https://www.db-thueringen.de/receive/dbt_mods_00043284https://www.db-thueringen.de/servlets/MCRFileNodeServlet/dbt_derivate_00048540/1932-6203_14_2019_12_e0225900.pdfhttps://www.db-thueringen.de/rsc/thumbnail/dbt_mods_00043284.png PyRates - a Python framework for rate-based neural simulations Gast, Richard Gast Richard VerfasserIn aut Author Rose, Daniel Rose Daniel VerfasserIn aut Author Salomon, Christoph Salomon Christoph VerfasserIn aut Author Möller, Harald E. Möller Harald E. VerfasserIn aut Author Weiskopf, Nikolaus Weiskopf Nikolaus VerfasserIn aut Author article Knösche, Thomas R. Knösche Thomas R. VerfasserIn aut Author text Text 2019-12-16 en electronic resource remote Computermedien Online-Ressource 26 Seiten 14 (2019), 12, e0225900, Seite 1-26 14, 2019 12 art. e0225900 text continuingjournal PLOS ONE / Public Library of Science Public Library of Science pbl Publisher 10067182-2 journal Lawrence, Kanada / San Francisco, Calif. 2006 PLOS en 500 610 1932-6203 http://uri.gbv.de/document/gvk:ppn:523574592 22676703 2017-04-06 10.1371/journal.pone.0225900 rda DE-627 200108 2020-01-08T13:01:36 1686684576 Converted from MARCXML to MODS version 3.6 using MARC21slim2MODS3-6.xsl (Revision 1.117gbv 2017/02/14) de http://uri.gbv.de/document/gvk:ppn:1686684576 article 2020-05-26 500 610 his Host institution 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 ratebased 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. dbt_mods_00043284