By Topic

Markov dependency based on Shannon's entropy and its application to neural spike trains

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Nakahama, H. ; Inst. of Brain Diseases, Tohoku Univ. School of Medicine, Sendai, Japan ; Yamamoto, M. ; Aya, K. ; Shima, K.
more authors

A measure of simplified dependency is introduced representing Markovian characteristics based on Shannon's entropy and conditional entropy under the Gaussian assumption. It is considered to be the most concise measure for expressing the higher order statistical properties of a time series and, in this regard, to be superior to a correlation or spectral measure. Simplified dependency is shown to be closely related to the prediction error in the autoregressive analysis of a time series and to be applicable also to non-Gaussian processes. Both the truncation method of distribution and the ensemble dependency analysis are informative for clarifying the statistical characteristics of interval sequence of a skewed distribution in a heterogeneous time series. These techniques serve to clarify the neural modulation mechanism.

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:SMC-13 ,  Issue: 5 )