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Dynamic causal

Dynamic causal models and autopoietic systems.

Dynamic causal models and autopoietic systems.

... * Correspondence should be adressed to: Olivier David <odavid@ujf-grenoble.fr> Abstract Dynamic Causal Modelling (DCM) and the theory of autopoietic systems are two important conceptual frameworks. In ...

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Dynamic causal modeling of evoked responses in EEG and MEG.

Dynamic causal modeling of evoked responses in EEG and MEG.

... of dynamic causal modeling (Friston et ...nonlinear causal architecture. The alternative to causal modeling is to simply establish statistical dependencies between activity in one brain region ...

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Dynamic causal models of neural system dynamics:current state and future extensions.

Dynamic causal models of neural system dynamics:current state and future extensions.

... field, causal mechanisms in neural systems are described in terms of effective ...Recently, Dynamic Causal Modelling (DCM) was introduced as a generic method to estimate effective connectivity from ...

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Clustering in Dynamic Causal Networks as a Measure of Systemic Risk on the Euro Zone

Clustering in Dynamic Causal Networks as a Measure of Systemic Risk on the Euro Zone

... Please insert here Figures (3) and (4) The network representations corresponding to heatmaps in Figure (4) are illustrated by Figures (5), (6) and (7). Corresponding network representations are provided for the six ...

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A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures

A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures

... spectral Dynamic Causal Modelling is a conventional technique for which we propose an alternative to estimate cross spectral ...conventional Dynamic Causal Modelling and the one wherein a ...

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Comparison of two integration methods for dynamic causal modeling of electrophysiological data

Comparison of two integration methods for dynamic causal modeling of electrophysiological data

... A B S T R A C T Dynamic causal modeling (DCM) is a methodological approach to study effective connectivity among brain re- gions. Based on a set of observations and a biophysical model of brain ...

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Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization.

Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization.

... Table 3: Second simulation: deviations of dipole orientations of contralateral SI/SII and ipsilateral SII from true orientations (angles around axes in degrees). IV D ISCUSSION In this paper, we have presented dynamical ...

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Dynamic causal modeling of subcortical connectivity of language.: DCM of a language network

Dynamic causal modeling of subcortical connectivity of language.: DCM of a language network

... Note, that the three winning families together (all including the deep source) have 95% exceedance probability. c) Exceedance probabilities for each model separately. Note [r] ...

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Dynamic causal modelling: a critical review of the biophysical and statistical foundations.

Dynamic causal modelling: a critical review of the biophysical and statistical foundations.

... integration, which is mediated by context-dependent interactions among spatially segregated areas (see, e.g., McIntosh 2000 ). In this view, the functional role played by any brain component (e.g. cortical area, ...

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A Probabilistic Causal Message Ordering Mechanism

A Probabilistic Causal Message Ordering Mechanism

... 5.4.1 Detecting delivery errors One of the challenges to evaluate the proposed ap- proach is to measure the error rate. When a message is said to be “causally ready” by our mechanism, we need to verify that it is really ...

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A Probabilistic Causal Message Ordering Mechanism

A Probabilistic Causal Message Ordering Mechanism

... first causal broadcast mechanism was introduced in the ISIS system ...ment causal communication consists in piggybacking on each messsage a process want to send the whole set of messages it has delivered ...

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Responsabilité morale et déterminisme : Une approche par le contrôle causal

Responsabilité morale et déterminisme : Une approche par le contrôle causal

... déterminisme causal ; la possibilité qu’un mécanisme sensible aux raisons puisse produire des actions différentes selon les raisons permet de conclure que l’auteur de l’action (via le mécanisme sensible à ses ...

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Ontology-based inference for causal explanation

Ontology-based inference for causal explanation

... of causal and taxonomical information, β is ...on causal statements and stress that the rˆole of ontology-based information is ...Our causal information is restricted to the cases where the causation ...

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Causal inference : a Tensor's perspective

Causal inference : a Tensor's perspective

... Sec. 1.4. Summary of Results 33 Empirical Studies: Toward Personalized, Data-Efficient Treatments Given that SI can estimate potential outcomes under treatment (as well as control) across all units, SI can effectively ...

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D.1.2 – Modular quasi-causal data structures

D.1.2 – Modular quasi-causal data structures

... new-york is here free of concurrent write w.r.t. paris and berlin, making causal consistency equiv- alent to sequential consistency for new-york. paris and berlin however write to X concurrently, in which case ...

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Causal Inference and Impact Evaluation

Causal Inference and Impact Evaluation

... for compliers is identical to the average effect for always‑takers and never‑takers. Bertanha & Imbens (2019) suggest testing the combina‑ tion of two equalities, namely the equality of the average outcomes of ...

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The causal and the moral

The causal and the moral

... However, the principle that I defended in this chapter--the principle that causation is required for the transmission of responsibility to outcomes-can explain equally well [r] ...

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Espèces naturelles, profil causal et constitution multiple

Espèces naturelles, profil causal et constitution multiple

... J’ai suggéré que les espèces naturelles sont postulées en sciences comme étant ce qui est sous-jacent à un ensemble de dispositions systématiquement associées. N’est-ce pas préci- sément la composition microstructurelle ...

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Causal structure discovery from incomplete data

Causal structure discovery from incomplete data

... Indeed, as the number of off-target effects increases, we see that in both interven- tional settings and both structure-learning metrics, UT-IGSP outperforms all other methods. However, JCI-GSP and UT-IGSP are roughly ...

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Ensuring referential integrity under causal consistency

Ensuring referential integrity under causal consistency

... only causal consistency: (1) before an outref is assigned to a source object (in initialisation or assignment), we ensure that the corresponding inref has been added to the target object; importantly, ...

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