1. Collaborative modelling: The future of computational neuroscience?
- Author
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Andrew P. Davison, Unité de Neurosciences Information et Complexité [Gif sur Yvette] (UNIC), Centre National de la Recherche Scientifique (CNRS), and Institut de Neurobiologie Alfred Fessard (INAF)
- Subjects
Computer science ,Models, Neurological ,Neuroscience (miscellaneous) ,MESH: Information Dissemination ,MESH: Software ,03 medical and health sciences ,0302 clinical medicine ,MESH: Computer Simulation ,MESH: Models, Neurological ,Component (UML) ,MESH: Cooperative Behavior ,Biological neural network ,Animals ,Humans ,Computer Simulation ,MESH: Animals ,Cooperative Behavior ,030304 developmental biology ,Network model ,0303 health sciences ,MESH: Humans ,Computational neuroscience ,Quantitative Biology::Neurons and Cognition ,Information Dissemination ,business.industry ,Systems Biology ,Neurosciences ,MESH: Neurosciences ,Data science ,MESH: Nerve Net ,MESH: Systems Biology ,MESH: Programming Languages ,Programming Languages ,[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] ,Artificial intelligence ,Cooperative behavior ,Nerve Net ,business ,Software ,030217 neurology & neurosurgery - Abstract
International audience; Given the complexity of biological neural circuits and of their component cells and synapses, building and simulating robust, well-validated, detailed models increasingly surpasses the resources of an individual researcher or small research group. In this article, I will briefly review possible solutions to this problem, argue for open, collaborative modelling as the optimal solution for advancing neuroscience knowledge, and identify potential bottlenecks and possible solutions.
- Published
- 2012