Bayesian brain : probabilistic approaches to neural coding

By 銅谷賢治, 石井信, Alexandre Pouget, Rajesh P. N. Rao. 2006

803 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5c2dd14c18397c27d8d370194bf905c37ed1c42a

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In Cognitive Neuroscience

Citations in the corpus

The Human Kernel (2015, background): “In recent years, probabilistic modelling has become a cornerstone of machine learning approaches [2, 3, 4], with applications in neural processing [5, 6, 3, 7] and human learning [8, 9].”
Goal-directed decision making in prefrontal cortex: a computational framework (2008, background): “The interest of this idea, in the present context, derives from a recent movement toward framing human and animal information processing, as well as the underlying neural computations, in terms of structured probabilistic inference [13, 14].”
Bayesian models of cognition (2008, background): “Related ideas are providing new paradigms for the study of neural coding and computation (Doya, Ishii, Pouget, & Rao, 2007), children’s cognitive development (Gopnik & Tenenbaum, in press), machine learning (Bishop, 2006) and artiﬁcial intelligence (Russell & Norvig, 2002).”
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).”
Value and reward based learning in neurorobots (2013, background): “This section considers discrete time (Markov) decision processes of the sort found in optimal control theory, models of behavior and decision making (Bellman, 1952; Watkins and Dayan, 1992; Camerer, 2003; Daw and Doya, 2006; Todorov, 2006; Dayan and Daw, 2008).”
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines (2015, methodology): “The development of methods for approximate Bayesian updating via distributed message passing over large networks of variables suggests that similar procedures might be used for large-scale probabilistic inference in the brain (29).”
Speed, Accuracy, and the Optimal Timing of Choices (2018, methodology): “(For recent reviews see Ratcliff and McKoon (2008) and Shadlen et al. (2006).)”