PT Journal
AU Gast, R
   Rose, D
   Salomon, C
   Möller, H
   Weiskopf, N
   Knösche, T
TI PyRates - a Python framework for rate-based neural simulations
SO PLOS ONE / Public Library of Science
PD December
PY 2019
BP art. e0225900
VL 14, 2019
IS 12
PU PLOS
DI 10.1371/journal.pone.0225900
WP https://www.db-thueringen.de/receive/dbt_mods_00043284
LA en
SN 1932-6203
AB 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.
PI Lawrence, Kanada / San Francisco, Calif.
ER