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Neural Connectomics Challenge

  • Book
  • © 2017

Overview

Editors:
  1. Demian Battaglia

    1. Institute for Systems Neuroscience, University Aix-Marseille, Marseille, France

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  2. Isabelle Guyon

    1. ChaLearn, Berkeley, USA

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  3. Vincent Lemaire

    1. Orange Labs, Lannion, France

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  4. Javier Orlandi

    1. FMC Department, University of Barcelona, Barcelona, Spain

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  5. Bisakha Ray

    1. NYU School of Medicine, New York, USA

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  6. Jordi Soriano

    1. FMC Department, University of Barcelona, Barcelona, Spain

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  • 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

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-x
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  2. First Connectomics Challenge: From Imaging to Connectivity

    • Javier Orlandi, Bisakha Ray, Demian Battaglia, Isabelle Guyon, Vincent Lemaire, Mehreen Saeed et al.
    Pages 1-22

  3. Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging

    • Antonio Sutera, Arnaud Joly, Vincent Franois-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst et al.
    Pages 23-36

  4. Supervised Neural Network Structure Recovery

    • Ildefons Magrans de Abril, Ann Nowé
    Pages 37-45

  5. Signal Correlation Prediction Using Convolutional Neural Networks

    • Lukasz Romaszko
    Pages 47-60

  6. Reconstruction of Excitatory Neuronal Connectivity via Metric Score Pooling and Regularization

    • Chenyang Tao, Wei Lin, Jianfeng Feng
    Pages 61-71

  7. Neural Connectivity Reconstruction from Calcium Imaging Signal Using Random Forest with Topological Features

    • Wojciech M. Czarnecki, Rafal Jozefowicz
    Pages 73-83

  8. Efficient Combination of Pairwise Feature Networks

    • Pau Bellot, Patrick E. Meyer
    Pages 85-93

  9. Predicting Spiking Activities in DLS Neurons with Linear-Nonlinear-Poisson Model

    • Sisi Ma, David J. Barker
    Pages 95-103

  10. SuperSlicing Frame Restoration for Anisotropic ssTEM and Video Data

    • Dmitry Laptev, Joachim M. Buhmann
    Pages 105-115

  11. Back Matter

    Pages 117-117
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Keywords

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.
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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

  • Institute for Systems Neuroscience, University Aix-Marseille, Marseille, France

    Demian Battaglia

  • ChaLearn, Berkeley, USA

    Isabelle Guyon

  • Orange Labs, Lannion, France

    Vincent Lemaire

  • FMC Department, University of Barcelona, Barcelona, Spain

    Javier Orlandi, Jordi Soriano

  • NYU School of Medicine, New York, USA

    Bisakha Ray

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

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