neural-ode-ion-channels
Code and figures for "Neural network differential equations for ion channel modelling".
Science Score: 23.0%
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○CITATION.cff file
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✓DOI references
Found 12 DOI reference(s) in README -
✓Academic publication links
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Low similarity (8.3%) to scientific vocabulary
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Repository
Code and figures for "Neural network differential equations for ion channel modelling".
Basic Info
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- Stars: 5
- Watchers: 2
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Metadata Files
README.md
Neural network differential equations for ion channel modelling
Source code associated with an article in Frontiers in Physiology by Chon Lok Lei and Gary R. Mirams.
From left to right shows the original Hodgkin-Huxley model (candidate model), the activation modelled using a neural network (NN-f), the activation with a neural network discrepancy term (NN-d), and the activation modelled with a three-state model (ground truth used in synthetic data studies with discrepancy).
Requirements
To run the code within this repository requires Python 3.5+ with the following dependencies
which can be installed via
$ pip install -r requirements.txt
Train the models
The following codes re-run the training for the models.
Synthetic data studies (no discrepancy)
- NN-f:
train-s1.py - NN-d:
train-s2.py
Synthetic data studies (with discrepancy)
- Candidate model:
train-d0.py - NN-f:
train-d1.py - NN-d:
train-d2.py
Experimental data
- NN-f:
train-r1.py - NN-d:
train-r2.py
Their trained results are stored in directories s1, s2, d1, etc.
Main figures and tables
To re-run and create the main figures and tables, use:
- Figure 2: figure-1.py
- Figure 3: figure-2.py
- Figure 4: figure-3.py
- Figure 5: figure-4.py
- Figure 6: figure-5.py
- Figure 7: figure-6.py
- Figure 8: figure-7.py
- Table 1: table-1.py
- Table 2: table-2.py.
These generate figures in directories figure-1, figure-2, etc.
Supplementary figures and tables
To re-run and create the supplementary figures and tables, use:
- Figure S2: figure-0-s.py
- Figure S3: figure-2-s.py
- Figure S4: figure-3-s.py
- Figure S5: figure-4-s.py
- Figure S6: figure-1-s2.py
- Figure S7: figure-1-s1.py
- Table S1: table-s1.py.
These generate figures in directories figure-2-s, figure-3-s, etc.
Others
data: Contains the experimental data from Beattie et al. 2018.model-structure: Contains Markov diagrams/schematics for the models.test-protocols: Contains time series files for various voltage-clamp protocols from Beattie et al. 2018 and Lei et al. 2019a & b.
Acknowledging this work
If you publish any work based on the contents of this repository please cite (CITATION file):
Lei, C. L. and Mirams, G. R. (2021). Neural network differential equations for ion channel modelling. Frontiers in Physiology, 12, 1166. doi:10.3389/fphys.2021.708944.
Owner
- Name: Chon Lok Lei
- Login: chonlei
- Kind: user
- Location: Macau
- Company: University of Macau
- Website: https://chonlei.github.io
- Twitter: chonloklei
- Repositories: 5
- Profile: https://github.com/chonlei
Lecturer (Macao Fellow) at the University of Macau. DPhil at the University of Oxford. BSc at Imperial College.