PyRates

PyRates provides a Python framework for constructing and simulating rate-based neural models to study population dynamics and brain states.

Key Features:

• Generic Model Definition: Enables flexible specification of rate-based neural models and modification of mathematical operators within the model's graph structure.
• Efficient Numerical Simulations: Translates models into compute graphs to enable parallelized, efficient numerical simulation of network dynamics.
• Scalability and Computational Capacity: Supports simulation of large-scale neural networks and demonstrates robust scalability in benchmark simulations.
• Consistency with Established Models: Model implementations have been validated against existing literature to ensure simulation behavior aligns with known results.

Scientific Applications:

• Brain-state and population dynamics: Use rate-based population models to explore brain states and neural population dynamics.
• Neural connectivity and network behavior: Investigate connectivity patterns and resulting network behavior in rate-based networks.
• Dynamic responses and multi-scale studies: Analyze dynamic responses of networks under varying conditions and study brain dynamics across scales.

Methodology:

PyRates translates user-defined rate-based models into compute graph representations to optimize computational efficiency and enable parallelized simulations.