Building mechanistic multiscale models, from molecules to networks, using NEURON and NetPyNE

CNS*2020 Tutorial — 18 July 2020

Organization for Computational Neuroscience (www.cnsorg.org)

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Organizers

  • Salvador Dura-Bernal (State University of New York Downstate, USA)

  • Robert A. McDougal (Yale University, USA)

  • Joe W. Graham (State University of New York Downstate, USA)

  • William W. Lytton (State University of New York Downstate, USA)

Brief Description

Understanding brain function requires characterizing the interactions occurring across many temporal and spatial scales. Mechanistic multiscale modeling aims to organize and explore these interactions. In this way, multiscale models provide insights into how changes at molecular and cellular levels, caused by development, learning, brain disease, drugs, or other factors, affect the dynamics of local networks and of brain areas. Large neuroscience data-gathering projects throughout the world (e.g. US BRAIN, EU HBP, Allen Institute) are making use of multiscale modeling, including the NEURON ecosystem, to better understand the vast amounts of information being gathered using many different techniques at different scales.

This tutorial will introduce multiscale modeling using two NIH-funded tools: the NEURON simulator [1], including the Reaction-Diffusion (RxD) module [2,3], and the NetPyNE tool [4]. The tutorial will include background, examples and hands on exercises covering the implementation of models at four key scales: (1) intracellular dynamics (e.g. calcium buffering, protein interactions), (2) single neuron electrophysiology (e.g. action potential propagation), (3) neurons in extracellular space (e.g. spreading depression), and (4) networks of neurons. For network simulations, we will use NetPyNE, a high-level interface to NEURON supporting both programmatic and GUI specification that facilitates the development, parallel simulation, and analysis of biophysically detailed neuronal circuits. We conclude with an example combining all three tools that links intracellular molecular dynamics with network spiking activity and local field potentials.

Basic familiarity with Python is recommended. No prior knowledge of NEURON or NetPyNE is required, however participants are encouraged to download and install each of these packages prior to the tutorial.

References

[1] Lytton WW, Seidenstein AH, Dura-Bernal S, McDougal RA, Schürmann F, Hines ML. Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON. Neural Comput. 28, 2063–2090, 2016.

[2] McDougal R, Hines M, Lytton W. (2013) Reaction-diffusion in the NEURON simulator. Front. Neuroinform. 7, 28. 10.3389/fninf.2013.00028

[3] Newton AJH, McDougal RA, Hines ML and Lytton WW (2018) Using NEURON for Reaction-Diffusion Modeling of Extracellular Dynamics. Front. Neuroinform. 12, 41. 10.3389/fninf.2018.00041

[4] Dura-Bernal S, Suter B, Gleeson P, Cantarelli M, Quintana A, Rodriguez F, Kedziora DJ, Chadderdon GL, Kerr CC, Neymotin SA, McDougal R, Hines M, Shepherd GMG, Lytton WW. (2019) NetPyNE: a tool for data-driven multiscale modeling of brain circuits. eLife 2019;8:e44494

Schedule

18 July 2020 — Full day Berlin Time NYC Time

NEURON Basics 4pm to 5pm 10am to 11am

RxD 5pm to 6:30pm 11am to 12:30pm

ModelDB 6:30pm to 7pm 12:30pm to 1pm

— Short break —

Introduction to NetPyNE 7pm to 7:30pm 1pm to 1:30pm

NetPyNE GUI-based tutorials: 7:30pm to 9pm 1:30pm to 3pm

  • Simple network

  • Morphologically-detailed cell network

  • Multi-scale network: from RxD to LFP

NetPyNE programmatic tutorial: 9pm to 10pm 3pm to 4pm

  • Oscillatory network