Neural Connectomics Challenge

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106,99 

The Springer Series on Challenges in Machine Learning

ISBN: 3319530690
ISBN 13: 9783319530697
Herausgeber: Demian Battaglia/Isabelle Guyon/Vincent Lemaire et al
Verlag: Springer Verlag GmbH
Umfang: x, 117 S., 28 s/w Illustr., 117 p. 28 illus.
Erscheinungsdatum: 12.05.2017
Auflage: 1/2017
Produktform: Gebunden/Hardback
Einband: GEB

Explains how machine learning tools have the capacity to predict the behavior or response of a complex systemOffers tools for the advancement of neuroscience through machine learning techniquesCombines elements of mathematics, physics, and computer science researchIncludes supplementary material: sn.pub/extras

Artikelnummer: 847932 Kategorie:

Beschreibung

This book illustrates the thrust of the scientific community to use machine learning concepts for tackling a complex problem: given time series of neuronal spontaneous activity, which is the underlying connectivity between the neurons in the network? The contributing authors also develop tools for the advancement of neuroscience through machine learning techniques, with a focus on the major open problems in neuroscience. 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.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.

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