Neural Connectomics Challenge (The Springer Series on Challenges in Machine Learning)
暫譯: 神經連結組學挑戰(施普林格機器學習挑戰系列)
- 出版商: Springer
- 出版日期: 2017-05-12
- 售價: $4,200
- 貴賓價: 9.5 折 $3,990
- 語言: 英文
- 頁數: 117
- 裝訂: Hardcover
- ISBN: 3319530690
- ISBN-13: 9783319530697
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相關分類:
Machine Learning
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相關主題
商品描述
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.
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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.
商品描述(中文翻譯)
本書闡述了科學界利用機器學習概念來解決一個複雜問題的努力:給定神經元自發活動的時間序列,網絡中神經元之間的基本連接是什麼?貢獻的作者們還開發了工具,以透過機器學習技術推進神經科學,重點關注神經科學中的主要開放問題。
雖然這些技術是為特定應用而開發,但它們解決了從觀察時間序列重建網絡的更一般問題,這是一個在多個領域中都受到關注的問題,包括計量經濟學、流行病學和氣候學等。
本書旨在為從事神經科學問題研究的數學、物理和計算機科學社群而設。內容也適合機器學習社群,因為它示範了如何從不同的角度來處理相同的問題。