Neural Network Modeling Framework

Project description

# 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 large networks that can be efficiently parallelized on GPUs/CPUs.

• 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.

• 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.

• Visualization and data analysis tools are provided.

• Tools for fast and parallelized exploration of model parameter spaces are provided.

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

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., Daniel, R., Moeller, H. E., Weiskopf, N. and Knoesche, T. R. (2019). “PyRates – A Python Framework for rate-based neural Simulations.” bioRxiv (https://www.biorxiv.org/content/10.1101/608067v2).

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