DEM: A variational treatment of dynamic systems

By Karl J. Friston, Nelson J. Trujillo-Barreto, Jean Daunizeau. NeuroImage, 2008

312 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c09fb7cfa28cde7ab254f878a5987758b8c5f4d0

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Citations in the corpus

Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering (2011, methodology): “Second, we provide a comprehensive evaluation of our proposed scheme and compare 177 it with DEM.”
Action and behavior: a free-energy formulation (2010, unknown): “2006; Friston 2008) and technical (Friston et al. 2008) literature.”
The free energy principle for action and perception: A mathematical review (2017, methodology): “̇̃ μα , in the generalised state-space is distinct from the ‘trajectory’ encoded in the brain (flow velocity) (Friston, 2008a, b; Friston et al., 2008).”
Construct validation of a DCM for resting state fMRI (2015, background): “Put simply, whilst DEM or GF estimates time-dependent ﬂ uctuations in neuronal states producing observed fMRI data, spDCMsimplyestimates the time-invariant parameters of their cross spectra.”
Hierarchical Models in the Brain (2008, methodology): “This treatment provides a heuristic summary of the material in [2].”
Free-energy and the brain (2007, background): “It is fairly simple to show (Friston, 2007) that the optimisation in Eq.”
Reinforcement Learning or Active Inference? (2009, background): “Although generalised coordinates may appear to complicate matters, they actually simplify inference greatly; see [13] and [39] for details.”
A Hierarchy of Time-Scales and the Brain (2008, unknown): “However, the details and conceptual background of the approach can be found in [36].”
Effective connectivity: Influence, causality and biophysical modeling (2011, unknown): “Examples of inverting nonlinear continuous time neural models using this procedure are described in Valdes-Sosa et al. (1999), Riera et al. (2007b), Friston and Daunizeau (2008), Marreiros et al. (2009), Stephan et al. (2008), and Daunizeau et al. (2009b).”
The free energy principle made simpler but not too simple (2022, unknown): “…filtering is a generic Bayesian filtering scheme for nonlinear state–space models formulated in generalised coordinates of motion [36]; special cases include variational filtering [90], dynamic expectation maximisation [91], extended Kalman filtering [92], and generalised predictive coding.”
Functional and Effective Connectivity: A Review (2011, unknown): “Recent advances in causal modeling based on random differential equations (Friston et al., 2008) can now accommodate hidden ﬂuctuations in neuronal states that enable the modeling of autonomous or endogenous brain dynamics.”