Janelia FlyEM and Google produced the largest dense connectome ever consisting of around 25,000 neurons and 20 million synaptic connections in the central fly brain. In this talk, I will first highlight the various methods that made this reconstruction possible and then discuss the feasibility and economics of future connectomic efforts. This large connectome poses many challenges for data interpretation. The second part of this talk will discuss various considerations for using this data for different types of biological questions. To simplify data analysis, our team introduces compact data representations and many tools for navigating the dataset.
Dense mapping of neural circuits at synaptic level of detail currently requires tracing neurons within volume EM datasets. When done manually, this process is laborious, error-prone, and hard to scale up to keep pace with the increasing size and number of volumes available for study. Automated methods to perform the tracing are therefore necessary. Within the Connectomics at Google team, we developed a segmentation technique called Flood-Filling Networks (FFNs) based on a recurrent convolutional neural network, which has established a new state of the art for segmentation of blockface volume EM data.
I will discuss how FFNs work, and how we used them together with machine learning-based image normalization methods to segment a 20 TB FIB-SEM volume of roughly half of a drosophila brain acquired by the FlyEM team at Janelia. This fully automated reconstruction formed the basis of an extensive proofreading effort organized by FlyEM, which culminated in the recent release of the hemibrain connectome -- currently the largest synapse-resolution map of brain connectivity ever produced in any species.