LEADER 03645nam  22006135  450 
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010   $a3-319-32285-0
024 7 $a10.1007/978-3-319-32285-8
035   $a(CKB)3710000000667193
035   $a(DE-He213)978-3-319-32285-8
035   $a(MiAaPQ)EBC4527902
035   $a(PPN)194079422
035   $a(EXLCZ)993710000000667193
100   $a20160512d2016      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aComputational Modeling of Neural Activities for Statistical Inference  /$fby Antonio Kolossa
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (XXIV, 127 p. 42 illus., 20 illus. in color.) 
311   $a3-319-32284-2 
320   $aIncludes bibliographical references.
327   $aBasic 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.
330   $aThis 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. .
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