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Tutorials

Construction and Use of Models: Part 1. Elementary Tools

A good place to start. Introduces some of NEURON's basic GUI tools.

A convenient GUI tool for converting Eutectic, Neurolucida, and swc morphometric data into NEURON models.

How to use the CellBuilder to construct stylized (stick figure) models, manage the biophysical properties of anatomically complex models, and specify variation of biophysical properties as functions of position (distance from a point etc.).

Channel Builder tutorial

A GUI tool for creating computationally efficient models of ligand- and voltage-gated channels.

Network Builder tutorial

Suggestion: use the Network Builder to make a toy net. Then click on its "Hoc file" button to create a file that you can mine for reusable code (e.g. so you can build a much bigger net algorithmically).

Multiple Run Fitter tutorial

NEURON's built-in optimization tool.

"ModelView: Compact display of parameters for NEURON models."

This presentation of the ModelView tool is almost a tutorial. From our poster at the 2004 HBP Spring Meeting.

The hands-on exercises from the 2003 NEURON Summer Courses in San Diego and Minneapolis.

Also check the "Courses and Tutorials" page for links to lessons from other NEURON courses.

And from our friends:

NEURON Tutorial

by Andrew Gillies and David Sterratt of Edinburgh University. Starts at "bog level" and, in five installments, has you adding new biophysical mechanisms and building networks.

NEURON Tutoral from the Obidos 2004 course

by Michael Rudolph of Unite de Neurosciences Integratives et Computationnelles (UNIC) at CNRS. Two big PDFs, plus and an even bigger "examples" file that is full of code and data.
US download site: http://www.neuron.yale.edu/ftp/neuron/contrib/obidos_tutorials/ (we've compressed the examples file with gzip, but it's still almost 12 MB!).
European download site: http://cns.iaf.cnrs-gif.fr/~michael/Obidos2004Tutorials/.

NEURON with Python

A very nice set of instructions and examples for installing and using NEURON with Python, by Andrew Davison at UNIC.

Installation of NEURON with Python for Linux using the bash shell
Accessing hoc from Python
Modelling single cells in NEURON with the Python interpreter

Key papers about NEURON

"The NEURON Simulation Environment" (overviewforhbtnn2e.pdf): by Hines & Carnevale. Preprint of an "executive summary" published as
Hines, M.L. and Carnevale, N.T. The NEURON simulation environment. In: The Handbook of Brain Theory and Neural Networks, 2nd ed, edited by M.A. Arbib. Cambridge, MA: MIT Press, 2003, pp. 769-773.
PIs: read this to learn why you should use NEURON.
"The NEURON Simulation Environment": An earlier paper with the same title, expanded from our first article in Neural Computation. Full of useful information for NEURON newbies.
"NEURON: a Tool for Neuroscientists" (spacetime.pdf 215596 bytes): by Hines & Carnevale. Powerful strategies for improving spatiotemporal accuracy while preserving computational efficiency.
1. The d_lambda criterion, a simple but very effective method for specifying the spatial grid.
2. Variable order / variable timestep integration with CVODE.
"Expanding NEURON's Repertoire of Mechanisms with NMODL": by Hines & Carnevale.
"Efficient Discrete Event Simulation of Spiking Neurons in NEURON": a PDF of our poster from the 2002 SFN Meeting that describes the three classes of integrate-and-fire ("artificial") neuron models that are built into NEURON.

"Classics"

NEURON has undergone many revisions since these were written, mostly to add new features or make existing ones easier to use or more efficient. Only rarely have these changes "broken" anything. If you see something that is seriously at variance with the current version of NEURON, please check the more recent documents. We would also appreciate it if you could bring such items to our attention, so we can correct them.

User's Manual: by John W. Moore. Examples of how to use many of NEURON's older GUI tools.
Reference Manual: by Michael L. Hines