Explore a 52-minute lecture on feedforward and feedback processes in visual recognition presented by Thomas Serre from Brown University's Cognitive, Linguistic & Psychological Sciences Department and Carney Institute for Brain Science. Delve into the limitations of convolutional neural networks in visual reasoning tasks and discover a novel recurrent network model inspired by the visual cortex. Learn how this computational neuroscience model addresses shortcomings in state-of-the-art feedforward networks for complex visual reasoning. Examine topics such as computer vision achievements, adversarial attacks, ImageNet, computational neuroscience, and the potential contributions of neuroscience to artificial intelligence. Gain insights into the depth of processing, experimental data, and the benefits of this approach through discussions on semantics, cluttered ABC results, and proof of concept.