ArticlePloS one2019
PyRates-A Python framework for rate-based neural simulations.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Neural population models for EEG: From Canonical models to alternative model structures.PLoS computational biology · 2026Article
- TVB C++: A Fast and Flexible Back-End for The Virtual Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Scikit-NeuroMSI: A Generalized Framework for Modeling Multisensory Integration.Neuroinformatics · 2025Article
- Joint contribution of adaptation and neuronal population recruitment to response level in visual area MT: a computational model.Scientific reports · 2025Article
- A comprehensive investigation of intracortical and corticothalamic models of the alpha rhythm.PLoS computational biology · 2025Article
- PyRates-A code-generation tool for modeling dynamical systems in biology and beyond.PLoS computational biology · 2023Article
- RateML: A Code Generation Tool for Brain Network Models.Frontiers in network physiology · 2022Article
- NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models.Frontiers in neuroinformatics · 2022Article
- On the Role of Arkypallidal and Prototypical Neurons for Phase Transitions in the External Pallidum.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.