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Title: Modeling the temporal network dynamics of neuronal cultures

Abstract

Neurons form complex networks that evolve over multiple time scales. In order to thoroughly characterize these networks, time dependencies must be explicitly modeled. Here, we present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. Our model combines the class of Stochastic Block Models for community formation with Gaussian processes to model changes in the community structure as a smooth function of time. We validate our model on synthetic data and demonstrate its utility on three different studies using in vitro cultures of dissociated neurons.

Authors:
ORCiD logo; ORCiD logo; ORCiD logo; ORCiD logo; ORCiD logo; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1632047
Alternate Identifier(s):
OSTI ID: 1630783; OSTI ID: 1634303
Report Number(s):
LLNL-JRNL-774226
Journal ID: ISSN 1553-7358; 10.1371/journal.pcbi.1007834
Grant/Contract Number:  
AC52-07NA27344; LDRD-17-SI-002
Resource Type:
Published Article
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online) Journal Volume: 16 Journal Issue: 5; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; biological and medical sciences; computer science; community structure; neural networks; neurons; extracellular matrix; hippocampus; action potentials; electrophysiology; network analysis

Citation Formats

Cadena, Jose, Sales, Ana Paula, Lam, Doris, Enright, Heather A., Wheeler, Elizabeth K., Fischer, Nicholas O., and Jbabdi, ed., Saad. Modeling the temporal network dynamics of neuronal cultures. United States: N. p., 2020. Web. doi:10.1371/journal.pcbi.1007834.
Cadena, Jose, Sales, Ana Paula, Lam, Doris, Enright, Heather A., Wheeler, Elizabeth K., Fischer, Nicholas O., & Jbabdi, ed., Saad. Modeling the temporal network dynamics of neuronal cultures. United States. doi:https://doi.org/10.1371/journal.pcbi.1007834
Cadena, Jose, Sales, Ana Paula, Lam, Doris, Enright, Heather A., Wheeler, Elizabeth K., Fischer, Nicholas O., and Jbabdi, ed., Saad. Tue . "Modeling the temporal network dynamics of neuronal cultures". United States. doi:https://doi.org/10.1371/journal.pcbi.1007834.
@article{osti_1632047,
title = {Modeling the temporal network dynamics of neuronal cultures},
author = {Cadena, Jose and Sales, Ana Paula and Lam, Doris and Enright, Heather A. and Wheeler, Elizabeth K. and Fischer, Nicholas O. and Jbabdi, ed., Saad},
abstractNote = {Neurons form complex networks that evolve over multiple time scales. In order to thoroughly characterize these networks, time dependencies must be explicitly modeled. Here, we present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. Our model combines the class of Stochastic Block Models for community formation with Gaussian processes to model changes in the community structure as a smooth function of time. We validate our model on synthetic data and demonstrate its utility on three different studies using in vitro cultures of dissociated neurons.},
doi = {10.1371/journal.pcbi.1007834},
journal = {PLoS Computational Biology (Online)},
number = 5,
volume = 16,
place = {United States},
year = {2020},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: https://doi.org/10.1371/journal.pcbi.1007834

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