Editors:
- Looks at the morphological properties of dendrites
- Summarizes the approaches to either measure dendrite morphology quantitatively or to actually generate synthetic dendrite morphologies in computer models
- Brings together a wide range of studies, with topics ranging from general to system-specific phenomena, with some having a strong experimental component, and others being fully theoretical
Part of the book series: Springer Series in Computational Neuroscience (NEUROSCI, volume 11)
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Table of contents (29 chapters)
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Front Matter
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Dendritic Morphology
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Front Matter
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Dendritic Computation
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Front Matter
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About this book
Neuronal dendritic trees are complex structures that endow the cell with powerful computing capabilities and allow for high neural interconnectivity. Studying the function of dendritic structures has a long tradition in theoretical neuroscience, starting with the pioneering work by Wilfrid Rall in the 1950s. Recent advances in experimental techniques allow us to study dendrites with a new perspective and in greater detail. The goal of this volume is to provide a résumé of the state-of-the-art in experimental, computational, and mathematical investigations into the functions of dendrites in a variety of neural systems.
The book first looks at morphological properties of dendrites and summarizes the approaches to measure dendrite morphology quantitatively and to actually generate synthetic dendrite morphologies in computer models. This morphological characterization ranges from the study of fractal principles to describe dendrite topologies, to the consequences of optimization principles for dendrite shape. Individual approaches are collected to study the aspects of dendrite shape that relate directly to underlying circuit constraints and computation.
The second main theme focuses on how dendrites contribute to the computations that neurons perform. What role do dendritic morphology and the distributions of synapses and membrane properties over the dendritic tree have in determining the output of a neuron in response to its input? A wide range of studies is brought together, with topics ranging from general to system-specific phenomena—some having a strong experimental component, and others being fully theoretical. The studies come from many different neural systems and animal species ranging from invertebrates to mammals. With this broad focus, an overview is given of the diversity of mechanisms that dendrites can employ to shape neural computations.
Reviews
From the book reviews:
“This is an outstanding reference devoted to the morphologies and functional electrophysiology of Dendrites in the central nervous system. … This is a good textbook for modelers, students, fellows, and advanced experts.” (Joseph J. Grenier, Amazon.com, November, 2014)Editors and Affiliations
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Institute of Clinical Neuroanatomy, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society & Goethe-University, Frankfurt, Germany
Hermann Cuntz
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Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
Michiel W.H. Remme
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Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel
Benjamin Torben-Nielsen
Bibliographic Information
Book Title: The Computing Dendrite
Book Subtitle: From Structure to Function
Editors: Hermann Cuntz, Michiel W.H. Remme, Benjamin Torben-Nielsen
Series Title: Springer Series in Computational Neuroscience
DOI: https://doi.org/10.1007/978-1-4614-8094-5
Publisher: Springer New York, NY
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: Springer Science+Business Media New York 2014
Hardcover ISBN: 978-1-4614-8093-8Published: 23 November 2013
Softcover ISBN: 978-1-4939-5353-0Published: 24 August 2016
eBook ISBN: 978-1-4614-8094-5Published: 23 November 2013
Series ISSN: 2197-1900
Series E-ISSN: 2197-1919
Edition Number: 1
Number of Pages: XVIII, 510
Number of Illustrations: 35 b/w illustrations, 66 illustrations in colour
Topics: Neurosciences, Neurobiology, Mathematical Models of Cognitive Processes and Neural Networks