The Springer Series on Challenges in Machine Learning

Neural Connectomics Challenge

Editors: Battaglia, D., Guyon, I., Lemaire, V., Orlandi, J., Ray, B., Soriano, J. (Eds.)

  • 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
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eBook $74.99
price for Mexico (gross)
  • ISBN 978-3-319-53070-3
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $99.00
price for Mexico
  • ISBN 978-3-319-53069-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.

Table of contents (9 chapters)

  • First Connectomics Challenge: From Imaging to Connectivity

    Orlandi, Javier (et al.)

    Pages 1-22

  • Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging

    Sutera, Antonio (et al.)

    Pages 23-36

  • Supervised Neural Network Structure Recovery

    Magrans de Abril, Ildefons (et al.)

    Pages 37-45

  • Signal Correlation Prediction Using Convolutional Neural Networks

    Romaszko, Lukasz

    Pages 47-60

  • Reconstruction of Excitatory Neuronal Connectivity via Metric Score Pooling and Regularization

    Tao, Chenyang (et al.)

    Pages 61-71

Buy this book

eBook $74.99
price for Mexico (gross)
  • ISBN 978-3-319-53070-3
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $99.00
price for Mexico
  • ISBN 978-3-319-53069-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

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
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-53070-3
DOI
10.1007/978-3-319-53070-3
Hardcover ISBN
978-3-319-53069-7
Series ISSN
2520-131X
Edition Number
1
Number of Pages
X, 117
Number of Illustrations and Tables
28 b/w illustrations
Topics