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Cortical knowledge structures guide word concept learning. [PDF]
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Bayesian plan recognition for Brain-Computer Interfaces
2009 IEEE International Conference on Robotics and Automation, 2009For people with very severe motor dysfunctions, Brain-Computer Interfaces (BCIs) may provide the solution to regain mobility and manipulation capabilities. Unfortunately, BCIs are characterized by a limited bandwidth and uncertainty on the BCI output.
Eric Demeester +3 more
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Hierarchical Bayesian Inference of Brain Activity
2008Magnetoencephalography (MEG) can measure brain activity with millisecond-order temporal resolution, but its spatial resolution is poor, due to the ill-posed nature of the inverse problem, for estimating source currents from the electromagnetic measurement. Therefore, prior information on the source currents is essential to solve the inverse problem.
Masa-aki Sato, Taku Yoshioka
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2006
Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control. A Bayesian approach can contribute to an understanding of the brain on multiple levels, by giving normative predictions about how an ideal sensory system should combine prior knowledge ...
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Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control. A Bayesian approach can contribute to an understanding of the brain on multiple levels, by giving normative predictions about how an ideal sensory system should combine prior knowledge ...
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Bayesian brains and cognitive mechanisms:
2008AbstractThe chapter considers the project of probabilistic rational analysis in relation to a particularly well-studied and simple heuristic, the Take the Best algorithm. The authors relate the tension between ‘rational’ and ‘algorithmic’ explanations of cognitive phenomena to the bias-variance dilemma in statistics.
Henry Brighton, Gerd Gigerenzer
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Bayes in the Brain—On Bayesian Modelling in Neuroscience
The British Journal for the Philosophy of Science, 2012According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of
Colombo, M., Seriès, P.
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Computational Psychiatry and the Bayesian Brain
2017Abstract This chapter considers recent advances in computational neuroscience that are especially relevant for psychiatry. We offer a review of computational psychiatry in terms of its ambitions, emerging domains of application, and promises for the future.
Karl J. Friston, Raymond J. Dolan
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Sparse coding and challenges for Bayesian models of the brain
Behavioral and Brain Sciences, 2013AbstractWhile the target article provides a glowing account for the excitement in the field, we stress that hierarchical predictive learning in the brain requires sparseness of the representation. We also question the relation between Bayesian cognitive processes and hierarchical generative models as discussed by the target article.
Thomas, Trappenberg, Paul, Hollensen
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