Brian 2 allows scientists to simply and efficiently simulate spiking neural network models. These models can feature novel dynamical equations, their interactions with the environment, and experimental protocols. To preserve high performance when defining new models, most simulators offer two options: low-level programming or description languages. The first option requires expertise, is prone to errors, and is problematic for reproducibility. The second option cannot describe all aspects of a computational experiment, such as the potentially complex logic of a stimulation protocol. Brian addresses these issues using runtime code generation. Scientists write code with simple and concise high-level descriptions, and Brian transforms them into efficient low-level code that can run interleaved with their code. We illustrate this with several challenging examples: a plastic model of the pyloric network, a closed-loop sensorimotor model, a programmatic exploration of a neuron model, and an auditory model with real-time input.
- Romain Brette
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Frances K Skinner, Krembil Research Institute, University Health Network, Canada
- Received: April 1, 2019
- Accepted: August 19, 2019
- Accepted Manuscript published: August 20, 2019 (version 1)
© 2019, Stimberg et al.
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