A SciUnit library for data-driven testing of basal ganglia models. Employed for testing via the HBP Validation Framework. This test shall take as input a BluePyOpt optimized output file, containing a hall_of_fame.json file specifying a collection of parameter sets. The validation test would then evaluate the model for all (or specified) parameter sets against various eFEL features.
NSuite is a framework for maintaining and running benchmarks and validation tests for multi-compartment neural network simulations on HPC systems. NSuite automates the process of building simulation engines, and running benchmarks and validation tests. NSuite is specifically designed to allow easy deployment on HPC systems in testing workflows, such as benchmark-driven development or continuous integration.
There are three motivations for the development of NSuite:
- The need for a definitive resource for comparing performance and correctness of simulation engines on HPC systems.
- The need to verify the performance and correctness of individual simulation engines as they change over time.
- The need to test that changes to an HPC system do not cause performance or correctness regressions in simulation engines.
The framework currently supports the simulation engines Arbor, NEURON, and CoreNeuron, while allowing other simulation engines to be added.
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The pipeline ingests data from multiple measurement types of spatially organized neuronal activity, such as ECoG or calcium imaging recordings. The pipeline returns statistical measures to quantify the dynamic wave-like activity patterns found in the data. Individual parts of the snakemake-based pipeline are fully configurable. The composition of Cobrawap elements can be adapted to various datasets through by means of a modular design of self-contained sequential stages composed of multiple atomic blocks.
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