A sparse matrix factorization algorithm for extracting co-activating neurons from large-scale recordings
Project description
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.
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 and our Statistical Analysis of Neural Data 2019 workshop poster
Ensembles learned using EnsemblePursuit from recordings in V1 have Gabor receptive fields.
Some ensembles are well explained by behavior PC's extracted from mouse orofacial movies.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file EnsemblePursuit-0.0.3.tar.gz.
File metadata
- Download URL: EnsemblePursuit-0.0.3.tar.gz
- Upload date:
- Size: 5.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
10a40bfd2bca5f32f97a2293f59f000e70a3624c5132298aa5136baf7b8488ab
|
|
| MD5 |
b1e241db28de55239ea511f7df3debf0
|
|
| BLAKE2b-256 |
235f1d8ecff042d35565d41e17ceb6fd6b8c7992d6776d997fc4740257c1e18c
|
File details
Details for the file EnsemblePursuit-0.0.3-py3-none-any.whl.
File metadata
- Download URL: EnsemblePursuit-0.0.3-py3-none-any.whl
- Upload date:
- Size: 18.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
8f529e180a7edcc067c5d232395a19916a6b149332130429548b4974a057def9
|
|
| MD5 |
cd21eb9697bc6d0ba1abb32bae790dcb
|
|
| BLAKE2b-256 |
492f59b080622824f89d7a31189998175d364c79a1c91d614d6778b594437f23
|