| 書目名稱 | Neural Connectomics Challenge |
| 編輯 | Demian Battaglia,Isabelle Guyon,Jordi Soriano |
| 視頻video | http://file.papertrans.cn/664/663548/663548.mp4 |
| 概述 | 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.Combi |
| 叢書名稱 | The Springer Series on Challenges in Machine Learning |
| 圖書封面 |  |
| 描述 | 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.. |
| 出版日期 | Book 2017 |
| 關(guān)鍵詞 | Neuronal networks; Effective connectivity; Neural imaging; Graph-theoretic measures; Pattern recognition |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-53070-3 |
| isbn_softcover | 978-3-319-85054-2 |
| isbn_ebook | 978-3-319-53070-3Series ISSN 2520-131X Series E-ISSN 2520-1328 |
| issn_series | 2520-131X |
| copyright | Springer International Publishing AG 2017 |
|Archiver|手機(jī)版|小黑屋|
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