大葉大學圖書館 |

Computational modeling of neural activities for statistical inference[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	612.813
書名/作者:	Computational modeling of neural activities for statistical inference/ by Antonio Kolossa.
作者:	Kolossa, Antonio.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xxiv, 127 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Evoked potentials (Electrophysiology) - Statistical methods.
標題:	Mathematics.
標題:	Mathematical Models of Cognitive Processes and Neural Networks.
標題:	Biomedical Engineering.
標題:	Neurosciences.
標題:	Physiological, Cellular and Medical Topics.
標題:	Simulation and Modeling.
ISBN:	9783319322858
ISBN:	9783319322841
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-32285-8

Computational modeling of neural activities for statistical inference[electronic resource] /
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:

647270
Evoked potentials (Electrophysiology)
--Statistical methods.

LC Class. No.: RC386.6.E86

Dewey Class. No.: 612.813

Computational modeling of neural activities for statistical inference[electronic resource] /
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