Titlebook: Computational Modeling of Neural Activities for Statistical Inference; Antonio Kolossa Book 2016 Springer International Publishing Switzer

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978-3-319-81243-4Springer International Publishing Switzerland 2016

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Book 2016encephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model wh

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over observable events and hidden states.Helps the reader t.This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer mod

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Zuordnung der Patienten zu den Varianten,cept of circularity in data analyses. Then it is shown how evidence for the coding of probability distributions in the brain can be obtained, using a framework that relates random variables to neural activities. Last, an overview on probability weighting by humans is given, the role of which in probabilistic reasoning is investigated in this work.

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Bayesian Inference and the Urn-Ball Task,s in the environment and predictions about observable events. The scope of the analyzed data is extended to the complete late positive complex (P3a, P3b, Slow Wave) and the N250. It starts with a brief overview on the Bayesian observer model and the urn-ball task and their relation to the Bayesian brain hypothesis.

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Book 2016computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field.. .

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Was wir nicht wissen: Offene Fragen,uting the model space . are presented in detail and the two most renowned ones are integrated into the digital filtering model. Next, the parameter optimization schemes as well as the composition of the design matrices for model estimation and selection (see Chap. .) are specified. Results and conclusions complete this chapter.