Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks

During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the defau...

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Main Authors:	Stepp, Nigel, Plenz, Dietmar, Srinivasa, Narayan
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4295840/

id	pubmed-4295840
recordtype	oai_dc
spelling	pubmed-42958402015-01-22 Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks Stepp, Nigel Plenz, Dietmar Srinivasa, Narayan Research Article During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1. Public Library of Science 2015-01-15 /pmc/articles/PMC4295840/ /pubmed/25590427 http://dx.doi.org/10.1371/journal.pcbi.1004043 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Stepp, Nigel Plenz, Dietmar Srinivasa, Narayan
spellingShingle	Stepp, Nigel Plenz, Dietmar Srinivasa, Narayan Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks
author_facet	Stepp, Nigel Plenz, Dietmar Srinivasa, Narayan
author_sort	Stepp, Nigel
title	Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks
title_short	Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks
title_full	Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks
title_fullStr	Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks
title_full_unstemmed	Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks
title_sort	synaptic plasticity enables adaptive self-tuning critical networks
description	During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4295840/
_version_	1613177087563137024

Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks

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