Statistical Techniques for Neuroscientists
Truong, Young K. | Lewis, Mechelle M.
- 出版商: CRC
- 出版日期: 2016-07-14
- 售價: $8,760
- 貴賓價: 9.5 折 $8,322
- 語言: 英文
- 頁數: 445
- 裝訂: Hardcover
- ISBN: 1466566140
- ISBN-13: 9781466566149
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商品描述
Statistical Techniques for Neuroscientists introduces new and useful methods for data analysis involving simultaneous recording of neuron or large cluster (brain region) neuron activity. The statistical estimation and tests of hypotheses are based on the likelihood principle derived from stationary point processes and time series. Algorithms and software development are given in each chapter to reproduce the computer simulated results described therein.
The book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and multimodal neuroimaging techniques. The author provides an overview of various methods being applied to specific research areas of neuroscience, emphasizing statistical principles and their software. The book includes examples and experimental data so that readers can understand the principles and master the methods.
The first part of the book deals with the traditional multivariate time series analysis applied to the context of multichannel spike trains and fMRI using respectively the probability structures or likelihood associated with time-to-fire and discrete Fourier transforms (DFT) of point processes. The second part introduces a relatively new form of statistical spatiotemporal modeling for fMRI and EEG data analysis. In addition to neural scientists and statisticians, anyone wishing to employ intense computing methods to extract important features and information directly from data rather than relying heavily on models built on leading cases such as linear regression or Gaussian processes will find this book extremely helpful.
商品描述(中文翻譯)
《神經科學家的統計技術》介紹了針對同時記錄神經元或大型神經元群(腦區)活動的數據分析的新方法和實用技術。統計估計和假設檢驗基於從平穩點過程和時間序列中推導出的似然原則。每一章都提供了算法和軟體開發,以重現其中描述的計算機模擬結果。
本書探討了解決神經科學中新興問題的當前統計方法。這些方法已應用於涉及多通道神經脈衝列、脈衝排序、盲源分離、功能和有效神經連接性、時空建模以及多模態神經影像技術的數據。作者提供了各種方法在神經科學特定研究領域中的應用概述,強調統計原則及其軟體。本書包含範例和實驗數據,以便讀者理解原則並掌握這些方法。
本書的第一部分處理傳統的多變量時間序列分析,應用於多通道脈衝列和功能性磁共振成像(fMRI)的背景,分別使用與發火時間相關的概率結構或似然性,以及點過程的離散傅立葉變換(DFT)。第二部分介紹了一種相對較新的統計時空建模形式,用於fMRI和腦電圖(EEG)數據分析。除了神經科學家和統計學家外,任何希望利用強大的計算方法直接從數據中提取重要特徵和信息,而不是過度依賴基於線性回歸或高斯過程等主導案例建立的模型的人,都會發現本書極具幫助。