Neuro-MINE (Model Identification of Neural Encoding) 🧠💻
Welcome to Neuro-MINE: your handy companion for processing neuronal response data!
This app allows users to use MINE as a GUI or in the command line to train a flexible, convolutional neural network (CNN) to analyze experimental datasets containing neural activity and corresponding predictors (e.g., behavioral responses).
Neuro-MINE makes an updated version of MINE (Costabile et al., 2023) available in an easy-to-use interface. This version of MINE now supports spiking data as well as episodic data. For episodic data, care is taken that model fits, Taylor decomposition, and prediction correctly handle episode boundaries. Furthermore, Neuro-MINE provides easily interpretable outputs in a model insights file that can be used as starting points for further analysis.
How to use
Read The Docs: https://neuro-mine.readthedocs.io
Acknowledgements
Authors:
Danica Matovic
Martin Haesemeyer
Jamie Costabile
Kaarthik Balakrishnan
Sina Schwinn
Publication: Costabile JD, Balakrishnan KA, Schwinn S, Haesemeyer M. Model discovery to link neural activity to behavioral tasks. Elife. 2023 Jun 6;12:e83289. doi: 10.7554/eLife.83289. PMID: 37278516; PMCID: PMC10310322. https://elifesciences.org/articles/83289
GitHub Repository of Original Publication: https://github.com/haesemeyer/mine_pub
Lab Website: https://www.thermofish.org/
Python Package: https://pypi.org/project/neuro-mine/
All code is licensed under the MIT license. See LICENSE for details.
© Martin Haesemeyer, Jamie D Costabile, Kaarthik A Balakrishnan, and Danica Matovic 2020-2025
Questions may be directed to haesemeyer.1@osu.edu
1 maintainer
Metadata
1 maintainer
Release files for neuro-mine 0.9.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| neuro_mine-0.9.1.tar.gz | 53.3 kB | Without provenance | Details for neuro_mine-0.9.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| neuro_mine-0.9.1-py3-none-any.whl | Python 3 | none | any | Details for neuro_mine-0.9.1-py3-none-any.whl |
Total release size: 114.4 kB
Release files / neuro_mine-0.9.1.tar.gz
| Download URL | neuro_mine-0.9.1.tar.gz |
|---|---|
| Size | 53.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
5de85e6d7176741414be627d8b884bf7007436a58681547a45ea9023db4ed2b9
|
|
BLAKE2b-256 checksum How to use checksums |
115df6e8cc6088bc010bf0d2be59e8c8266f9d6b8f596e651e713c3eb17998e9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.6
|
Release files / neuro_mine-0.9.1-py3-none-any.whl
| Download URL | neuro_mine-0.9.1-py3-none-any.whl |
|---|---|
| Size | 61.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
1245d9b7001415e2a52682783988073d22f7f2d24758af3c1829daa73b9930a9
|
|
BLAKE2b-256 checksum How to use checksums |
098e22b409e37d56c5205e6436e79e918a38a5cb2bc473fc038908659ad4fbd0
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.9.6
|
Security for neuro-mine 0.9.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
