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Add spikewrap projects to GSoC (#132)
* Add spikewrap * Update index. * Small fixes,. * Add to toctree.
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‎docs/source/get-involved/gsoc/index.md‎

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[`datashuttle`](https://datashuttle.neuroinformatics.dev/index.html) is a tool to automate neuroscience project folder creation, validation and transfer. It creates and validates projects standardised to the [NeuroBlueprint](https://neuroblueprint.neuroinformatics.dev/latest/index.html) specification. It also allows these folders to be easily synchronised between computers.
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:::{grid-item-card} {fas}`database;sd-text-primary` `spikewrap`
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:link: projects_2025/spikewrap
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:link-type: doc
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[`spikewrap`](https://spikewrap.neuroinformatics.dev/index.html) is a package for managing extracellular electrophysiology analysis.
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## Apply to GSoC with NIU

‎docs/source/get-involved/gsoc/projects_2025/index.md‎

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ethology
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brainglobe
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datashuttle
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spikewrap
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‎docs/source/get-involved/gsoc/projects_2025/spikewrap.md‎

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# GSoC NIU Projects 2025: `spikewrap`
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If you are interested in any of these [spikewrap](https://github.com/neuroinformatics-unit/spikewrap) projects,
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[get in touch](https://spikewrap.neuroinformatics.dev/community/index.html)!
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Feel free to open a new topic on our [Zulip GSoC channel](https://neuroinformatics.zulipchat.com/#narrow/channel/487898-GSoC) and ask the community.
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Our working language is English.
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<!-- ------------------------------ -->
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:::{dropdown} {fas}`database;sd-text-primary` Add motion correction preprocessing
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Extracellular electrophysiology is a technique used to record the activity of thousands of neurons in the brain simultaneously.
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Understanding how this neural activity drives behavior requires reliably identifying and distinguishing individual neurons
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based on their electrophysiological signatures—a process known as spike sorting.
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[spikewrap](https://spikewrap.neuroinformatics.dev/) is a Python package designed to streamline electrophysiology
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preprocessing and spike sorting across experimental projects. It leverages
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[SpikeInterface](https://spikeinterface.readthedocs.io/en/latest/index.html), a popular package that exposes many electrophysiology processing tools.
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The aim of spikewrap is to abstract away implementation details and make running electrophysiological analysis as simple as possible.
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This project involves adding 'motion correction' preprocessing to spikewrap.
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**Deliverables**
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<!-- Goals, or expected status after Community Bonding Period, Start of Coding, End of Coding. Stretch goals? -->
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- Add SpikeInterface [motion correction](https://spikeinterface.readthedocs.io/en/latest/modules/motion_correction.html) functionality to spikewrap
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- Test motion correction functions.
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- Document the new functionality
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**Duration**
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<!-- Small (~90 hours), Medium (~175 hours) or Large (~350 hours) -->
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Large (~350 hours)
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**Difficulty**
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<!-- Is this project geared more toward a student level or a more advanced developer level? -->
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In terms of coding requirements, a beginner or intermediate developer would be well suited.
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However, the project will require working closely with extracellular electrophysiological data,
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which can be quite complex. Therefore, domain knowledge of extracellular electrophysiology would be an advantage.
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**Required skills**
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Experience with Python and running neuroscience (preferably electrophysiology) experiments.
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**Nice-to-haves**
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Experience with extracellular electrophysiology.
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**Potential mentors**
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- [@JoeZiminski](https://github.com/JoeZiminski)
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**Further reading**
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<!-- The best pages include links to more detailed descriptions and related materials for each project. They might even include actual use cases! -->
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[spikewrap](https://spikewrap.neuroinformatics.dev/) and [SpikeInterface](https://spikeinterface.readthedocs.io/en/latest/index.html) documentation.
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<!-- ------------------------------ -->
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:::{dropdown} {fas}`database;sd-text-primary` Extend spikewrap test functionality
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[spikewrap](https://spikewrap.neuroinformatics.dev/) is a young project in the prototyping phase.
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Testing the package is not straightforward and requires access to GPU hardware and SLURM scheduling software
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on a high-performance compute (HPC) cluster. Currently, test suite is currently lagging development.
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This project will involve developing a comprehensive test suite for running experimental pipelines in spikewrap.
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This will involve running all possible options the software supports on an HPC system, leveraging GPU, SLURM and
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singularity image functionality.
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- **Duration**
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<!-- Small (~90 hours), Medium (~175 hours) or Large (~350 hours) -->
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Large (~350 hours)
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**Difficulty**
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<!-- Is this project geared more toward a student level or a more advanced developer level? -->
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This project is well-suited for a beginner to intermediate level developer, in particular with an interest
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in testing infrastructure. Less domain knowledge is required for this project compared to `Add motion correction preprocessing`.
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**Required skills**
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Experience with Python
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**Potential mentors**
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- [@JoeZiminski](https://github.com/JoeZiminski)
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**Further reading**
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<!-- The best pages include links to more detailed descriptions and related materials for each project. They might even include actual use cases! -->
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[spikewrap](https://spikewrap.neuroinformatics.dev/) and [SpikeInterface](https://spikeinterface.readthedocs.io/en/latest/index.html) documentation.
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