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+.
1 maintainer
Metadata
1 maintainer
Release files for ntm 0.1.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| ntm-0.1.1.tar.gz | 26.0 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ntm-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 53.2 kB
Release files / ntm-0.1.1.tar.gz
| Download URL | ntm-0.1.1.tar.gz |
|---|---|
| Size | 26.0 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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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
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Release files / ntm-0.1.1-py3-none-any.whl
| Download URL | ntm-0.1.1-py3-none-any.whl |
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| Size | 27.2 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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