The Bayesian brain: the role of uncertainty in neural coding and computation.

By David C. Knill, Alexandre Pouget. Trends in Neurosciences, 2004

2,494 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/2a3146db2f3cb39ef37105d428eef964a253daf2

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Citations in the corpus

When the world becomes 'too real': a Bayesian explanation of autistic perception. (2012, methodology): “Bayesian statistical decision theory, a principled method of optimal reasoning under uncertainty, formalizes Helmoltz’s and Gregory’s notions of perception as unconscious inference [34-37].”
Multistability and Perceptual Inference (2012, background): “ARTICLE Communicated by Nando de Freitas”
Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory (2012, neutral): “A few recent studies have explored the idea that neural circuits instantiate Bayesian inferences (Knill & Pouget, 2004), but this work is only just beginning.”
The sense of agency – a phenomenological consequence of enacting sensorimotor schemes (2017, background): “…proximal and distal sensory consequences of an action, and which can serve in comparisons with either actual sensory feedback or the desired state of the agent (for predictive in-the-head models of cognition in general also see Wolpert et al. 2003; Knill and Pouget 2004; Grush 2004; or Seth 2014).”
Adolescent risk-taking in the context of exploration and social influence (2021, background): “Evidence suggests that learning to interact with novel environments can be characterized by some form of Bayesian learning (Daw et al., 2005, 2006; Dayan & Daw, 2008; Knill & Pouget, 2004; Marković & Kiebel, 2016; Mathys et al., 2011; Nassar et al., 2010).”
A framework for Bayesian optimality of psychophysical laws (2012, background): “…in the nervous system is algorithmically well-matched to the statistical properties of the natural environment (Olshausen & Field, 2004) and that therefore operation of the brain is probabilistically efficient, i.e. Bayes-optimal (Friston, 2010; Jacobs & Kruschke, 2011; Knill & Pouget, 2004).”
Stochastic variational learning in recurrent spiking networks (2014, background): “On the level of behavioral neuroscience, human performance approaches in many psychophysical learning paradigms Bayes optimality, i.e., the best statistical model cannot perform better than humans do (Knill and Pouget, 2004; Körding and Wolpert, 2004).”
Emerging concepts for the dynamical organization of resting-state activity in the brain (2010, neutral): “It seems reasonable to link this behaviour to the notion from Bayes’ models of the brain 69,70 : by being in a constant state of exploration the brain generates predictions about likely future network configurations that will be appropriate for novel contexts.”
Serotonin and brain function: a tale of two receptors (2017, background): “Viewed through the lens of the popular Bayesian brain model of brain function (Knill and Pouget, 2004), one could see this 5-HT2ARmediated entropic state as working to ‘reset’ reinforced priors in depression – such as pessimistic beliefs and negative self-perceptions (Moutoussis et al.”
Bayesian Inference Explains Perception of Unity and Ventriloquism Aftereffect: Identification of Common Sources of Audiovisual Stimuli (2007, methodology): “Bayesian models are shown to be one of the most powerful methods for understanding human psychophysical experiments on multisensory integration (Deneve & Pouget, 2004; Knill & Pouget, 2004; K ¨ ording & Wolpert, 2004).”
Sound-induced flash illusion as an optimal percept (2005, background): “Conclusion The findings of this study suggest that the brain uses a mechanism similar to Bayesian inference [19] to decide whether, to what degree, and how (in which direction) to integrate the signals from auditory and visual modalities, and that the sound-induced flash illusion can be viewed as an”
Using Free Energy Principle For Blind Image Quality Assessment (2015, neutral): “Similar to the Bayesian brain hypothesis [45], a basic premise of the free energy based brain theory is that the cognitive process is manipulated by an internal generative model.”