Year: 2018
Type: article
Abstract: 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 models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them requires they be translated into code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. Two main approaches to addressing this issues have been followed. The first is to limit users to a fixed set of optimized models, which limits flexibility. The second is to allow model definitions in a high level interpreted language, although this may limit performance. Recently, a third approach has become increasingly popular: using code generation to automatically translate high level descriptions into efficient low level code to combine the best of previous approaches. This approach also greatly enriches efforts to standardize simulator-independent model description languages. In the past few years, a number of code generation pipelines have been developed in the computational neuroscience community, which differ considerably in aim, scope and functionality. This article provides an overview of existing pipelines currently used within the community and contrasts their capabilities and the technologies and concepts behind them.
Source: Frontiers in Neuroinformatics
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
Institutions: Forschungszentrum Jülich, Jülich Aachen Research Alliance, Centre National de la Recherche Scientifique, Institut de la Vision, Inserm, Sorbonne Université, Cornell University, Monash University, University of Sheffield, Institut des Neurosciences Paris-Saclay, Unit of Neuroscience Information and Complexity, Jülich Supercomputing Centre, University College London, Imperial College London, Yale University, University of Manchester, University of Geneva, École Polytechnique Fédérale de Lausanne, Universidade Federal do ABC, Ruhr University Bochum, Heidelberg University, University of Sussex, RWTH Aachen University, Aix-Marseille Université, Institut de Neurosciences des Systèmes
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,136
Funders: Wellcome Trust, European Commission, Agence Nationale de la Recherche, Fundação de Amparo à Pesquisa do Estado de São Paulo, Bundesministerium für Bildung und Forschung, Board of the Swiss Federal Institutes of Technology, National Institutes of Health, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council, Horizon 2020, National Institute of Neurological Disorders and Stroke
Awards: G1327394386, G1568516718, G2089920575, G2309032329, G2489726015, G2645940334, G3149985778, G3230416436, Development and standardization of biologically realistic neural network models through an open source database., G3416348303, The Open Source Brain repository: enabling the collaborative development of open and accessible models for neuroscience, The Digital Fruit Fly Brain, Human Brain Project Specific Grant Agreement 1, G4175096997, G4285470481, G434337789, Brains on Board: Neuromorphic Control of Flying Robots, G5673166571, Human Brain Project Specific Grant Agreement 2, Accelerating Scientific Discovery with Accelerated Computing, Inactivation of axonal sodium channels: implications for excitability and synaptic transmission, G605763551, Green Brain - Computational Modelling of the Honeybee Brain, G7081818270, D-USA Verbund: Mechanistische Zusammenhänge zwischen Struktur und funktioneller Dynamik im menschlichen Gehirn, G9000092675
OpenAlex ID: W2900311786