Our lab is interested in the mechanisms that underlie synaptic competition between neurons that innervate the same target cell. Such competitive interactions are responsible for sharpening the patterns of neural connections during development and may also be important in learning and memory formation. The Lichtman laboratory studies synaptic competition by visualizing synaptic rearrangements directly in living animals using modern optical imaging techniques. We have concentrated on neuromuscular junctions in a very accessible neck muscle in mice where new transgenic animals and other labeling strategies allow individual nerve terminals and postsynaptic specializations to be monitored over hours or months.
Our research focuses on studying the development of the cerebellum in mice across five key developmental stages: P0, P3, P7, P10, and P14.
My role in this project focused on applying machine learning techniques (U-Net) to slice electron microscopy (EM) images of cerebellar tissue.
Reconstructing neural circuits is a crucial technique in connectomics research, mainly accomplished through near-nanometer resolution
electron microscopy (EM) imaging of ultrathin sections of brain tissue. In this project, my responsibility is to stitch together the image tiles of interest collected from EM into a complete 2D image and then align the 2D images to achieve a 3D reconstruction
In addition, I also perform data analysis on EM images captured at various developmental stages. My analysis involves categorizing axons into three distinct states: T junctions, typical axons, and early-ending axons. This detailed examination of axon states contributes to our comprehensive understanding of synaptic competition dynamics."
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