pyrates 0.9.0

Neural Network Modeling Framework

[](https://github.com/pyrates-neuroscience/PyRates) [](https://travis-ci.com/pyrates-neuroscience/PyRates) <img src=”https://github.com/pyrates-neuroscience/PyRates/blob/master/PyRates_logo_color.svg” width=”20%” heigth=”20%” align=”right”> [](https://badge.fury.io/py/pyrates)

# PyRates PyRates is a framework for neural modeling and simulations, developed by Richard Gast and Daniel Rose at the Max Planck Institute of Human Cognitive and Brain Sciences, Leipzig, Germany.

Basic features:

• Different backends: Numpy for fast simulations of small- to medium-sized networks. Tensorflow for efficient parallelization on GPUs/CPUs, Fortran for parameter continuations.

• Each model is internally represented by a networkx graph of nodes and edges, with the former representing the model units (i.e. single cells, cell populations, …) and the latter the information transfer between them. In principle, this allows to implement any kind of dynamic neural system that can be expressed as a graph via PyRates.

• Solutions of initial value problems via different numerical solvers (e.g. full interface to scipy.integrate.solve_ivp)

• Parameter continuations and bifurcation analysis via PyAuto, an interface to auto-07p

• Storage of solutions in pandas.DataFrame

• Efficient parameter sweeps on single and multiple machines via the grid_search module

• Model optimization via genetic algorithms

• Visualization of results via seaborn

• Post-processing of simulation results via scipy and MNE Python

• The user has full control over the mathematical equations that nodes and edges are defined by.

• Model configuration and simulation can be done within a few lines of code.

• Various templates for rate-based population models are provided that can be used for neural network simulations imediatly.

Installation

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 ` Alternatively, it is possible to clone this repository and run the following line from the directory in which the repository was cloned: ` python setup.py install ` Finally, a singularity container of the most recent version of this software can be found [here](https://singularity.gwdg.de/containers/3). This container provides a stand-alone version of PyRates including all necessary Python tools to be run, independent of local operating systems. To be able to use this container, you need to install [Singularity](https://singularity.lbl.gov/) on your local machine first. Follow these [instructions](https://singularity.lbl.gov/quickstart) to install singularity and run scripts inside the PyRates container.

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.

Reference

If you use this framework, please cite: 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.

Contact

If you have questions, problems or suggestions regarding PyRates, please contact [Richard Gast](https://www.cbs.mpg.de/person/59190/376039) or [Daniel Rose](https://www.cbs.mpg.de/person/51141/374227).