Using Machine Learning to Observe How the Brain Adapts to Different Environments

Johns Hopkins scientists have used machine learning to analyze how the brain adapts to different environments. Their method helped to visualize and track changes in the strength of synapses, which are connection points through which nerve cells in the brain communicate. The use of machine learning enhanced the quality of images composed of thousands of synapses, which are difficult to visualize with standard microscopes. The researchers used genetically altered mice with green-glowing chemical sensors at synapses. They captured baseline images and then exposed the mice to a chamber with new sights, smells and tactile stimulation for a single five-minute period, before imaging the same area every other day over several weeks to track changes in synapse strength in response to the new stimuli.