2021 CNS NEURON + NetPyNE tutorial

Course Information


3 July 2021


CNS Conference (online)


Robert McDougal; Salvador Dura-Bernal; Bill Lytton


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 8.0 simulator [1], including the Reaction-Diffusion (RxD) module [2,3], and the NetPyNE tool [4]. The tutorial will combine 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. The tutorial will use these tools on the cloud, so no software installation is necessary.

Software tools

NEURON https://neuron.yale.edu
RxD https://neuron.yale.edu/neuron/docs/reaction-diffusion
NetPyNE https://netpyne.org


Time (New York City / EDT) Presenter Subject
10:00 - 10:30 Bill Lytton Overview: implementing the conceptual model
10:30 - 11:50 Robert McDougal NEURON scripting basics
11:50 - 12:00 coffee break  
12:00 - 1:00 Adam Newton Reaction-Diffusion
1:00 - 1:30 lunch break  
1:30 - 2:50 Salvador Dura-Bernal NetPyNE GUI-based tutorials
2:50 - 3:00 coffee break  
3:00 - 4:00 Salvador Dura-Bernal NetPyNE programming tutorial

References and background

New to Python?

If you’re new to Python programming, there are many excellent tutorials online. Here's some material from an informatics course taught by one of the instructors (Prof. McDougal):

Basic calculations, variables, data types Lecture (20m 2s) Colab notebook Exercises Solutions
Functions, Methods, f-strings Lecture (24m 12s) Colab notebook Exercises Solutions
Looping (for loops) and making choices (if statements) Lecture (30m 23s) Colab notebook Exercises Solutions
Loading and using libraries (modules) Lecture (9m 34s) Slides Exercises Solutions
Loading and manipulating data with pandas Lecture (36m 37s) Slides Exercises Solutions
Visualizing data with ggplot Lecture (15m 3s) Slides Exercises Solutions


Key papers

[1] Hines M, Carnevale T, McDougal RA. (2020) NEURON Simulation Environment. In: Jaeger D., Jung R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. doi:10.1007/978-1-4614-7320-6_795-2 

[2] McDougal RA, Hines ML, Lytton WW. (2013) Reaction-diffusion in the NEURON simulator. Front. Neuroinform. 7, 28. doi:10.3389/fninf.2013.00028

[3] Newton AJH, McDougal RA, Hines ML, Lytton WW (2018) Using NEURON for Reaction-Diffusion: Modeling of Extracellular Dynamics. Front. Neuroinform. 12, 41. doi: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 doi:10.7554/eLife.44494


Course material


Runnable code notebooks:

Video resources:

How to register

Register for the tutorials at the CNS website: https://www.cnsorg.org/cns-2021-quick