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
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;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2009.4960382
