© 2016

Computational Modeling of Neural Activities for Statistical Inference

  • Provides empirical evidence for the Bayesian brain hypothesis

  • Presents observer models which are useful to compute probability distributions over observable events and hidden states

  • Helps the reader to better understand the neural coding by means of Bayesian rules


Table of contents

  1. Front Matter
    Pages i-xxiv
  2. Antonio Kolossa
    Pages 71-110
  3. Antonio Kolossa
    Pages 111-113
  4. Back Matter
    Pages 115-127

About this book


This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field.



Event-related potentials Digital filtering model Design matrices for model estimation Bayesian observer model Neural statistical inference

Authors and affiliations

  1. 1.Inst. für NachrichtentechnikTechnische Universität BraunschweigBraunschweigGermany

Bibliographic information