This package is written by:
- Hooram Nam,
hooramnam@openmailbox.org - Jakob Macke,
jakob.macke@caesar.de
This repository contains different methods for the Gaussian process model with Poisson observations. It has been developed and implemented with the goal of modelling spike-train recordings from neural populations, but some of the methods will be applicable more generally.
In particular, the repository includes methods for
- Laplace approximation for state-inference
- Variational method for state-inference
- Expectation maximisation for parameter learning, using Laplace or Variational inference
- Full EM, where all available trials are processed in each iteration
- Variants of stochastic EM, where a subset of avilable trials are processed in each iteration
- matplotlib == 1.4.3
- pandas == 0.16.2
- numpy == 1.10.1
- scipy == 0.16.1
- statsmodels == 0.6.1
To get started, run the example script either by python example.py in bash orrun example.py in iPython. The software is developed within the Anaconda python 3 environment.
If you notice a bug, want to request a feature, or have a question or feedback, please make use of the issue-tracking capabilities of the repository. We love to hear from people using our code -- please send an email to info@mackelab.org.
The code in this repository is a work in progress. This work is published under the GNU General Public License. The code is provided "as is" and has no warranty whatsoever.