VBA: a probabilistic treatment of nonlinear models for neurobiological and behavioural data.
This work is in line with an on-going effort tending toward a computational (quantitative and refutable) understanding of human neuro-cognitive processes. Many sophisticated models for behavioural and neurobiological data have flourished during the past decade. Most of these models are partly unspecified (i.e. they have unknown parameters) and nonlinear. This makes them difficult to peer with a formal statistical data analysis framework. In turn, this compromises the reproducibility of model-based empirical studies. This work exposes a software toolbox that provides generic, efficient and robust probabilistic solutions to the three problems of model-based analysis of empirical data: (i) data simulation, (ii) parameter estimation/model selection, and (iii) experimental design optimization.
Brain and Spine Institute, Paris, France ; Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.
Jean
J
Daunizeau
Gatsby computational neuroscience Unit, University College London, London, United Kingdom.
Vincent
V
Adam
Brain and Spine Institute, Paris, France.
Lionel
L
Rigoux
D000465:Algorithms; D001499:Bayes Theorem; D003071:Cognition; D019295:Computational Biology; D003198:Computer Simulation; D003657:Decision Making; D006801:Humans; D008954:Models, Biological; D008959:Models, Neurological; D009415:Nerve Net; D016011:Normal Distribution; D011336:Probability; D012984:Software; D013269:Stochastic Processes