Novel Technique May Lead to Insights on Synaptic Activity in Learning and Disease

Scientists at Carnegie Mellon University reportedly have developed a new approach to broadly survey learning-related changes in synapse properties. In a study (“Unbiased, High-Throughput Electron Microscopy Analysis of Experience-Dependent Synaptic Changes in the Neocortex”) published in the Journal of Neuroscience, 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. As the brain learns and responds to sensory stimuli, its neurons make connections with one another. These synapses facilitate neuronal communication, and their anatomic and electrophysiological properties contain information vital to understanding how the brain behaves in health and disease. Investigators use different techniques, including electron microscopy, to identify and analyze synapse properties.…


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