been shown to enhance information processing in dynamical systems, and there is evidence for self-organized criticality in neural networks. A plausible mechanism for such self-organization is activity-dependent synaptic plasticity. Here, we model neurons as discrete-state nodes on an adaptive network following stochastic dynamics. At a threshold connectivity, this system undergoes a dynamical phase transition at which persistent activity sets in. In a low-dimensional representation of the macroscopic dynamics, this corresponds to a transcritical bifurcation. We show analytically that adding activity-dependent rewiring rules, inspired by homeostatic plasticity, leads to the emergence of an attractive steady state at criticality and present numerical evidence for the system's evolution to such a state.}},
added-at = {2019-06-10T14:53:09.000+0200},
author = {Droste, Felix and Do, Anne-Ly and Gross, Thilo},
biburl = {https://www.bibsonomy.org/bibtex/277b75756a46cc019360a8669f85f509b/nonancourt},
citeulike-article-id = {11681245},
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citeulike-linkout-1 = {http://rsif.royalsocietypublishing.org/content/10/78/20120558.abstract},
citeulike-linkout-2 = {http://rsif.royalsocietypublishing.org/content/10/78/20120558.full.pdf},
citeulike-linkout-3 = {http://rsif.royalsocietypublishing.org/cgi/content/abstract/10/78/20120558},
citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/22977096},
citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=22977096},
day = 6,
doi = {10.1098/rsif.2012.0558},
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issn = {1742-5662},
journal = {Journal of The Royal Society Interface},
keywords = {neural-networks, self\_organized\_criticality critical-phenomena},
month = jan,
number = 78,
pmid = {22977096},
posted-at = {2013-01-13 22:51:01},
priority = {2},
publisher = {The Royal Society},
timestamp = {2019-07-31T12:26:23.000+0200},
title = {{Analytical investigation of self-organized criticality in neural networks}},
url = {http://dx.doi.org/10.1098/rsif.2012.0558},
volume = 10,
year = 2013
}