![]() ![]() In 2019, Ramdya’s group introduced DeepFl圓D, another deep learning-based software that uses multiple cameras to quantify the movements of a fruit fly in 3D space. “Each camera acquired a single image of an animal, and multiple images across different cameras could then be triangulated to calculate 3-dimensional positions or poses.” But this triangulation of images requires multiple, synchronized cameras and elaborate calibration protocols, making it hard to adopt for neuroscientific studies of small animals. “In the past, we used a deep neural network to perform this kind of ‘pose estimation’ in animals,” says Ramdya, referring to the process by which a computer can predict the positions of body parts in camera images. This goal of reverse-engineering biological behavior has far-reaching applications in robotics and AI. ![]() ![]() This tool allows them to study the brain mechanisms controlling body movements. His group has now published a paper in Nature Methods presenting new software that can simplify one of neuroscience’s most crucial yet laborious tasks: capturing 3D models of freely moving animals. “When people perform experiments in neuroscience they have to make precise measurements of behavior,” says Professor Pavan Ramdya at EPFL’s School of Life Sciences, who led the study.
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