The free-energy principle: a rough guide to the brain?

By Karl J. Friston. 2009

1,786 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Neuroscience

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Citations in the corpus

Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review (2018, methodology): “orks have also sought to incorporate probabilistic inference into a model of biological decision making and control (Solway and Botvinick, 2012; Botvinick and An, 2009; Botvinick and Toussaint, 2012; Friston, 2009). The particular frameworks employed in these approaches differ: the formulation propo”
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “r, 2002a; Cooper, 2005), generative adversarial networks (Goodfellow et al., 2014), and various combinations of the above. RBM learning is considered related to the free-energy principle, proposed by Friston (2009) as a central principle governing learning in the brain. Hebbian rules, in particular,”
Attention, Uncertainty, and Free-Energy (2010, methodology): “This section focuses on directed spatial attention predictive coding (Rao and Ballard, 1998) and involves recurrent message passing among hierarchical levels of cortical systems to optimize a probabilistic representation of the world (Mumford, 1992; Friston, 2009).”
Neural Elements for Predictive Coding (2016, background): “This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing…”
Perceptions as Hypotheses: Saccades as Experiments (2012, background): “D a Finally, the fourth assumption is the Laplace assumption that, in terms of neural codes, leads to the Laplace code that is arguably the simplest and most ﬂexible of all neural codes (Friston, 2009).”
Expectation and Surprise Determine Neural Population Responses in the Ventral Visual Stream (2010, background): “…influences, the general question of how predictive coding schemes may relate to attention is the subject of ongoing debate (Rao and Ballard, 2005; Spratling, 2008; Friston, 2009) and, in our view, represents a highly interesting question for future research (Summerfield and Egner, 2009).”
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence (2019, background): “The process is often formalized along Bayesian lines, with current predictions serving as a prior distribution and new data providing a likelihood for dynamic updating (Friston 2009; Friston and Kiebel 2009).”
Repetition Suppression and Expectation Suppression Are Dissociable in Time in Early Auditory Evoked Fields (2012, background): “This temporally ordered neuronal suppression can be explained by hierarchical predictive coding models (Rao and Ballard, 1999; Friston, 2005, 2009; Kiebel et al., 2008; Wacongne et al., 2011).”
Novelty or Surprise? (2013, background): “Itti and Baldi (2005, 2006, 2009) argue that this deﬁnition has key advantages over alternatives in being more principled, more widely applicable, and more able to account for what attracts human visual attention.”
Dopamine, Affordance and Active Inference (2012, neutral): “In terms of neural codes, this is referred to this as the Laplace code and is arguably the simplest and most flexible of all neural codes [66].”
Predictive Coding or Evidence Accumulation? False Inference and Neuronal Fluctuations (2010, background): “This interpretation is not at odds with an account grounded in the free-energy principle, because the increased precision that may be reflected in higher levels of endogenous activity is thought to mediate the effects of directed attention [15].”
Information-Theoretic Bounded Rationality (2015, background): “The variational Bayes framework has recently also been proposed as a theoretical framework to understand brain function [Friston, 2009, 2010] where perception is modeled as variational Bayesian inference over hidden causes of observations.”
The math is not the territory: navigating the free energy principle (2021, background): “…equation and, in particular, the solenoidal and irrotational flows that fall out of the Helmholtz decomposition thereof, of which the irrotational flow can be conceptualised as a gradient-ascent on an attracting set (Friston 2009, 2010, 2012, 2019; Friston and Stephan 2007; Friston et al.”
Affective Value in the Predictive Mind (2017, background): “Therefore, survival “depends upon avoiding surprising encounters and physiological states that are uncharacteristic of a given phenotype” (Friston 2009).”
Uncertainty in perception and the Hierarchical Gaussian Filter (2014, methodology): “…techniques which optimize a free-energy bound on the surprise about sensory inputs, given a model of the environment, and represent a special case of the general “Bayesian brain” hypothesis (Dayan et al., 1995; Knill and Pouget, 2004; Körding and Wolpert, 2006; Friston, 2009; Doya et al., 2011).”