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Establishing a Statistical Link between Network Oscillations and Neural Synchrony

Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spik...

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Autores principales: Zhou, Pengcheng, Burton, Shawn D., Snyder, Adam C., Smith, Matthew A., Urban, Nathaniel N., Kass, Robert E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4605746/
https://www.ncbi.nlm.nih.gov/pubmed/26465621
http://dx.doi.org/10.1371/journal.pcbi.1004549
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author Zhou, Pengcheng
Burton, Shawn D.
Snyder, Adam C.
Smith, Matthew A.
Urban, Nathaniel N.
Kass, Robert E.
author_facet Zhou, Pengcheng
Burton, Shawn D.
Snyder, Adam C.
Smith, Matthew A.
Urban, Nathaniel N.
Kass, Robert E.
author_sort Zhou, Pengcheng
collection PubMed
description Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.
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spelling pubmed-46057462015-10-29 Establishing a Statistical Link between Network Oscillations and Neural Synchrony Zhou, Pengcheng Burton, Shawn D. Snyder, Adam C. Smith, Matthew A. Urban, Nathaniel N. Kass, Robert E. PLoS Comput Biol Research Article Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony. Public Library of Science 2015-10-14 /pmc/articles/PMC4605746/ /pubmed/26465621 http://dx.doi.org/10.1371/journal.pcbi.1004549 Text en © 2015 Zhou et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zhou, Pengcheng
Burton, Shawn D.
Snyder, Adam C.
Smith, Matthew A.
Urban, Nathaniel N.
Kass, Robert E.
Establishing a Statistical Link between Network Oscillations and Neural Synchrony
title Establishing a Statistical Link between Network Oscillations and Neural Synchrony
title_full Establishing a Statistical Link between Network Oscillations and Neural Synchrony
title_fullStr Establishing a Statistical Link between Network Oscillations and Neural Synchrony
title_full_unstemmed Establishing a Statistical Link between Network Oscillations and Neural Synchrony
title_short Establishing a Statistical Link between Network Oscillations and Neural Synchrony
title_sort establishing a statistical link between network oscillations and neural synchrony
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4605746/
https://www.ncbi.nlm.nih.gov/pubmed/26465621
http://dx.doi.org/10.1371/journal.pcbi.1004549
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