Dynamic Causal Models and Autopoietic Systems

Dynamic Causal Modelling (DCM) and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems like the brain. DCM has been developed recently by the neuroimaging community...

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Autor principal:	DAVID,OLIVIER
Lenguaje:	English
Publicado:	Sociedad de Biología de Chile 2007
Materias:	Dynamic Causal Modelling brain functional organization plasticity autonomous systems autopoiesis
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500010
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spelling	oai:scielo:S0716-976020070005000102008-05-28Dynamic Causal Models and Autopoietic SystemsDAVID,OLIVIER Dynamic Causal Modelling brain functional organization plasticity autonomous systems autopoiesis Dynamic Causal Modelling (DCM) and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems like the brain. DCM has been developed recently by the neuroimaging community to explain, using biophysical models, the non-invasive brain imaging data are caused by neural processes. It allows one to ask mechanistic questions about the implementation of cerebral processes. In DCM the parameters of biophysical models are estimated from measured data and the evidence for each model is evaluated. This enables one to test different functional hypotheses (i.e., models) for a given data set. Autopoiesis and related formal theories of biological systems as autonomous machines represent a body of concepts with many successful applications. However, autopoiesis has remained largely theoretical and has not penetrated the empiricism of cognitive neuroscience. In this review, we try to show the connections that exist between DCM and autopoiesis. In particular, we propose a simple modification to standard formulations of DCM that includes autonomous processes. The idea is to exploit the machinery of the system identification of DCMs in neuroimaging to test the face validity of the autopoietic theory applied to neural subsystems. We illustrate the theoretical concepts and their implications for interpreting electroencephalographic signals acquired during amygdala stimulation in an epileptic patient. The results suggest that DCM represents a relevant biophysical approach to brain functional organisation, with a potential that is yet to be fully evaluatedinfo:eu-repo/semantics/openAccessSociedad de Biología de ChileBiological Research v.40 n.4 20072007-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500010en10.4067/S0716-97602007000500010
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Dynamic Causal Modelling brain functional organization plasticity autonomous systems autopoiesis
spellingShingle	Dynamic Causal Modelling brain functional organization plasticity autonomous systems autopoiesis DAVID,OLIVIER Dynamic Causal Models and Autopoietic Systems
description	Dynamic Causal Modelling (DCM) and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems like the brain. DCM has been developed recently by the neuroimaging community to explain, using biophysical models, the non-invasive brain imaging data are caused by neural processes. It allows one to ask mechanistic questions about the implementation of cerebral processes. In DCM the parameters of biophysical models are estimated from measured data and the evidence for each model is evaluated. This enables one to test different functional hypotheses (i.e., models) for a given data set. Autopoiesis and related formal theories of biological systems as autonomous machines represent a body of concepts with many successful applications. However, autopoiesis has remained largely theoretical and has not penetrated the empiricism of cognitive neuroscience. In this review, we try to show the connections that exist between DCM and autopoiesis. In particular, we propose a simple modification to standard formulations of DCM that includes autonomous processes. The idea is to exploit the machinery of the system identification of DCMs in neuroimaging to test the face validity of the autopoietic theory applied to neural subsystems. We illustrate the theoretical concepts and their implications for interpreting electroencephalographic signals acquired during amygdala stimulation in an epileptic patient. The results suggest that DCM represents a relevant biophysical approach to brain functional organisation, with a potential that is yet to be fully evaluated
author	DAVID,OLIVIER
author_facet	DAVID,OLIVIER
author_sort	DAVID,OLIVIER
title	Dynamic Causal Models and Autopoietic Systems
title_short	Dynamic Causal Models and Autopoietic Systems
title_full	Dynamic Causal Models and Autopoietic Systems
title_fullStr	Dynamic Causal Models and Autopoietic Systems
title_full_unstemmed	Dynamic Causal Models and Autopoietic Systems
title_sort	dynamic causal models and autopoietic systems
publisher	Sociedad de Biología de Chile
publishDate	2007
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500010
work_keys_str_mv	AT davidolivier dynamiccausalmodelsandautopoieticsystems
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Dynamic Causal Models and Autopoietic Systems

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