Overview
- Explains how machine learning tools have the capacity to predict the behavior or response of a complex system
- Offers tools for the advancement of neuroscience through machine learning techniques
- Combines elements of mathematics, physics, and computer science research
- Includes supplementary material: sn.pub/extras
Part of the book series: The Springer Series on Challenges in Machine Learning (SSCML)
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Table of contents (9 chapters)
Keywords
About this book
While the techniques have been developed for a specific application, they address the more general problem of network reconstruction from observational time series, a problem of interest in a wide variety of domains, including econometrics, epidemiology, and climatology, to cite only a few.
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The book is designed for the mathematics, physics and computer science communities that carry out research in neuroscience problems. The content is also suitable for the machine learning community because it exemplifies how to approach the same problem from different perspectives.
Editors and Affiliations
Bibliographic Information
Book Title: Neural Connectomics Challenge
Editors: Demian Battaglia, Isabelle Guyon, Vincent Lemaire, Javier Orlandi, Bisakha Ray, Jordi Soriano
Series Title: The Springer Series on Challenges in Machine Learning
DOI: https://doi.org/10.1007/978-3-319-53070-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-53069-7Published: 12 May 2017
Softcover ISBN: 978-3-319-85054-2Published: 08 May 2018
eBook ISBN: 978-3-319-53070-3Published: 04 May 2017
Series ISSN: 2520-131X
Series E-ISSN: 2520-1328
Edition Number: 1
Number of Pages: X, 117
Number of Illustrations: 28 b/w illustrations
Topics: Artificial Intelligence, Image Processing and Computer Vision