GSoC NIU Projects 2025: spikewrap#
If you are interested in any of these spikewrap projects, get in touch! Feel free to open a new topic on our Zulip GSoC channel and ask the community.
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Add motion correction preprocessing
Extracellular electrophysiology is a technique used to record the activity of thousands of neurons in the brain simultaneously. Understanding how this neural activity drives behavior requires reliably identifying and distinguishing individual neurons based on their electrophysiological signatures—a process known as spike sorting.
spikewrap is a Python package designed to streamline electrophysiology preprocessing and spike sorting across experimental projects. It leverages SpikeInterface, a popular package that exposes many electrophysiology processing tools. The aim of spikewrap is to abstract away implementation details and make running electrophysiological analysis as simple as possible.
This project involves adding ‘motion correction’ preprocessing to spikewrap.
Deliverables
Add SpikeInterface motion correction functionality to spikewrap
Test motion correction functions.
Document the new functionality
Duration
Large (~350 hours)
Difficulty
In terms of coding requirements, a beginner or intermediate developer would be well suited. However, the project will require working closely with extracellular electrophysiological data, which can be quite complex. Therefore, domain knowledge of extracellular electrophysiology would be an advantage.
Required skills Experience with Python and running neuroscience (preferably electrophysiology) experiments.
Nice-to-haves Experience with extracellular electrophysiology.
Potential mentors
Further reading
spikewrap and SpikeInterface documentation.
Extend spikewrap test functionality
spikewrap is a young project in the prototyping phase. Testing the package is not straightforward and requires access to GPU hardware and SLURM scheduling software on a high-performance compute (HPC) cluster. Currently, test suite is currently lagging development.
This project will involve developing a comprehensive test suite for running experimental pipelines in spikewrap. This will involve running all possible options the software supports on an HPC system, leveraging GPU, SLURM and singularity image functionality.
Duration
Large (~350 hours)
Difficulty
This project is well-suited for a beginner to intermediate level developer, in particular with an interest
in testing infrastructure. Less domain knowledge is required for this project compared to Add motion correction preprocessing.
Required skills Experience with Python
Potential mentors
Further reading
spikewrap and SpikeInterface documentation.