000K  utf8
0100  1686684576
0500  Ou
1100  2019$c2019-12-16
1500  eng
2051  10.1371/journal.pone.0225900
2240  
3000  Gast, Richard
3010  Knösche, Thomas R.
3010  Möller, Harald E.
3010  Rose, Daniel
3010  Salomon, Christoph
3010  Weiskopf, Nikolaus
4000  PyRates - a Python framework for rate-based neural simulations  [Gast, Richard]
4060  26 Seiten
4209  In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of rate-based neural models. PyRates provides intuitive access to and modification of all mathematical operators in a graph, thus allowing for a highly generic model definition. For computational efficiency and parallelization, the model is translated into a compute graph. Using the example of two different neural models belonging to the family of ratebased population models, we explain the mathematical formalism, software structure and user interfaces of PyRates. We show via numerical simulations that the behavior of the PyRates model implementations is consistent with the literature. Finally, we demonstrate the computational capacities and scalability of PyRates via a number of benchmark simulations of neural networks differing in size and connectivity.
4950  https://doi.org/10.1371/journal.pone.0225900$xR$3Volltext$534
4961  http://uri.gbv.de/document/gvk:ppn:1686684576
5051  500
5051  610