Modeling the temporal network dynamics of neuronal cultures

PLoS Comput Biol. 2020 May 26;16(5):e1007834. doi: 10.1371/journal.pcbi.1007834. eCollection 2020 May.

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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Action Potentials*
  • Animals
  • Cells, Cultured
  • Cerebral Cortex / cytology
  • Electrodes
  • Hippocampus / cytology
  • Markov Chains
  • Mice
  • Models, Neurological*
  • Nerve Net / physiology*
  • Neuroglia / cytology
  • Neurons / physiology*
  • Normal Distribution
  • Probability
  • Rats
  • Stochastic Processes
  • Time Factors

Grants and funding

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and supported by LDRD 17-SI-002. Release Number: LLNL-JRNL-774226. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.