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Structure learning in coupled dynamical systems and dynamic causal modelling

Jafarian, A; Zeidman, P; Litvak, V; Friston, K; (2019) Structure learning in coupled dynamical systems and dynamic causal modelling. Philosophical Transactions A: Mathematical, Physical and Engineering Sciences , 377 (2160) , Article 20190048. 10.1098/rsta.2019.0048. Green open access

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Abstract

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill-posed problem that commonly arises when modelling real-world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of network architectures—and implicit coupling functions—in terms of their Bayesian model evidence. These methods are collectively referred to as dynamic causal modelling. We focus on a relatively new approach that is proving remarkably useful, namely Bayesian model reduction, which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems.

Type: Article
Title: Structure learning in coupled dynamical systems and dynamic causal modelling
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1098/rsta.2019.0048
Publisher version: https://doi.org/10.1098/rsta.2019.0048
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Dynamic causal modelling, Bayesian model selection, Bayesian model reduction
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10085947
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