Data-Driven Computational Neuroscience: Machine Learning and Statistical Models

Bielza, Concha, Larrañaga, Pedro

  • 出版商: Cambridge
  • 出版日期: 2020-11-26
  • 售價: $3,240
  • 貴賓價: 9.5$3,078
  • 語言: 英文
  • 頁數: 708
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 110849370X
  • ISBN-13: 9781108493703
  • 相關分類: Machine Learning
  • 立即出貨 (庫存=1)

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商品描述

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered.

商品描述(中文翻譯)

資料驅動的計算神經科學有助於將資料轉化為對大腦結構和功能的洞察。這本針對研究人員和研究生的介紹是對神經科學統計和機器學習方法的首次深入全面的探討。這些方法通過真實問題的案例研究來演示,以使讀者能夠建立自己的解決方案。本書涵蓋了各種方法,包括使用非概率模型進行監督分類(最近鄰、分類樹、規則歸納、人工神經網絡和支持向量機)和概率模型(判別分析、邏輯回歸和貝葉斯網絡分類器)、元分類器、多維分類器和特徵子集選擇方法。書中的其他部分專門介紹了使用概率圖模型(貝葉斯網絡和馬爾可夫網絡)進行關聯發現以及使用點過程進行空間統計(完全空間隨機性和集群、規則和吉布斯過程)。本書考慮了細胞、結構、功能、醫學和行為神經科學的各個層面。