NetpyNE, a tool for data-driven multiscale modeling of brain circuits

Salvador Dura-Bernal*, Benjamin A. Suter, Padraig Gleeson, Matteo Cantarelli, Adrian Quintana, Facundo Rodriguez, David J. Kedziora, George L. Chadderdon, Cliff C. Kerr, Samuel A. Neymotin, Robert A. McDougal, Michael Hines, Gordon M.G. Shepherd, William W. Lytton

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne. org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

Original languageEnglish (US)
Article numbere44494
StatePublished - Apr 2019

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Neuroscience


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