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Title: Modeling the temporal network dynamics of neuronal cultures

Journal Article · · PLoS Computational Biology (Online)
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  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
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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.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
AC52-07NA27344; LDRD-17-SI-002
OSTI ID:
1632047
Alternate ID(s):
OSTI ID: 1630783; OSTI ID: 1634303
Report Number(s):
LLNL-JRNL-774226; 966883
Journal Information:
PLoS Computational Biology (Online), Vol. 16, Issue 5; ISSN 1553-7358
Publisher:
Public Library of ScienceCopyright Statement
Country of Publication:
United States
Language:
English

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  • De Vico Fallani, Fabrizio; Richiardi, Jonas; Chavez, Mario
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Figures / Tables (12)

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59 BASIC BIOLOGICAL SCIENCES
biological and medical sciences
computer science
community structure
neural networks
neurons
extracellular matrix
hippocampus
action potentials
electrophysiology
network analysis