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.
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
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-319-53070-3
- Copyright Information Springer International Publishing AG 2017
- Publisher Name Springer, Cham
- eBook Packages Computer Science
- Print ISBN 978-3-319-53069-7
- Online ISBN 978-3-319-53070-3
- Series Print ISSN 2520-131X
- Series Online ISSN 2520-1328
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