This package implements a statistical test that can assess higher-order correlations of neural population spike counts in terms of an information theoretic analysis. The test yields reliable results even when the number of experimental samples is small. If you use this software for publication, please cite [1].
- demo_mcmaxenttest - Demonstration of how to apply the test to count data
- mcmaxenttest/mcmaxenttest.py - Monte Carlo maximum entropy test
- mcmaxenttest/tests/test_mcmaxenttest.py - Tests for the mcmaxenttest module.
- README.rst - This file
- LICENSE - Software license
The test consists of two parts: (1) construction of a reference distribution which is based on the single neuron spike count distributions and the correlation coefficient and (2) a goodness-of-fit test to calculate a p-value and eventually reject the reference distribution.
The reference distribution formalizes the linear dependency assumption. For this purpose, we apply a maximum entropy model subject to a set of constraints. The constraints contain the complete single neuron spike count distributions and the linear correlation coefficient. Everything is therefore fixed by the distribution constraints except for the higher-order correlations. If this reference distribution can be statistically rejected then we can conclude that higher-order correlations do matter.
The single neuron spike count distributions and the correlation coefficient are not known a priori. Instead, they must be estimated from the data. For simplicity, we assume that the single neuron distributions are Poisson distributed. This leaves us with the estimation of firing rates and the correlation coefficient. The test should be applicable even when the number of samples is very small. Therefore, any estimates of distribution parameters are not reliable. Instead of relying on specific estimates of these parameters, we maximize the p-value over these parameters and then use the most conservative p-value.
Please see [1] for a more detailed description of the test.
1. Onken A, Dragoi V, Obermayer K (2012). A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts. PLoS Comput Biol 8(6): e1002539. doi:10.1371/journal.pcbi.1002539
Copyright (C) 2012, 2017 Arno Onken
The mcmaxenttest package 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, or (at your option) any later version.
The mcmaxenttest package 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/>.