PyRates—A Python framework for rate-based neural simulations

Autoři: Richard Gast aff001;  Daniel Rose aff003;  Christoph Salomon aff001;  Harald E. Möller aff002;  Nikolaus Weiskopf aff003;  Thomas R. Knösche aff001
Působiště autorů: MEG and Cortical Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany aff001;  Nuclear Magnetic Resonance Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany aff002;  Neurophysics Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany aff003;  Institute for Biomedical Engineering and Informatics, TU Ilmenau, Ilmenau, Thuringia, Germany aff004
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225900


In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of rate-based neural models. PyRates provides intuitive access to and modification of all mathematical operators in a graph, thus allowing for a highly generic model definition. For computational efficiency and parallelization, the model is translated into a compute graph. Using the example of two different neural models belonging to the family of rate-based population models, we explain the mathematical formalism, software structure and user interfaces of PyRates. We show via numerical simulations that the behavior of the PyRates model implementations is consistent with the literature. Finally, we demonstrate the computational capacities and scalability of PyRates via a number of benchmark simulations of neural networks differing in size and connectivity.

Klíčová slova:

Data visualization – Electroencephalography – Membrane potential – Network analysis – Neural networks – Neurons – Simulation and modeling – Synapses


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2019 Číslo 12