Thursday, April 25, 2024
4/25/2024
At the end of 2020, the IARPA MICrONS program will conclude with an automated reconstruction of all neurons in a cubic millimeter of mouse visual cortex, along with the neurons’ synaptic connectivity and calcium-imaged responses to video stimuli. We believe that this dataset could become the most widely used resource in the field of cortical circuits, but more work is required to realize this potential. We propose to create an online community called Pyr (pronounced “peer”) that takes the result of MICrONS as its starting point. We propose to detect and correct the remaining errors in the automated segmentation through automated algorithms and human proofreading working in tandem. Automated error detection and correction will utilize hand-designed heuristics as well as deep learning and deep metric learning. Proofreading will be crowdsourced to nonscientist volunteers, a concept pioneered by Eyewire, but updated to handle a 1000x larger dataset and far more accurate artificial intelligence. We expect that Pyr will generate more cortical connections than any previous study, by orders of magnitude. This information will be accompanied by calcium-imaged visual responses of 80,000 neurons. With such an enormous and rich dataset, the primary bottleneck will become scientific discovery. We propose to crowdsource discovery by assembling a community of researchers who collaborate with Pyr and each other. Discovery infrastructure will include a “Science API” that allows programmatic access to the primary and derived data through Python functions, an automated system for “materializing” the connectome that makes the result of proofreading available to researchers with essentially zero delay, and a versioning system will insure that analyses are reproducible. We will create a framework enabling the community to contribute their own kinds of annotations, which will be included in materialization. Finally, we will build interactive discovery tools that couple Science API queries with visualization. We envision a community of researchers who will build upon the above discovery infrastructure, creating and sharing analysis tools of their own. They will investigate diverse questions about cortical structure and function, including many that we cannot foresee at all. In the future, Pyr could incorporate other cortical datasets as they become available, with much less engineering effort than the original MICrONS dataset. As with MICrONS, software will be developed in the open in public github repos. This software could be reused by others to build systems similar to Pyr.
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