Calculating mutual information for spike trains and other data with distances but no coordinates | Zendy

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Calculating mutual information for spike trains and other data with distances but no coordinates

Author(s) -

Conor Houghton

Publication year - 2015

Publication title -

royal society open science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.84

H-Index - 51

ISSN - 2054-5703

DOI - 10.1098/rsos.140391

Subject(s) - spike (software development) , computer science , mutual information , spike train , similarity (geometry) , estimator , pattern recognition (psychology) , artificial intelligence , algorithm , data mining , mathematics , statistics , software engineering , image (mathematics)

Many important data types, such as the spike trains recorded from neurons in typical electrophysiological experiments, have a natural notion of distance or similarity between data points, even though there is no obvious coordinate system. Here, a simple Kozachenko–Leonenko estimator is derived for calculating the mutual information between datasets of this type.

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