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＜電子ブック＞
Computational Modeling of Neural Activities for Statistical Inference / by Antonio Kolossa

版	1st ed. 2016.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2016
本文言語	英語
大きさ	XXIV, 127 p. 42 illus., 20 illus. in color : online resource
著者標目	*Kolossa, Antonio author SpringerLink (Online service)
件　名	LCSH:Neural networks (Computer science)  LCSH:Biomedical engineering LCSH:Neurosciences LCSH:Biomathematics LCSH:Computer simulation FREE:Mathematical Models of Cognitive Processes and Neural Networks FREE:Biomedical Engineering and Bioengineering FREE:Neuroscience FREE:Mathematical and Computational Biology FREE:Computer Modelling
一般注記	Basic Principles of ERP Research, Surprise, and Probability Estimation -- Introduction to Model Estimation and Selection Methods -- A New Theory of Trial-by-Trial P300 Amplitude Fluctuations -- Bayesian Inference and the Urn-Ball Task -- Summary and Outlook 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 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 which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing 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. HTTP:URL=https://doi.org/10.1007/978-3-319-32285-8

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