Statistical techniques for neuroscientists

Young K. Truong, Mechelle Lewis

Research output: Book/ReportBook

Abstract

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.

Original languageEnglish (US)
PublisherCRC Press
Number of pages415
ISBN (Electronic)9781466566156
ISBN (Print)9781466566149
DOIs
StatePublished - Oct 4 2016

Fingerprint

Spike
Spatio-temporal Modeling
Functional Magnetic Resonance Imaging
Neuroscience
Point Process
Neuron
Data analysis
Likelihood Principle
Neurosciences
Neuroimaging
Test of Hypothesis
Computing Methods
Statistical Estimation
Multivariate Time Series
Blind Source Separation
Discrete Fourier transform
Statistical Modeling
Multivariate Analysis
Time Series Analysis
Stationary point

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Neuroscience(all)

Cite this

Truong, Young K. ; Lewis, Mechelle. / Statistical techniques for neuroscientists. CRC Press, 2016. 415 p.
@book{684866c3e02a4d6da73868be539a82cf,
title = "Statistical techniques for neuroscientists",
abstract = "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.",
author = "Truong, {Young K.} and Mechelle Lewis",
year = "2016",
month = "10",
day = "4",
doi = "10.1201/9781315374345",
language = "English (US)",
isbn = "9781466566149",
publisher = "CRC Press",

}

Statistical techniques for neuroscientists. / Truong, Young K.; Lewis, Mechelle.

CRC Press, 2016. 415 p.

Research output: Book/ReportBook

TY - BOOK

T1 - Statistical techniques for neuroscientists

AU - Truong, Young K.

AU - Lewis, Mechelle

PY - 2016/10/4

Y1 - 2016/10/4

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85052456715&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052456715&partnerID=8YFLogxK

U2 - 10.1201/9781315374345

DO - 10.1201/9781315374345

M3 - Book

AN - SCOPUS:85052456715

SN - 9781466566149

BT - Statistical techniques for neuroscientists

PB - CRC Press

ER -