Code Generation in Computational Neuroscience: A Review of Tools and Techniques

Code Generation in Computational Neuroscience: A Review of Tools and Techniques

Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell m...

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Bibliographic Details
Main Authors: Inga Blundell, Romain Brette, Thomas A. Cleland, Thomas G. Close, Daniel Coca, Andrew P. Davison, Sandra Diaz-Pier, Carlos Fernandez Musoles, Padraig Gleeson, Dan F. M. Goodman, Michael Hines, Michael W. Hopkins, Pramod Kumbhar, David R. Lester, Bóris Marin, Abigail Morrison, Eric Müller, Thomas Nowotny, Alexander Peyser, Dimitri Plotnikov, Paul Richmond, Andrew Rowley, Bernhard Rumpe, Marcel Stimberg, Alan B. Stokes, Adam Tomkins, Guido Trensch, Marmaduke Woodman, Jochen Martin Eppler
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-11-01
Series:Frontiers in Neuroinformatics
Subjects:
code generation
simulation
neuronal networks
domain specific language
modeling language
Online Access:https://www.frontiersin.org/article/10.3389/fninf.2018.00068/full

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https://www.frontiersin.org/article/10.3389/fninf.2018.00068/full

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