This code package is for the Tensor-Maximum-Entropy (TME) method. This method generates random surrogate data that preserves a specified set of first and second order marginal moments of a data tensor, which makes it well equipped to test for the null hypothesis that a structure in data is an epiphenomenon of these specified set of primary featu…
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README.md
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README.md

TME

==================================================================== Gamaleldin F. Elsayed, Columbia University

John P. Cunningham, Columbia University

Copyright (C) 2017 Gamaleldin F. Elsayed and John P. Cunningham

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

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==================================================================== Basic Usage Example

Follow the demo.m file

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==================================================================== TME

This code package is for the Tensor-Maximum-Entropy (TME) method. This method generates random surrogate data that preserves a specified set of first and second order marginal moments of a data tensor, which makes it well equipped to test for the null hypothesis that a structure in data is an epiphenomenon of these specified set of primary features of the data tensor. The random surrogate data are sampled from a maximum entropy distribution. This distribution unlike traditional maximum entropy method have constraints on the marginal first and second moments of the tensor mode.

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The BibTeX citations for the primary papers used in this project are:

@article{elsayed, title={Structure in neural population recordings: an expected byproduct of simpler phenomena?}, author={Gamaleldin Elsayed, John Cunningham}, journal={Nature Neuroscience}, volume={}, year={} }

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