A framework for defining differential equation based models used in neuroscience and reducing their dimensionality with mathematical model order reduction (MOR) methods.

This project requires Python 3.5 or greater. Use of virtual environments is strongly encouraged, but not required, to avoid clashes with numpy and matplotlib versions! See the venv documentation for more information and installation instructions.

Once your brand new virtual environment is activated, navigate to the folder of this repository and install it with

pip install -e .  

or replace the . with the path to this project. All the dependencies will be downloaded automatically.

Finally, confirm that the installation was successfull by executing the provided tests

python tests/model_test.py
python tests/reduction_test.py

Numpy warnings about the np.matrix class can safely be ignored.

Launch a Jupyter Notebook instance with

jupyter notebook

and find the notebook named fitzhugh_nagumo_meanfield_reduction.ipynb. Follow the notebook!

Citation for this project will be provided soon! For the time being, our previous work studying MOR of a synaptic plasiticy model can be cited as

• Lehtimäki, Mikko, Ippa Seppälä, Lassi Paunonen, and Marja-Leena Linne. "Accelerated Simulation of a Neuronal Population via Mathematical Model Order Reduction." In 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), pp. 118-122. IEEE, 2020.
• Lehtimäki, Mikko, Lassi Paunonen, and Marja-Leena Linne. "Projection-based order reduction of a nonlinear biophysical neuronal network model." In 2019 IEEE 58th Conference on Decision and Control (CDC), pp. 1-6. IEEE, 2019.
• Lehtimäki, M., Paunonen, L., Pohjolainen, S. and Linne, M.L., 2017. Order reduction for a signaling pathway model of neuronal synaptic plasticity. IFAC-PapersOnLine, 50(1), pp.7687-7692.

Our work can also be followed in my ResearchGate profile!