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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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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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Computational Modeling of Neural Activities for Statistical Inference
Verlag:
Springer International Publishing, 2018
ISBN 10: 3319812432
ISBN 13: 9783319812434
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 codin. Bestandsnummer des Verkäufers 448756135
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Computational Modeling of Neural Activities for Statistical Inference
ISBN 10: 3319812432
ISBN 13: 9783319812434
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Taschenbuch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 152 pp. Englisch. Bestandsnummer des Verkäufers 9783319812434
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Computational Modeling of Neural Activities for Statistical Inference
ISBN 10: 3319812432
ISBN 13: 9783319812434
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This authored monograph supplies empirical evidence for the Bayesian brain hypothesis bymodeling event-related potentials (ERP) of the human electroencephalogram (EEG)during successive trials in cognitive tasks. The employed observer models are useful to computeprobability distributions over observable events and hidden states, depending onwhich are present in the respective tasks. Bayesianmodel selection is then used to choose the model which best explains the ERP amplitudefluctuations.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. Bestandsnummer des Verkäufers 9783319812434
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Computational Modeling of Neural Activities for Statistical Inference
Verlag:
Springer International Publishing Mai 2018, 2018
ISBN 10: 3319812432
ISBN 13: 9783319812434
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This authored monograph supplies empirical evidence for the Bayesian brain hypothesis bymodeling event-related potentials (ERP) of the human electroencephalogram (EEG)during successive trials in cognitive tasks. The employed observer models are useful to computeprobability distributions over observable events and hidden states, depending onwhich are present in the respective tasks. Bayesianmodel selection is then used to choose the model which best explains the ERP amplitudefluctuations.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. 152 pp. Englisch. Bestandsnummer des Verkäufers 9783319812434
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Computational Modeling of Neural Activities for Statistical Inference
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Computational Modeling of Neural Activities for Statistical Inference
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Computational Modeling of Neural Activities for Statistical Inference
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Computational Modeling of Neural Activities for Statistical Inference
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Computational Modeling of Neural Activities for Statistical Inference
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