FlyBrainLab: A Complete Programming Environment for Discovering the Functional Logic of the Fruit Fly Brain
Presenting author:
Recent technological advances have given rise to massive neuromics datasets (connectomic, synaptomic, transciptomic & neurophysiology), providing a complex, but fragmented foundation for brain research. To address the challenges posed by these datasets of unprecedented scale and granularity, we developed FlyBrainLab, a complete programming environment for discovering of the functional logic of the brain.
FlyBrainLab provides a user interface supporting visualization of brain circuits and creation of circuit diagrams that can be immediately compiled for program execution. Such capabilities facilitate the construction, comparison and validation of the plethora of emerging executable brain circuit models of the Drosophila nervous system. Additionally, FlyBrainLab integrates and standardizes four large connectomics datasets for adult and larval flies into a graph database (NeuroArch), and provides the capability to explore, analyze and share circuits.
The FlyBrainLab UI is tightly integrated into JupyterLab, thereby addressing a common shortcoming of computational neuroscience research: reproducibility. FlyBrainLab enables comparisons between models developed by different researchers, across different developmental stages of the fruit fly or using different datasets. We demonstrate these capabilities by comparing models of the central complex that highlight the differences in the components each model derives from the data, the assumptions made, and the responses of the models.
FlyBrainLab provides a user interface supporting visualization of brain circuits and creation of circuit diagrams that can be immediately compiled for program execution. Such capabilities facilitate the construction, comparison and validation of the plethora of emerging executable brain circuit models of the Drosophila nervous system. Additionally, FlyBrainLab integrates and standardizes four large connectomics datasets for adult and larval flies into a graph database (NeuroArch), and provides the capability to explore, analyze and share circuits.
The FlyBrainLab UI is tightly integrated into JupyterLab, thereby addressing a common shortcoming of computational neuroscience research: reproducibility. FlyBrainLab enables comparisons between models developed by different researchers, across different developmental stages of the fruit fly or using different datasets. We demonstrate these capabilities by comparing models of the central complex that highlight the differences in the components each model derives from the data, the assumptions made, and the responses of the models.