Carnegie Mellon develops new method for analyzing synaptic density

IMAGE: This image shows synapses in the somatosensory cortex stained with ethanolic phosphotungstic acid and visualized using electron microscopy. Synapses were identified using a Carnegie Mellon-developed machine learning algorithm that enables… view more Credit: Image credit: Saket Navlakha and Alison Barth; the Journal of Neuroscience. PITTSBURGH – Carnegie Mellon University researchers 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…


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