The complex network that forms the human brain allows us to recognize thousands of objects, actions and scenes in a few milliseconds with almost no effort. The vision problem can be considered solved in the biological brain, but we are far from achieving satisfying solutions in computational systems. In this topic we propose to develop computational algorithms inspired in the human brain, or more specifically, in the ventral visual processing stream.Previous research has shown that a specifically tuned set of bank of filters can mimic the V1 IT cortex (inferior temporal cortex) . Nevertheless, the "untangle" process that obtains meaningful information from "pixel" images in the retina remains unknown. We will propose deep learning architectures  to obtain robust computational algorithms applied to visual tasks in the machine.
The algorithms developed will be applied to real computer vision problems applied to Neuroscience, in the Princeton Neuroscience institute, and range from detection and tracking of rodents in low resolution videos, image segmentation and limb detection, and motion estimation of whiskers using high speed cameras.
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