Overview
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.
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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.
The book, Neural Connectomics Challenge (The Springer Series on Challenges in Machine Learning) [Bulk, Wholesale, Quantity] ISBN#9783319530697 in Hardcover by may be ordered in bulk quantities. Minimum starts at 25 copies. Availability based on publisher status and quantity being ordered.
Details
Format:
Hardcover
Publication Date:
05/12/2017
ISBN-13:
9783319530697
Pages:
117
Publisher:
Springer International Publishing
