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Monday Oct. 12, 2020, 5:30 p.m.–Oct. 12, 2020, 6 p.m. in Jupyter in Scientific Research

FlyBrainLab: Interactive Computing in the Connectomic/Synaptomic Era

Tingkai Liu, Aurel A. Lazar, Mehmet Kerem Turkcan, Yiyin Zhou

Audience level:

Brief Summary

FlyBrainLab is a complete programming environment that accelerates the discovery of functional logic of the Fruit Fly Brain by supporting interactive exploration of Fruit Fly Brain data across modality. Built upon JupyterLab, the NeuroMynerva user interface of FlyBrainLab enables a highly intuitive and automated workflow for efficient development and comparison of fly brain circuit models.


In recent years, a wealth of Drosophila neuroscience data has become available. These include cell type, connectome and synaptome datasets for both the larva and adult fly (https://tinyurl.com/yxtquutw), describing the diversity and interconnects between the fundamental building blocks of fruit fly brains. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we developed an interactive computing environment called FlyBrainLab. FlyBrainLab is a full-stack application that is uniquely positioned towards accelerating the discovery of the functional logic of the Drosophila brain. Its interactive open source architecture seamlessly integrates and brings together computational models with neuroanatomical, neurogenetic and electrophysiological data, changing the organization of neuroscientific fly brain data from a group of unconnected databases, arrays and tables, to a well-structured data and executable circuit repository.

In this talk, we highlight the FlyBrainLab User Interface - NeuroMynerva. Built upon the latest JupyterLab platform, NeuroMynerva supports a highly intuitive and automated workflow that streamlines the 3D exploration and visualization of fly brain circuits, and the interactive exploration of the functional logic of executable circuits created directly from the explored and visualized fly brain data.

Furthermore, the natural integration with Jupyter Notebooks addresses a common issue in Computational Neuroscience research - reproducibility. By developing data interactivity around Notebooks, NeuroMynerva (and FlyBrainLab) supports the efficient comparison of fly brain circuit models, across model instances developed by different researchers, across different developmental stages of the fruit fly and across different datasets.

Finally, NeuroMynerva shares JupyterLab’s philosophy with extensibility and supports community contributions of custom extension that incorporate additional data types into the FlyBrainLab ecosystem.

  1. Website http://fbl.fruitflybrain.org/
  2. FlyBrainLab Source Code: https://github.com/FlyBrainLab/FlyBrainLab
  3. NeuroMynerva Source Code: https://github.com/FlyBrainLab/NeuroMynerva
  4. FlyBrainLab Publication: https://www.biorxiv.org/content/10.1101/2020.06.23.168161v1