Enhanced AI tracks neurons in moving animals

EPFL and Harvard scientists have developed a pioneering new method that uses deep learning and a new technique called 'targeted augmentation' to track neurons in moving and deforming animals. Recent advances allow imaging of neurons inside freely moving animals. However, to decode circuit activity, these imaged neurons must be computationally identified and tracked. This becomes particularly challenging when the brain itself moves and deforms inside an organism's flexible body, e.g. in a worm. Until now, the scientific community has lacked the tools to address the problem. Now, a team of scientists from EPFL and Harvard have developed a pioneering AI method to track neurons inside moving and deforming animals. The study, now published in Nature Methods , was led by Sahand Jamal Rahi at EPFL's School of Basic Sciences.
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