Overview
Provides empirical evidence for the Bayesian brain hypothesis
Presents observer models which are useful to compute probability distributions over observable events and hidden states
Helps the reader to better understand the neural coding by means of Bayesian rules
Includes supplementary material: sn.pub/extras
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Table of contents (5 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Computational Modeling of Neural Activities for Statistical Inference
Authors: Antonio Kolossa
DOI: https://doi.org/10.1007/978-3-319-32285-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-32284-1Published: 23 May 2016
Softcover ISBN: 978-3-319-81243-4Published: 27 May 2018
eBook ISBN: 978-3-319-32285-8Published: 12 May 2016
Edition Number: 1
Number of Pages: XXIV, 127
Number of Illustrations: 22 b/w illustrations, 20 illustrations in colour
Topics: Mathematical Models of Cognitive Processes and Neural Networks, Biomedical Engineering and Bioengineering, Neurosciences, Physiological, Cellular and Medical Topics, Simulation and Modeling