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https://github.com/pyrates-neuroscience/pyrates

Open-source, graph-based Python code generator and analysis toolbox for dynamical systems (pre-implemented and custom models). Most pre-implemented models belong to the family of neural population models.
https://github.com/pyrates-neuroscience/pyrates

code-generation delayed-differential-equation differential-equations dynamical-systems fortran90 julia matlab network-simulator neural-networks numpy parameter-search python pytorch scientific-computing scientific-research simulations tensorflow

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Open-source, graph-based Python code generator and analysis toolbox for dynamical systems (pre-implemented and custom models). Most pre-implemented models belong to the family of neural population models.

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README 

        
PyRates

=======



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PyRates is a framework for dynamical systems modeling, developed by Richard Gast and Daniel Rose. 

It is an open-source project that everyone is welcome to contribute to.



Basic features

===============



Basic features:

---------------



- Frontend:

   - implement models via a frontend of your choice: *YAML* or *Python*

   - create basic mathematical building blocks (i.e. differential equations and algebraic equations) and use them to define a networks of nodes connected by edges

   - create hierarchical networks by connecting networks via edges

- Backend:

   - choose from a number of different backends

   - `NumPy` backend for dynamical systems modeling on CPUs via *Python*

   - `Tensorflow` and `PyTorch` backends for parameter optimization via gradient descent and dynamical systems modeling on GPUs

   - `Julia` backend for dynamical system modeling in *Julia*, via tools such as `DifferentialEquations.jl`

   - `Fortran` backend for dynamical systems modeling via *Fortran 90* and interfacing the parameter continuation software *Auto-07p*

   - `Matlab` backend for differential equation solving via Matlab

- Other features:

   - perform quick numerical simulations via a single function call

   - choose between different numerical solvers

   - perform parameter sweeps over multiple parameters at once

   - generate backend-specific run functions that evaluate the vector field of your dynamical system

   - Implement dynamic edge equations that include scalar delays or delay distributions (delay distributions are automatically translated into gamma-kernel convolutions)

   - choose from various pre-implemented dynamical systems that can be directly used for simulations or integrated into custom models



Installation

============



Stable release (PyPI)

---------------------



PyRates can be installed via the `pip` command. We recommend to use `Anaconda` to create a new python environment with Python >= 3.6 and then simply run the following line from a terminal with the environment being activated:

```

pip install pyrates

```



You can install optional (non-default) packages by specifying one or more options in brackets, e.g.:

```

pip install pyrates[backends]

```



Available options are `backends`, `dev`, and `all` at the moment. 

The latter includes all optional packages. 

Furthermore, the option `tests` includes all packages necessary to run tests found in the github repository.



Development version (github)

----------------------------



Alternatively, it is possible to clone this repository and run one of the following lines 

from the directory in which the repository was cloned:

```

python setup.py install

```

or

```

pip install '.[]'

```



Documentation

=============



For a full API of PyRates, see https://pyrates.readthedocs.io/en/latest/.

For examplary simulations and model configurations, please have a look at the jupyter notebooks provided in the documenation folder.



References

==========



If you use this framework, please cite:



[Gast, R., Knösche, T. R. & Kennedy, A. (2023). PyRates - A Code-Generation Tool for Dynamical Systems Modeling. PLOS Computational Biology 19 (12), e1011761.](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011761)



and



[Gast, R., Rose, D., Salomon, C., Möller, H. E., Weiskopf, N., & Knösche, T. R. (2019). PyRates-A Python framework for rate-based neural simulations. PloS one, 14(12):e0225900.](https://doi.org/10.1371/journal.pone.0225900)



Other work that used PyRates:



[Weise, K., Poßner, L., Müller, E., Gast, R. & Knösche, T. R. (2020) Software X, 11:100450.](https://www.sciencedirect.com/science/article/pii/S2352711020300078)



[Gast, R., Gong, R., Schmidt, H., Meijer, H.G.E., & Knösche, T.R. (2021) On the Role of Arkypallidal and Prototypical Neurons for Phase Transitions in the External Pallidum. Journal of Neuroscience, 41(31):6673-6683.](https://www.jneurosci.org/content/41/31/6673.abstract)



[Gast, R., Solla, S.A. & Kennedy, A. (2023). Macroscopic dynamics of neural networks with heterogeneous spiking thresholds. Physical Review E, 107(2):024306.](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.107.024306)



Contact

=======



If you have questions, problems or suggestions regarding PyRates, please contact [Richard Gast](https://www.richardgast.me).