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  5. Synchronization, stochasticity, and phase waves in neuronal networks with spatially-structured connectivity
 
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Synchronization, stochasticity, and phase waves in neuronal networks with spatially-structured connectivity
File(s)
fncom-14-569644.pdf (4.55 MB)
Published version
OA Location
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604323/
Author(s)
Kulkarni, Anirudh
Ranft, Jonas
Hakim, Vincent
Type
Journal Article
Abstract
Oscillations in the beta/low gamma range (10–45 Hz) are recorded in diverse neural
structures. They have successfully been modeled as sparsely synchronized oscillations
arising from reciprocal interactions between randomly connected excitatory (E) pyramidal
cells and local interneurons (I). The synchronization of spatially distant oscillatory spiking
E–I modules has been well-studied in the rate model framework but less so for modules
of spiking neurons. Here, we first show that previously proposed modifications of
rate models provide a quantitative description of spiking E–I modules of Exponential
Integrate-and-Fire (EIF) neurons. This allows us to analyze the dynamical regimes
of sparsely synchronized oscillatory E–I modules connected by long-range excitatory
interactions, for two modules, as well as for a chain of such modules. For modules with
a large number of neurons (> 105
), we obtain results similar to previously obtained ones
based on the classic deterministic Wilson-Cowan rate model, with the added bonus
that the results quantitatively describe simulations of spiking EIF neurons. However, for
modules with a moderate (∼ 104
) number of neurons, stochastic variations in the spike
emission of neurons are important and need to be taken into account. On the one hand,
they modify the oscillations in a way that tends to promote synchronization between
different modules. On the other hand, independent fluctuations on different modules tend
to disrupt synchronization. The correlations between distant oscillatory modules can be
described by stochastic equations for the oscillator phases that have been intensely
studied in other contexts. On shorter distances, we develop a description that also takes
into account amplitude modes and that quantitatively accounts for our simulation data.
Stochastic dephasing of neighboring modules produces transient phase gradients and
the transient appearance of phase waves. We propose that these stochastically-induced
phase waves provide an explanative framework for the observations of traveling waves
in the cortex during beta oscillations.
Date Issued
2020-10-19
Date Acceptance
2020-08-18
Citation
Frontiers in Computational Neuroscience, 2020, 14, pp.1-33
URI
http://hdl.handle.net/10044/1/91862
URL
https://www.frontiersin.org/articles/10.3389/fncom.2020.569644/full
DOI
https://www.dx.doi.org/10.3389/fncom.2020.569644
ISSN
1662-5188
Publisher
Frontiers Media
Start Page
1
End Page
33
Journal / Book Title
Frontiers in Computational Neuroscience
Volume
14
Copyright Statement
© 2020 Kulkarni, Ranft and Hakim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
License URL
http://creativecommons.org/licenses/by/4.0/
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000583292000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Mathematical & Computational Biology
Neurosciences
Neurosciences & Neurology
beta oscillations
synchronization
traveling waves
rate model
spiking networks
spatially structured connectivity
stochasticity
GAMMA-BAND SYNCHRONIZATION
MOTOR CORTEX
PATTERN-FORMATION
DYNAMICS
OSCILLATIONS
MECHANISMS
MODELS
Publication Status
Published
Article Number
ARTN 569644
Date Publish Online
2020-10-19
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