Text

Computational Modeling of Neural Activities for Statistical Inference

---

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

---

Availability

No copy data

Detail Information

Series Title

-

Call Number

004

Publisher

Switzerland : Springer., 2016

Collation

-

Language

English

ISBN/ISSN

9783319322858

Classification

004

Content Type

-

Media Type

-

Carrier Type

online resource

Edition

-

Subject(s)

Computer Science

Specific Detail Info

-

Statement of Responsibility

-

Other Information

Cataloger

Kurnadi

Source

-

Validator

-

Digital Object Identifier (DOI)

-

Journal Volume

-

Journal Issue

-

Subtitle

-

Parallel Title

-

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment