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Computational modeling of neural activities for statistical inference

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computational modeling of neural activities for statistical inference/ by Antonio Kolossa.
Author:	Kolossa, Antonio.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xxiv, 127 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Evoked potentials (Electrophysiology) - Statistical methods. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-32285-8
ISBN:	9783319322858

Computational modeling of neural activities for statistical inference
Kolossa, Antonio.

Computational modeling of neural activities for statistical inference[electronic resource] /by Antonio Kolossa. - Cham :Springer International Publishing :2016. - xxiv, 127 p. :ill., digital ;24 cm.

Basic Principles of ERP Research, Surprise, and Probability Estimation -- Introduction to Model Estimation and Selection Methods -- A New Theory of Trial-by-Trial P300 Amplitude Fluctuations -- Bayesian Inference and the Urn-Ball Task -- Summary and Outlook.

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.

ISBN: 9783319322858

Standard No.: 10.1007/978-3-319-32285-8doiSubjects--Topical Terms:

1109383
Evoked potentials (Electrophysiology)
--Statistical methods.

LC Class. No.: RC386.6.E86

Dewey Class. No.: 612.813

Computational modeling of neural activities for statistical inference
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505 0 $a Basic Principles of ERP Research, Surprise, and Probability Estimation -- Introduction to Model Estimation and Selection Methods -- A New Theory of Trial-by-Trial P300 Amplitude Fluctuations -- Bayesian Inference and the Urn-Ball Task -- Summary and Outlook.
520 $a 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.
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