Neural Connectomics Challenge

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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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.

• Neuronal networks
• Effective connectivity
• Neural imaging
• Graph-theoretic measures
• Pattern recognition
• Network inference
• Partial correlation
• Deep learning
• Connectomes
• Connectomics
• Network reconstruction
• Neural connectomics
• Causality
• Time series
• Machine learning

• 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

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