Metadata-Version: 2.1
Name: EnsemblePursuit
Version: 0.0.2
Summary: A sparse matrix factorization algorithm for extracting co-activating neurons from large-scale recordings
Home-page: https://github.com/MouseLand/EnsemblePursuit
Author: Marius Pachitariu, Carsen Stringer, Maria Kesa
Author-email: maria.kesa@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.13.0)
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: torch

# EnsemblePursuit-- a sparse matrix factorization algorithm for extracting co-activating neurons from large-scale recordings

Ensemble Pursuit is a matrix factorization algorithm that extracts sparse neural components of co-activating cells. 

<img src="https://github.com/mariakesa/EnsemblePursuit/blob/master/Figures/fig11.png" height="150" width="350">

The matrix U is a sparse matrix (because of an L0 penalty in the cost function) that encodes which neurons belong to a component. V is an average timecourse of these neurons, e.g. component time course.

For more details see the  [wiki](https://github.com/mariakesa/EnsemblePursuit/wiki) and our [Statistical Analysis of Neural Data 2019 workshop poster](https://github.com/mariakesa/EnsemblePursuit/blob/master/sand_poster2019.pdf)

Ensembles learned using EnsemblePursuit from recordings in V1 have Gabor receptive fields. 

![alt text](https://github.com/mariakesa/EnsemblePursuit/blob/master/Figures/ep_rec_fields.png)

Some ensembles are well explained by behavior PC's extracted from mouse orofacial movies.

![](https://github.com/mariakesa/EnsemblePursuit/blob/master/Figures/mouse.gif)


![alt text](https://github.com/mariakesa/EnsemblePursuit/blob/master/Figures/Behavior.png)




