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PLoS Comput Biol. 2014 Jan;10(1):e1003441. doi: 10.1371/journal.pcbi.1003441. Epub 2014 Jan 23.

VBA: a probabilistic treatment of nonlinear models for neurobiological and behavioural data.

Author information

1
Brain and Spine Institute, Paris, France ; Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.
2
Gatsby computational neuroscience Unit, University College London, London, United Kingdom.
3
Brain and Spine Institute, Paris, France.

Abstract

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.

PMID:
24465198
PMCID:
PMC3900378
DOI:
10.1371/journal.pcbi.1003441
[Indexed for MEDLINE]
Free PMC Article
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