Neural Topic Modelling
Project description
Neural Topic Modelling (NTM)
Neural topic modelling uses machine learning methods from text mining to analyse brain data.
Currently, neural topic modelling is being developed for electrophysiological recordings, but will be extended to incorporate LFP traces and Ca2+ imaging recordings.
Neural topic modelling is based on latent dirichlet allocation (LDA, Blei et al., 2003) and makes use of it's scalability to large datasets. Since the number and size of brain recording datasets has increased substantially over the last few years (from 10s to 10,000s), new methods are needed to cope with the copiousness of datasets avaiable to researchers now.
Installation
pip install ntm
Note that you need Python 3.7+.
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 ntm-0.1.1.tar.gz.
File metadata
- Download URL: ntm-0.1.1.tar.gz
- Upload date:
- Size: 26.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ac1f036aacf713bd87b84f82172568a79b50073c945f26b6b68a51e7ec40f9cf
|
|
| MD5 |
773f73a368440ce2952c12551a57b462
|
|
| BLAKE2b-256 |
fbaabb362fd89f1ef59330cc65b7d6c079b7016a78aca5f323202b8050027541
|
File details
Details for the file ntm-0.1.1-py3-none-any.whl.
File metadata
- Download URL: ntm-0.1.1-py3-none-any.whl
- Upload date:
- Size: 27.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f90717bd8bb4e8ad3078df9884806e9341eb4673e9e1ddfeaa1d10eb8b152dad
|
|
| MD5 |
3acc784a58cdf33b6941de09a2ebcf4b
|
|
| BLAKE2b-256 |
f6cf8c6c86fa551551ed0d51b8019eff7f8a9fdef049e6b5d318a57ff39eb1de
|