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Establishing a Statistical Link between Network Oscillations and Neural Synchrony

Zhou, P and Burton, SD and Snyder, AC and Smith, MA and Urban, NN and Kass, RE (2015) Establishing a Statistical Link between Network Oscillations and Neural Synchrony. PLoS Computational Biology, 11 (10). ISSN 1553-734X

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Abstract

© 2015 Zhou et al. 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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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zhou, P
Burton, SD
Snyder, AC
Smith, MAsmithma@pitt.eduSMITHMA
Urban, NNnurban@pitt.eduNURBAN0000-0002-0365-9068
Kass, RE
Date: 1 January 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: PLoS Computational Biology
Volume: 11
Number: 10
DOI or Unique Handle: 10.1371/journal.pcbi.1004549
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Neurobiology
School of Medicine > Ophthalmology
Refereed: Yes
ISSN: 1553-734X
Date Deposited: 23 Aug 2016 13:43
Last Modified: 04 Feb 2019 15:58
URI: http://d-scholarship.pitt.edu/id/eprint/28510

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