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High-throughput, Machine-Learning Tool Could Help Researchers Better Understand Synaptic Activity in Learning and Disease

Alison Barth, professor of biological sciences and interim director of Carnegie Mellon’s BrainHub neuroscience initiative along with researchers Saket Navlakha, formerly of Carnegie Mellon and now at the Salk Institute for Biological Studies; Nicholas J. Audette, Dylan D. McCreary, and Joe Suhan of Carnegie Mellon’s Department of Biological Sciences and the CNBC; and Ziv Bar-Joseph of Carnegie Mellon’s Machine Learning Department and Lane Center for Computational Cancer Research have developed a new approach to broadly survey learning-related changes in synapse properties.  In a study published in the Journal of Neuroscience and featured on the journal’s cover, the researchers used machine-learning algorithms to analyze thousands of images from the cerebral cortex. This allowed them to identify synapses from an entire cortical region, revealing unanticipated information about how synaptic properties change during development and learning.  The study is one of the largest electron microscopy studies ever carried out, evaluating more subjects and more images than prior researchers have attempted. Read more.