In recent years, end-to-end learning algorithms have revolutionized many areas of research, such as computer vision 1, natural language processing 2, gaming 3, robotics, etc. Deep-learning techniques have achieved the highest levels of success in many of these tasks, given their astonishing capability to model both the features/filters and the classification rule.
The algorithms developed in this line of research will focus on enhancing deep-learning architectures and improving their learning capabilities, in terms of invariant (rotation, translation, warping, scaling) feature extraction 4, computational efficiency and parallelization 5, speeding up the network learning times 6,7 and connecting images to sequences.
These algorithms will be applied to real computer vision problems in the field of Neuroscience, in collaboration with the Princeton Neuroscience Institute. These range from detection and tracking of rodents in low resolution videos, image segmentation and limb detection, motion estimation of whiskers using high-speed cameras and in vivo calcium image segmentation of neural network activity in rodents 8.
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