The recent striking expansion in the use of simulation tools in the
field of neuroscience has been encouraged by the rapid growth of quantitative
observations that both stimulate and constrain the formulation of new
hypotheses of neuronal function, and enabled by the availability of
ever-increasing computational power at low cost. These factors have motivated
the design and implementation of NEURON, the goal of which is to provide a
powerful and flexible environment for simulations of individual neurons and
networks of neurons. NEURON has special features that accommodate the complex
geometry and nonlinearities of biologically realistic models, without
interfering with its ability to handle more speculative models that involve a
high degree of abstraction.
As we note in this paper, one particularly advantageous feature is that the user can specify the physical properties of a cell without regard for the strictly computational concern of how many compartments are employed to represent each of the cable sections. In a future publication we will examine how the NMODL translator is used to define new membrane channels and calculate ionic concentration changes. Another will describe the Vector class. In addition to providing very efficient implementations of frequently needed operations on lists of numbers, the vector class offers a great deal of programming leverage, especially in the management of network models.
NEURON source code, executables, and documents
are available at http://neuron.duke.edu
and http://www.neuron.yale.edu ,
and by ftp from ftp.neuron.yale.edu .
|We wish to thank John Moore, Zach Mainen, Bill Lytton, and the many other users of NEURON for their encouragement, helpful suggestions, and other contributions. This work was supported by NIH grant NS 11613 ("Computer Methods for Physiological Problems") to MLH and by the Yale Neuroengineering and Neuroscience Center (NNC).|
Address questions and inquiries to
Michael Hines or Ted Carnevale
Digital preprint of "The NEURON Simulation Environment" by M.L. Hines and N.T. Carnevale,
Neural Computation, Volume 9, Number 6 (August 15, 1997), pp. 1179-1209.
Copyright © 1997 by the Massachusetts Institute of Technology, all rights reserved.