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The Brian Simulator

Overview
Journal Front Neurosci
Date 2009 Dec 17
PMID 20011141
Citations 160
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Abstract

"Brian" is a simulator for spiking neural networks (http://www.briansimulator.org). The focus is on making the writing of simulation code as quick and easy as possible for the user, and on flexibility: new and non-standard models are no more difficult to define than standard ones. This allows scientists to spend more time on the details of their models, and less on their implementation. Neuron models are defined by writing differential equations in standard mathematical notation, facilitating scientific communication. Brian is written in the Python programming language, and uses vector-based computation to allow for efficient simulations. It is particularly useful for neuroscientific modelling at the systems level, and for teaching computational neuroscience.

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References
1.
JEFFRESS L . A place theory of sound localization. J Comp Physiol Psychol. 1948; 41(1):35-9. DOI: 10.1037/h0061495. View

2.
Sturzl W, Kempter R, van Hemmen J . Theory of arachnid prey localization. Phys Rev Lett. 2000; 84(24):5668-71. DOI: 10.1103/PhysRevLett.84.5668. View

3.
Diesmann M, Gewaltig M, Aertsen A . Stable propagation of synchronous spiking in cortical neural networks. Nature. 1999; 402(6761):529-33. DOI: 10.1038/990101. View

4.
Davison A, Bruderle D, Eppler J, Kremkow J, Muller E, Pecevski D . PyNN: A Common Interface for Neuronal Network Simulators. Front Neuroinform. 2009; 2:11. PMC: 2634533. DOI: 10.3389/neuro.11.011.2008. View

5.
Hines M, Davison A, Muller E . NEURON and Python. Front Neuroinform. 2009; 3:1. PMC: 2636686. DOI: 10.3389/neuro.11.001.2009. View