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Computational Modeling of Neural Activities for Statistical Inference
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 obse...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edición: | 1st ed. 2016. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-32285-8 | ||
| 003 | DE-He213 | ||
| 005 | 20220115192218.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 160512s2016 sz | s |||| 0|eng d | ||
| 020 | |a 9783319322858 |9 978-3-319-32285-8 | ||
| 024 | 7 | |a 10.1007/978-3-319-32285-8 |2 doi | |
| 050 | 4 | |a QA76.87 | |
| 072 | 7 | |a PBWH |2 bicssc | |
| 072 | 7 | |a MAT003000 |2 bisacsh | |
| 072 | 7 | |a PBWH |2 thema | |
| 082 | 0 | 4 | |a 519 |2 23 |
| 100 | 1 | |a Kolossa, Antonio. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Computational Modeling of Neural Activities for Statistical Inference |h [electronic resource] / |c by Antonio Kolossa. |
| 250 | |a 1st ed. 2016. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2016. | |
| 300 | |a XXIV, 127 p. 42 illus., 20 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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. . | ||
| 650 | 0 | |a Neural networks (Computer science) . | |
| 650 | 0 | |a Biomedical engineering. | |
| 650 | 0 | |a Neurosciences. | |
| 650 | 0 | |a Biomathematics. | |
| 650 | 0 | |a Computer simulation. | |
| 650 | 1 | 4 | |a Mathematical Models of Cognitive Processes and Neural Networks. |
| 650 | 2 | 4 | |a Biomedical Engineering and Bioengineering. |
| 650 | 2 | 4 | |a Neuroscience. |
| 650 | 2 | 4 | |a Mathematical and Computational Biology. |
| 650 | 2 | 4 | |a Computer Modelling. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319322841 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319322865 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319812434 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-32285-8 |z Texto Completo |
| 912 | |a ZDB-2-SMA | ||
| 912 | |a ZDB-2-SXMS | ||
| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||