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

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

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Bayes Theorem
  • Cognition
  • Computational Biology
  • Computer Simulation*
  • Decision Making
  • Humans
  • Models, Biological
  • Models, Neurological
  • Nerve Net
  • Normal Distribution
  • Probability*
  • Software
  • Stochastic Processes

Grant support

This work was supported by the European Research Council (JD) and by the Ville de Paris (LR). In addition, authors acknowledge support from the IHU-A-IM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.