DeDist (Decoding Distributions) is a Python library for analytically calculating the full maximum likelihood decoding distribution for a given neural encoding system with Gaussian noise, with arbitrary tuning curves (heterogeneous or homogeneous) and arbitrary noise correlations.
For more information see the paper: S.W. Keemink and M.C.W. Van Rossum (2017) Biases in multivariate neural population codes, ArXiv, https://doi.org/10.1101/113803.
pip install DeDist
See the ipython notebooks in the example folder:
https://github.com/swkeemink/DeDist/blob/master/Examples/Basic%20Usage%201D.ipynb
https://github.com/swkeemink/DeDist/blob/master/Examples/Correlated%20noise%201D.ipynb
S.K. was supported by the EuroSpin Erasmus Mundus program and by the EPSRC Doctoral Training Centre in Neuroinformatics (EP/F500386/1 and BB/F529254/1).
Unless otherwise stated in individual files, all code is Copyright (c) 2017, Sander Keemink and Mark van Rossum. All rights reserved.
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 2 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/.