Recent advances in calcium imaging acquisition techniques are creating data sets of the order of Terabytes/week. Memory and computationally efficient algorithms are required to analyse in reasonable amount of time terabytes of data.
This projects implements a set of essential methods required in the calcium imaging movies analysis pipeline. Fast and scalable algorithms are implemented for motion correction, movie🍿 manipulation and source and spike extraction. CaImAn also contains some routine to the analysis of behaviour from video cameras📷 . In summary, CaImAn provides a general purpose tool to handle large movies, with special emphasis tools🧰 for calcium imaging and behavioural data sets.
You can find more information about this project and how to use it on the Github repository.
the comparison of human vs software labelling
This project led by Eftychios Pnevmatikakis and Andrea Giovannucci (see my recommandation letter) was the one I worked on during my internship in New York City at the Simons foundation during the summer 2017. I worked on the comparison project, the Graphical User interface, the documentation and many more topics.
I think that nothing can describe better what this mission was about than my internship report.
the GUI of CaImAn in Matlab
CaImAn has meant a lot to me and I am very proud to announce that our paper has been submitted, accepted and published on eLife very recently.
A further explanation of the performance of the project.