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Record Nr. |
UNINA9910254061603321 |
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Autore |
Kolossa Antonio |
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Titolo |
Computational Modeling of Neural Activities for Statistical Inference / / by Antonio Kolossa |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (XXIV, 127 p. 42 illus., 20 illus. in color.) |
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Disciplina |
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Soggetti |
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Neural networks (Computer science) |
Biomedical engineering |
Neurosciences |
Biomathematics |
Computer simulation |
Mathematical Models of Cognitive Processes and Neural Networks |
Biomedical Engineering and Bioengineering |
Physiological, Cellular and Medical Topics |
Simulation and Modeling |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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