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NeuronUnit

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Difficulty level
Intermediate
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Duration
17:21

This lecture covers NeuronUnit, a library that builds upon SciUnit and integrates with several existing neuroinformatics resources to support validating single-neuron models using data gathered by neurophysiologists.

Topics covered in this lesson

Workshop lecture at Neuroinformatics 2014 in Leiden, The Netherlands
Workshop title: Open collaboration in computational neuroscience
Talk title: NeuroElectro and NeuronUnit
Speaker: Richard Gerkin

Talk abstract

Rigorously validating a quantitative scientific model requires comparing its predictions against an unbiased selection of experimental observations according to sound statistical criteria. Developing new models thus requires a comprehensive and contemporary understanding of competing models, relevant data and statistical best practices. Today, developing such an understanding requires an encyclopedic knowledge of the literature. Unfortunately, in rapidly-growing fields like neuroscience, this is becoming increasingly untenable, even for the most conscientious scientists. For new scientists, this can be a significant barrier to entry.

Software engineers seeking to verify, validate and contribute to a complex software project rely not only on volumes of human documentation, but on suites of simple executable tests, called "unit tests''. Drawing inspiration from this practice, we have developed SciUnit, an easy-to-use framework for developing "model validation tests'' -- executable functions, here written in Python.  These tests generate and statistically validate predictions from a specified class of scientific models against one relevant empirical observation to produce a score indicating agreement between the model and the data.  Suites of such validation tests, collaboratively developed by a scientific community in common repositories, produce up-to-date statistical summaries of the state of the field.  Here we aim to detail this test-driven workflow and introduce it to the neuroscience community.  As an initial example, we describe NeuronUnit, a library that builds upon SciUnit and integrates with several existing neuroinformatics resources to support validating single-neuron models using data gathered by neurophysiologists.