DocumentCode
3525892
Title
Some statistical issues in estimating information in neural spike trains
Author
Vu, Vincent Q. ; Yu, Bin ; Kass, Robert E.
Author_Institution
Dept. of Stat., Univ. of California, Berkeley, CA
fYear
2009
fDate
19-24 April 2009
Firstpage
3509
Lastpage
3512
Abstract
Information theory provides an attractive framework for attacking the neural coding problem. This entails estimating information theoretic quantities from neural spike train data. This paper highlights two issues that may arise: non-parametric entropy estimation and non-stationarity. It gives an overview of these issues and some of the progress that has been made.
Keywords
entropy; maximum likelihood estimation; medical signal processing; neurophysiology; Information theory; information estimation; neural coding problem; neural spike trains; nonparametric entropy estimation; Cognition; Entropy; Estimation theory; Information theory; Mutual information; Nervous system; Neurons; Pulse generation; Signal generators; Statistics; entropy; estimation; information theory; nervous system;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location
Taipei
ISSN
1520-6149
Print_ISBN
978-1-4244-2353-8
Electronic_ISBN
1520-6149
Type
conf
DOI
10.1109/ICASSP.2009.4960382
Filename
4960382
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