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* Add new project types * Update links * First draft of electrophysiology page. Please review * Update collaborations.md Please review, I think this is okay maybe I can run it by Pierre. * Update docs/source/projects/developer_tools.md Co-authored-by: Chang Huan Lo <[email protected]> * Update docs/source/projects/electrophysiology.md Co-authored-by: Chang Huan Lo <[email protected]> * Update neuroblueprint link to website * Update docs/source/projects/electrophysiology.md Co-authored-by: Chang Huan Lo <[email protected]> * Update docs/source/projects/electrophysiology.md Co-authored-by: Chang Huan Lo <[email protected]> * Added card images for data management and behaviour * same backgrounds for all cards * added collaborations card * added ephys card * Added movement text * Standardise headings * Remove collaborations for now * edited movement text --------- Co-authored-by: Joe Ziminski <[email protected]> Co-authored-by: Chang Huan Lo <[email protected]> Co-authored-by: niksirbi <[email protected]>
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# Behaviour | ||
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## movement | ||
[movement](https://movement.neuroinformatics.dev/) aims to **facilitate the study of animal behaviour in neuroscience** by providing a suite of **Python tools to analyse body movements** across space and time. | ||
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At its core, movement handles trajectories of *keypoints*, which are specific body parts of an *individual*. An individual's posture or *pose* is represented by a set of keypoint coordinates, given in 2D (x,y) or 3D (x,y,z). The sequential collection of poses over time forms *pose tracks*. In neuroscience, these tracks are typically extracted from video data using software like [DeepLabCut](http://www.mackenziemathislab.org/deeplabcut) or [SLEAP](https://sleap.ai/). | ||
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With movement, our vision is to present a consistent interface for pose tracks and to analyse them using modular and accessible tools. We aim to accommodate data from a range of pose estimation packages, in 2D or 3D, tracking a single or multiple individuals. The focus will be on providing functionalities for data cleaning, visualisation and motion quantification. | ||
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While movement isn't designed for behaviour classification or action segmentation, it may extract features useful for these tasks. We are planning to develop separate packages for this purpose, which will be compatible with movement and the existing ecosystem of related tools. |
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# Collaborations | ||
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## UNLE | ||
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We have collaborated with Gatsby researcher Pierre Glasser in the productionasation of the UNLE package. UNLE (unnormalised neural likelihood estimation) provide two new methods for simulation-based inference (SBI), a form of Bayesian inference on the outputs of high-fiedelity simulators of natural phenomenon. |
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# Electrophysiology | ||
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## spikewrap | ||
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[Spikewrap](https://github.com/neuroinformatics-unit/spikewrap) is a tool to simplify the execution and visualisation of extracellular electrophysiology pre-processing and spike-sorting pipelines. Taking input organised to our [NeuroBlueprint](https://neuroblueprint.neuroinformatics.dev) standard, it will provide a flexible way to process an entire project's electrophysiological data with an easy-to-use interface. | ||
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Under the hood Spikewrap utilises the power of [SpikeInterface](https://spikeinterface.readthedocs.io/en/latest/), an open-source tool with many contributors in the electrophysiology analysis field. Spikewrap aims to abstract away the implementation details to allow researchers to test different pipeline configurations with the click of a button. | ||
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We actively contribute to the development of SpikeInterface, ensuring that our efforts benefit existing community projects. |
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