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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. . 606 $aNeural networks (Computer science) 606 $aBiomedical engineering 606 $aNeurosciences 606 $aBiomathematics 606 $aComputer simulation 606 $aMathematical Models of Cognitive Processes and Neural Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/M13100 606 $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X 606 $aNeurosciences$3https://scigraph.springernature.com/ontologies/product-market-codes/B18006 606 $aPhysiological, Cellular and Medical Topics$3https://scigraph.springernature.com/ontologies/product-market-codes/M31020 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 615 0$aNeural networks (Computer science) 615 0$aBiomedical engineering. 615 0$aNeurosciences. 615 0$aBiomathematics. 615 0$aComputer simulation. 615 14$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aBiomedical Engineering and Bioengineering. 615 24$aNeurosciences. 615 24$aPhysiological, Cellular and Medical Topics. 615 24$aSimulation and Modeling. 676 $a519 700 $aKolossa$b Antonio$4aut$4http://id.loc.gov/vocabulary/relators/aut$0755869 906 $aBOOK 912 $a9910254061603321 996 $aComputational modeling of neural activities for statistical inference$91523236 997 $aUNINA