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Neural Assemblies

An Alternative Approach to Classical Artificial Intelligence

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  • © 2022
  • Latest edition

Overview

  • Puts ideas of the 1st edition into the framework of modern computational and cognitive neuroscience
  • Highlights key aspects in the development of comp. neuroscience and artificial neural networks of the last 40 years
  • Does not require in depth knowledge of mathematics, computer science, psychology, or neuroscience

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About this book

In the new edition of Neural Assemblies, the author places his original ideas and motivations within the framework of modern and cognitive neuroscience and gives a short and focused overview of the development of computational neuroscience and artificial neural networks over the last 40 years. 

In this book the author develops a theory of how the human brain might function. Starting with a motivational introduction to the brain as an organ of information processing, he presents a computational perspective on the basic concepts and ideas of neuroscience research on the underlying principles of brain function. In addition, the reader is introduced to the most important methods from computer science and mathematical modeling that are required for a computational understanding of information processing in the brain.

Written by an expert in the field of neural information processing, this book offers a personal historical view of the development of artificial intelligence, artificial neural networks, and computational cognitive neuroscience over the last 40 years, with a focus on the realization of higher cognitive functions rather than more peripheral sensory or motor organization. The book is therefore aimed at students and researchers who want to understand how the basic neuroscientific and computational concepts in the study of brain function have changed over the last decades.


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Table of contents (11 chapters)

  1. Basic Facts and Ideas for a Brain

  2. Further Developments Until Today

Authors and Affiliations

  • Ulm University, Ulm, Germany

    Günther Palm

About the author

Prof Dr Günther Palm

Professor Palm began his studies of mathematics at the University of Hamburg and graduated at the Eberhard-Karls-University of Tübingen with a PhD thesis on “Entropie und Generatoren in dynamischen Verbänden“ supervised by Prof. Dr. Rainer Nagel in 1975. He then worked as a research assistant at the Max Planck Institute for Biological Cybernetics, Tübingen, on topics of quantitative neuroanatomy, information theory, nonlinear systems theory, associative memory and brain theory from 1975 to 1988. During that time he spent one year (1983/1984) in Berlin as a fellow of the Wissenschaftskolleg. In 1988 he became professor for theoretical brain research at the University of Düsseldorf. Since 1991 he is director of the Institute of Neural Information Processing at Ulm University. He retired in 2016 and is working part-time on neural data analysis at the Forschungszentrum Jülich since 2017.

Professor Palm's research focus is oninformation theory, neural networks, associative memory, and specifically on Hebbian cell assemblies. By 2015, he has published more than 300 peer-reviewed articles in international journals, 60 invited contributions, and (co-)edited 8 books. He is author of the monographs “Neural Assemblies. An Alternative Approach to Artificial Intelligence” (1982), and “Novelty, Information and Surprise” (2012).


Bibliographic Information

  • Book Title: Neural Assemblies

  • Book Subtitle: An Alternative Approach to Classical Artificial Intelligence

  • Authors: Günther Palm

  • DOI: https://doi.org/10.1007/978-3-031-00311-0

  • Publisher: Springer Cham

  • eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-031-00310-3Published: 29 July 2022

  • Softcover ISBN: 978-3-031-00313-4Published: 29 July 2023

  • eBook ISBN: 978-3-031-00311-0Published: 28 July 2022

  • Edition Number: 2

  • Number of Pages: XXI, 259

  • Number of Illustrations: 77 b/w illustrations, 10 illustrations in colour

  • Topics: Neurosciences, Computer Science, general, Artificial Intelligence, Data Structures and Information Theory

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