Computational Modeling of Neural Activities for Statistical Inference by Antonio Kolossa.

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...

Full description

Saved in:
Bibliographic Details
Main Author: Kolossa, Antonio (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Series:Springer eBook Collection.
Subjects:
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.

MARC

LEADER 00000nam a22000005i 4500
001 b3285186
003 MWH
005 20191026102105.0
007 cr nn 008mamaa
008 160512s2016 gw | s |||| 0|eng d
020 |a 9783319322858 
024 7 |a 10.1007/978-3-319-32285-8  |2 doi 
035 |a (DE-He213)978-3-319-32285-8 
050 4 |a E-Book 
072 7 |a PBWH  |2 bicssc 
072 7 |a MAT003000  |2 bisacsh 
072 7 |a PBWH  |2 thema 
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 
490 1 |a Springer eBook Collection 
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. . 
590 |a Loaded electronically. 
590 |a Electronic access restricted to members of the Holy Cross Community. 
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. 
690 |a Electronic resources (E-books) 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
830 0 |a Springer eBook Collection. 
856 4 0 |u https://holycross.idm.oclc.org/login?auth=cas&url=https://doi.org/10.1007/978-3-319-32285-8  |3 Click to view e-book  |t 0 
907 |a .b32851868  |b 04-18-22  |c 02-26-20 
998 |a he  |b 02-26-20  |c m  |d @   |e -  |f eng  |g gw   |h 0  |i 1 
912 |a ZDB-2-SMA 
950 |a Mathematics and Statistics (Springer-11649) 
902 |a springer purchased ebooks 
903 |a SEB-COLL 
945 |f  - -   |g 1  |h 0  |j  - -   |k  - -   |l he   |o -  |p $0.00  |q -  |r -  |s b   |t 38  |u 0  |v 0  |w 0  |x 0  |y .i21983483  |z 02-26-20 
999 f f |i fe856b90-0e72-52e5-ac7e-9bbbfa498b13  |s 09d5a4d2-f9a0-5be7-9d4e-a2398c4a72c9  |t 0 
952 f f |p Online  |a College of the Holy Cross  |b Main Campus  |c E-Resources  |d Online  |t 0  |e E-Book  |h Library of Congress classification  |i Elec File