Description: Unit tests are ubiquitous in software development (Google alone has 4.7 million running all the time!), but surprisingly sparse in science. The SciUnit (http://sciun.it) framework was developed to help researchers create unit tests for scientific models. SciUnit tests ask how well a scientific model does what it claims to do, by testing its predictions against specific experimental results. Performance on these tests is thus one measure of whether the model is a good (or at least useful) description of reality. SciUnit is being used in large projects in neuroscience, including in the Human Brain Project and OpenWorm, as well as in individual labs.
Aims: These unit tests can be used not only to assess developed models, but also to guide model development, including the tuning of model parameters to reproduce experimental data. Several challenges arise in multi-objective optimization of models against data, including parallelization, visualization, and reproducibility. We aim to solve some of these challenges in the optimization of models of neurons and neural circuits. In particular, we want to refine and deploy reproducible optimization workflows for models against diverse sets of neurophysiology data, and then share and compare these optimized models for subsequent use in larger research questions.
Tags: Python, Git, Jupyter, Numpy