Computational Modeling of Neural Activities for Statistical Inference
Language: English
Published by Palgrave Macmillan, 2016
- Hardcover
- Used
Seller: Buchpark, Trebbin, GermanyBuchpark
AbeBooks seller since September 30, 2021
Condition: Used - Fine
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Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.
Seller Inventory # 26669998/12
- Title
- Computational Modeling of Neural Activities for Statistical Inference
- Author
- Kolossa, Antonio
- Publisher
- Palgrave Macmillan
- Publication year
- 2016
- Condition
- Sehr gut
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3319322842
- ISBN 13
- 9783319322841
- Seller catalogs
- Bücher
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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