2020-07-29 –, Red Track
We study the larval zebrafish, a vertebrate whose whole brain can be imaged during behavior. In this system, we can observe neural activity underlying computations in visual processing, decision making and adaptive motor control. To analyze terabyte-size imaging data and test a variety of hypotheses about the neural processes, we are using a Julia-based pipeline that takes full advantage of the parallelism, multiple dispatch and flexible package ecosystem of the language.
For analysing imaging and behavioral data I am developing two packages:
Calcium.jl - for extraction of lower-dimensional neuronal signals from dense, volumentric imaging data. It provides non-negative matrix factorization or local correlation-based methods as well as visualization, pre- and postprocessing tools.
Fishyfits.jl - defining interfaces and providing methods for simple models that can be fit and compared across behavioral data and neural activity, building on Julia's extensive model fitting and optimization toolkit.
I will discuss some challanges on developing codebases that have to interoperate with Python pipelines, as Julia is not yet widespread in our lab or the wider neuroscience community.
Finally, I will showcase examples from our publications on using Binder for sharing reproducible analyses.
I am a PhD student in systems neuroscience at the Portugues Lab at the Max Planck Institute of Neurobiology and Technical University of Munich studying sensorimotor control in larval zebrafish using VR and whole-brain imaging.