Until now, the scientific community has lacked the tools to address the problem. This becomes particularly challenging when the brain itself moves and deforms inside an organism’s flexible body, e.g. However, to decode circuit activity, these imaged neurons must be computationally identified and tracked. Recent advances allow imaging of neurons inside freely moving animals. This significantly reduces manual annotation, accelerating brain imaging research and deepening our understanding of neural behaviors.ĮPFL and Harvard scientists develop an AI-based method for tracking neurons in moving animals, enhancing brain research efficiency with minimal manual annotation. A groundbreaking AI method created by EPFL and Harvard scientists allows for efficient tracking of neurons in moving animals, using a convolutional neural network with ‘targeted augmentation’.
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