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Brian 2, an intuitive and efficient neural simulator

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Title: Brian 2, an intuitive and efficient neural simulator
Authors: Stimberg, M
Brette, R
Goodman, DFM
Item Type: Journal Article
Abstract: Brian 2 allows scientists to simply and efficiently simulate spiking neural network models. These models can feature novel dynamical equations, their interactions with the environment, and experimental protocols. To preserve high performance when defining new models, most simulators offer two options: low-level programming or description languages. The first option requires expertise, is prone to errors, and is problematic for reproducibility. The second option cannot describe all aspects of a computational experiment, such as the potentially complex logic of a stimulation protocol. Brian addresses these issues using runtime code generation. Scientists write code with simple and concise high-level descriptions, and Brian transforms them into efficient low-level code that can run interleaved with their code. We illustrate this with several challenging examples: a plastic model of the pyloric network, a closed-loop sensorimotor model, a programmatic exploration of a neuron model, and an auditory model with real-time input.</jats:p>
Issue Date: 20-Aug-2019
Date of Acceptance: 20-Aug-2019
URI: http://hdl.handle.net/10044/1/72805
DOI: https://dx.doi.org/10.7554/elife.47314
ISSN: 2050-084X
Publisher: eLife Sciences Publications Ltd
Journal / Book Title: eLife
Volume: 8
Copyright Statement: © 2019 eLife Sciences Publications Ltd. Subject to a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/), except where otherwise noted.
Publication Status: Published online
Online Publication Date: 2019-08-20
Appears in Collections:Electrical and Electronic Engineering