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Analysis of Parallel Spike Trains

  • Book
  • © 2010

Overview

Editors:
  1. Sonja Grün

    1. Computational Neuroscience Group, RIKEN Brain Science Institute, Saitama, Japan

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  2. Stefan Rotter

    1. Bernstein-Zentrum für Computational Neur, Neuroscience, Universität Freiburg, Freiburg, Germany

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    You can also search for this editor in PubMed   Google Scholar

  • This first textbook on spike train analysis
  • Supplies the reader with stochastic modeling tools and numerical methods.
  • Highlights various prerequisites and pitfalls to avoid potentially wrong interpretations of data.
  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Series in Computational Neuroscience (NEUROSCI, volume 7)

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Table of contents (21 chapters)

  1. Practical Issues

    1. Front Matter

      Pages 343-343
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  2. Practical issues

    1. Simulation of Stochastic Point Processes with Defined Properties

      • Stefano Cardanobile, Stefan Rotter
      Pages 345-357

    2. Generation and Selection of Surrogate Methods for Correlation Analysis

      • Sebastien Louis, Christian Borgelt, Sonja Grün
      Pages 359-382

    3. Bootstrap Tests of Hypotheses

      • Valérie Ventura
      Pages 383-398

    4. Generating Random Numbers

      • Hans Ekkehard Plesser
      Pages 399-411

    5. Practically Trivial Parallel Data Processing in a Neuroscience Laboratory

      • Michael Denker, Bernd Wiebelt, Denny Fliegner, Markus Diesmann, Abigail Morrison
      Pages 413-436

  3. Erratum to: Population Coding

    • Stefano Panzeri, Fernando Montani, Giuseppe Notaro, Cesare Magri, Rasmus S. Petersen
    Pages E1-E1

  4. Back Matter

    Pages 439-443
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Keywords

About this book

Solid and transparent data analysis is the most important basis for reliable interpretation of experiments. The technique of parallel spike train recordings using multi-electrode arrangements has been available for many decades now, but only recently gained wide popularity among electro physiologists. Many traditional analysis methods are based on firing rates obtained by trial-averaging, and some of the assumptions for such procedures to work can be ignored without serious consequences. The situation is different for correlation analysis, the result of which may be considerably distorted if certain critical assumptions are violated. The focus of this book is on concepts and methods of correlation analysis (synchrony, patterns, rate covariance), combined with a solid introduction into approaches for single spike trains, which represent the basis of correlations analysis. The book also emphasizes pitfalls and potential wrong interpretations of data due to violations of critical assumptions.

Editors and Affiliations

  • Computational Neuroscience Group, RIKEN Brain Science Institute, Saitama, Japan

    Sonja Grün

  • Bernstein-Zentrum für Computational Neur, Neuroscience, Universität Freiburg, Freiburg, Germany

    Stefan Rotter

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