Evidence map›Paper›PMID 26089794›Full record

ArticleFrontiers in computational neuroscience2015

Self-organization of synchronous activity propagation in neuronal networks driven by local excitation.

Mehdi Bayati, Alireza Valizadeh, Abdolhossein Abbassian, Sen Cheng

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Article in Frontiers in computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

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0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  6. Dynamical Ising model of spatially coupled ecological oscillators.Journal of the Royal Society, Interface · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mehdi BayatiMercator Research Group "Structure of Memory", Ruhr-Universität Bochum Bochum, Germany.
Alireza ValizadehDepartment of Physics, Institute for Advanced Studies in Basic Sciences Zanjan, Iran ; School of Cognitive Sciences, Institute for Research in Fundamental Sciences Tehran, Iran.
Abdolhossein AbbassianSchool of Mathematics, Institute for Research in Fundamental Sciences Tehran, Iran.
Sen ChengMercator Research Group "Structure of Memory", Ruhr-Universität Bochum Bochum, Germany ; Department of Psychology, Ruhr-Universität Bochum Bochum, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence.

Indexed as

feed-forward networkslocally connected random networksneuronal sequencespike timing dependent plasticity (STDP)synfire chains

Identifiers

PMID26089794
PMCPMC4454885

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.