Computational Modeling of Neural Activities for Statistical Inference
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 ar
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Computational Modeling of Neural Activities for Statistical Inference
Computational Modeling of Neural Activities for Statistical Inference
Antonio Kolossa
Computational Modeling of Neural Activities for Statistical Inference
123
Antonio Kolossa Institut für Nachrichtentechnik Technische Universität Braunschweig Braunschweig Germany
ISBN 978-3-319-32284-1 DOI 10.1007/978-3-319-32285-8
ISBN 978-3-319-32285-8
(eBook)
Library of Congress Control Number: 2016937953 © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
Foreword
Thomas Bayes (around 1701–1761) took a deep interest in probability theory. His seminal essay on inverse probabilities was published in the Philosophical Transactions of the Royal Society of London in 1763, 2 years after Bayes had passed away. What we know today as Bayes’ theorem has become fundamental to many scientific disciplines such as engineering, natural sciences, neurosciences, cognitive sciences, statistics, and beyond. The field of decision and estimation theory is totally centered around Bayes’ theorem today. The background of this book is the encounter of two contemporary followers of Bayes: Prof. Dr. rer. soc. Bruno Kopp, affiliated to the Medizinische Hochschule Hannover, Hannover, Germany, and Prof. Dr.-Ing. Tim Fingscheidt, Technische Universität Braunschweig, Braunschweig, Germany. While Kopp’s research focuses on understanding the principles of predictive learning in mind and brain, Fingscheidt’s research interests cover signal processing and machine learning, mostly with applications in speech. They both believed that event-related potentials (ERPs)—i.e., scalp-recorded real-time proxies of cortical activities—should be predictable by computational methods, and that the pursuit of this modeling effort could create an encompassing
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