【独家发布】【2016新书】Computational Modeling of Neural Activities for Statistical Infere

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图书名称：Computational Modeling of Neural Activities for Statistical Inference
作者：Antonio Kolossa
出版社：Springer
页数：287
出版时间：2016                           
语言：English
格式：pdf
内容简介：
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.pdf (13.26 MB)

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关键词：Statistical Computation Activities statistica statistic 出版社 hypothesis supplies evidence English

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Computational Modeling of Neural Activities for Statistical Inference

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Bayesian Methods for Multilevel Modeling