Connectionists: ITE toolbox: new features, JMLR

[Zoltan Szabo]

(apologies for cross-posting)

Dear Connectionists,

The estimation of information theoretical quantities is of crucial 
importance in numerous problems of computational neuroscience. The 
redesigned ITE (Information Theoretical Estimators) toolbox, which 
obtained many new features and functionalities since its initial 
release, might be of your interest in this respect. The package has 
recently appeared in JMLR, a short summary of its current capabilities:

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Information Theoretical Estimators Toolbox

Zoltán Szabó;

JMLR 15(Jan):283-287, 2014.

Link: http://jmlr.org/papers/v15/szabo14a.html


Abstract

We present ITE (information theoretical estimators) a free and open 
source, multi-platform, Matlab/Octave toolbox that is capable of 
estimating many different variants of entropy, mutual information, 
divergence, association measures, cross quantities, and kernels on 
distributions. Thanks to its highly modular design, ITE supports 
additionally (i) the combinations of the estimation techniques, (ii) the 
easy construction and embedding of novel information theoretical 
estimators, and (iii) their immediate application in information 
theoretical optimization problems. ITE also includes a prototype 
application in a central problem class of signal processing, independent 
subspace analysis and its extensions.

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ITE is hosted on Bitbucket ("https://bitbucket.org/szzoli/ite/"); feel 
free to use it.

Best,

Zoltan

-- 
Zoltan Szabo
Gatsby Computational Neuroscience Unit
University College London
http://www.gatsby.ucl.ac.uk/~szabo/










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