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Definition
NEURON is a simulation environment that is primarily used for creating and using computational models of individual neurons and networks of neurons.
Detailed Description
This entry surveys key aspects of the NEURON simulation environment (NEURON hereafter). For more detailed coverage of these and related topics, please see The NEURON Book (Carnevale and Hines 2006), documentation at NEURON’s web site neuron.yale.edu, or our chapter on numerical integration (Carnevale and Hines 2014).
Domain of Utility
NEURON is a “domain-specific” simulator in the sense that, by design, it has features that make it particularly well-suited for constructing and simulating certain kinds of models. It emerged from a collaboration between John Moore and Michael Hines at Duke University. Their initial aim was to develop software for models used to complement experimental research on spike initiation and conduction in squid axon (Hines 1992, 1994). NEURON’s domain of utility...
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References
Carnevale N, Hines M (2006) The NEURON book. Cambridge University Press, Cambridge, UK
Carnevale N, Hines M (2014) Numerical integration in computational neuroscience. In: Jaeger D, Jung R (eds) Encyclopedia of computational neuroscience. Springer, Berlin
Carnevale N, Tsai K, Claiborne B, Brown T (1995) The electrotonic transformation: a tool for relating neuronal form to function. In: Tesauro G, Touretzky DS, Leen TK (eds) Advances in neural information processing systems. MIT Press, Cambridge, MA, pp 69–76
Carnevale N, Tsai K, Claiborne B, Brown T (1997) Comparative electrotonic analysis of three classes of rat hippocampal neurons. J Neurophysiol 78:703–720
CoreNEURON GitHub Repository (2019) CoreNEURON – simulator optimized for large scale neural network simulations. https://github.com/BlueBrain/CoreNeuron. Accessed 27 Aug 2019
Courtemanche M, Ramirez RJ, Nattel S (1998) Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. Am J Phys 275:H301–H321
Crane G, Hines M, Neild T (2001) Simulating the spread of membrane potential changes in arteriolar networks. Microcirculation 8:33–43
Hines M (1992) NEURON – a program for simulation of nerve equations. In: Eeckman F (ed) Neural systems: analysis and modeling. Kluwer, Norwell, pp 127–136
Hines M (1994) The NEURON simulation program. In: Skrzypek J (ed) Neural network simulation environments. Kluwer, Norwell, pp 147–163
Hines M, Carnevale N (2001) NEURON: a tool for neuroscientists. Neuroscientist 7:123–135
Hines M, Carnevale N (2005) Recent developments in NEURON. Brains, Minds and Media 1:bmm221, urn:nbn:de:0009-3-2210
Hines M, Carnevale N (2008) Translating network models to parallel hardware in NEURON. J Neurosci Methods 169:425–455
Hines M, Eichner H, Schürmann F (2008a) Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors. J Comput Neurosci 25:203–210
Hines M, Markram H, Schürmann F (2008b) Fully implicit parallel simulation of single neurons. J Comput Neurosci 25:439–448
Kernighan B, Pike R (1984) A2: Hoc manual. In: The UNIX programming environment. Prentice-Hall, Englewood Cliffs, pp 329–333
Kumbhar P, Hines M, Fouriaux J, Ovcharenko A, King J, Delalondre F et al (2019) CoreNEURON: an optimized compute engine for the NEURON simulator arXiv:1901.10975
Lytton W, Omurtag A (2007) Tonic-clonic transitions in computer simulation. J Clin Neurophysiol 24:175–181
McDougal R, Hines M, Lytton W (2013) Reaction-diffusion in the NEURON simulator. Front Neuroinform 7:28. https://doi.org/10.3389/fninf.2013.00028
Migliore M, Cannia C, Lytton W, Markram H, Hines M (2006) Parallel network simulations with NEURON. J Comput Neurosci 21:119–129
Newton AJ, McDougal RA, Hines ML, Lytton WW (2018) Using NEURON for reaction-diffusion Modeling of extracellular dynamics. Front Neuroinform 12:41. https://doi.org/10.3389/fninf.2018.00041
O'Boyle M, Carnevale N, Claiborne B, Brown T (1996) A new graphical approach for visualizing the relationship between anatomical and electrotonic structure. In: Bower JM (ed) Computational neuroscience: trends in research 1995. Academic, San Diego, pp 423–428
Yu Y, McTavish T, Hines M, Shepherd G, Valenti C, Migliore M (2013) Sparse distributed representation of odors in a large-scale olfactory bulb circuit. PLoS Comput Biol 9:e1003014. https://doi.org/10.1371/journal.pcbi.1003014
Acknowledgments
This work was supported by NIH award R01NS011613. The growing list of individuals who have made significant contributions to NEURON’s development is posted at www.neuron.yale.edu/neuron/credits
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Hines, M., Carnevale, T., McDougal, R.A. (2022). NEURON Simulation Environment. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-1006-0_795
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