MIA: Alyssa Wilson, Contrastive deep learning for electron microscopy connectomics data (2021)

Models, Inference and Algorithms Broad Institute of MIT and Harvard October 13, 2021 Alyssa Wilson Seung Lab, Princeton Neuroscience Institute, Princeton University Scalable analysis of electron microscopy connectomics data: Revealing neural circuit properties using contrastive deep learning The nanometer-resolution, 3D image volumes of brain tissue reconstructed using high-throughput electron microscopy (EM) are beginning to generate important insights about how neural circuitry is built, and about how that structure relates to neural function. This technique is unique in that it simultaneously resolves all cells in a tissue, their organelles, and their connections (chemical synapses), which makes it possible to build a very detailed picture of patterns in neuronal connectivity. However, even in small EM image volumes there can be hundreds to millions of any single type of biological object, so that it is impossible for humans to assess all examples and exhaustively define and