Neural connectomics challenge /

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Bibliographic Details
Imprint:Cham : Springer, 2017.
Description:1 online resource
Series:The Springer series on challenges in machine learning, 2520-131X
Springer series on challenges in machine learning,
Subject:Computational neuroscience.
Neural networks (Neurobiology)
MEDICAL -- Physiology.
SCIENCE -- Life Sciences -- Human Anatomy & Physiology.
Computational neuroscience.
Neural networks (Neurobiology)
Electronic books.
Format: E-Resource Book
URL for this record:
Hidden Bibliographic Details
Other authors / contributors:Battaglia, Demian.
Guyon, Isabelle, editor of compilation.
Lemaire, Vincent, editor of compilation.
Orlandi, Javier, editor of compilation.
Ray, Bisakha, editor of compilation.
Soriano, Jordi, editor of compilation.
Digital file characteristics:text file PDF
Notes:Includes bibliographical references.
Print version record.
Summary: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.
Other form:Print version: Neural connectomics challenge. Cham : Springer, 2017 3319530690 9783319530697
Standard no.:10.1007/978-3-319-53070-3