PyRates—A Python framework for rate-based neural simulations

• Published in: PloS one Vol. 14; no. 12; p. e0225900
• Main Authors: Gast, Richard, Rose, Daniel, Salomon, Christoph, Möller, Harald E., Weiskopf, Nikolaus, Knösche, Thomas R.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.12.2019; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial neural networks; Benchmarking; Biology and Life Sciences; Biomedical engineering; Blood; Brain - physiology; Brain Mapping; Computational neuroscience; Computer and Information Sciences; Computer programs; Computer simulation; Connectome; Data visualization; Electroencephalography; Formalism (Literary criticism); Humans; Informatics; Interfaces; Magnetic Resonance Imaging; Mathematical models; Medical imaging; Medicine and Health Sciences; Models, Neurological; Models, Theoretical; Nervous system; Neural networks; Neurons; Neurosciences; Neurosciences - methods; NMR; Nuclear magnetic resonance; Numerical analysis; Numerical simulations; Operators (mathematics); Parallel processing; Research and Analysis Methods; Software; User interfaces; United States > US; Germany
• ISSN: 1932-6203; 1932-6203
• 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.