Team develops new method for analyzing synaptic density

Electron micrograph of stained somatosensory cortex synapses that were identified using a machine-learning algorithm. Credit: Saket Navlakha and Alison L. Barth 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 subjects and more images than prior researchers have attempted. As the brain learns and responds to sensory stimuli, its neurons make connections with one another.…


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