A free energy principle for the brain.

By Karl J. Friston, James M. Kilner, Lee M. Harrison. Journal of Physiology - Paris, 2006

1,320 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Plant Science

View Paper: https://www.semanticscholar.org/paper/641a9d87b9636d4ef2e353a569ddede68bd29131

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Citations in the corpus

DEM: A variational treatment of dynamic systems (2008, methodology): “be used by neurobiological implementations of this scheme (i.e., encoded explicitly in the brain; see Friston 2005; Friston et al., 2006).”
The free energy principle for action and perception: A mathematical review (2017, background): “First the central theory (Friston et al., 2006) which offers a particular explanation of cognition in terms of Bayesian inference.”
A free energy principle for a particular physics (2019, background): “This is often cast in terms of a perception-action cycle (Friston et al., 2006; Fuster, 2004).”
Model-Based Reasoning in Humans Becomes Automatic with Training (2015, result): “It is also consistent with the notion of "models" throughout processing hierarchies in the brain, from low-level sensory processing to high-level cognition [45,46].”
An Interoceptive Predictive Coding Model of Conscious Presence (2011, methodology): “…this minimization are based on so-called “variational Bayes” worked out by (Neal and Hinton, 1998) among others; these algorithms have plausible neurobiological implementations, at least in cortical hierarchies (Hinton and Dayan, 1996; Lee and Mumford, 2003; Friston et al., 2006; Friston, 2009).”
Using Free Energy Principle For Blind Image Quality Assessment (2015, methodology): “In addition, some recent advances in brain science and neuroscience [29], [30] motivated us to design the no-reference free energy based quality metric (NFEQM) [17] for predicting the quality of both blurry and noisy images.”
The graphical brain: Belief propagation and active inference (2017, background): “In particular, we assume that the brain engages in some form of (Bayesian) inference—and can therefore be described as maximizing Bayesian model evidence (Clark, 2013; Friston, Kilner, & Harrison, 2006; Hohwy, 2016; Mumford, 1992).”
Information Theory of Decisions and Actions (2011, background): “This can be generalized to consider directly the desired distribution of outcomes of the agent’s action (Friston et al. 2006; Friston 2009).”
Active interoceptive inference and the emotional brain (2016, background): “13 (20), free-energy accounts of perception and behaviour (4) and hierarchical Bayesian models of learning (78).”
Action and behavior: a free-energy formulation (2010, background): “The idea that the brain uses hierarchical inference has been established for years (Mumford 1992; Rao and Ballard 1998; Friston 2005; Friston et al. 2006) and provides a nice explanation for the hierarchical organization of cortical systems.”
The anatomy of choice: active inference and agency (2013, background): “This is important, because it connects utility-based schemes to variational Bayes and, more generally, inferential schemes that may underwrite biological self-organization (Ashby, 1947; Friston, 2012).”
The free energy principle made simpler but not too simple (2022, neutral): “Gradient flows that minimise surprisal (i.e., self-information) lead to a series of influential accounts of neuronal dynamics; including the principle of maximum mutual information [67,68], the principles of minimum redundancy and maximum efficiency [69] and the free energy principle [70].”
Reinforcement Learning or Active Inference? (2009, background): “Under this specification, optimum behaviour emerges in agents that conform to a free-energy principle, which provides a principled basis for understanding both action and perception [9,10].”