Neural computations for active perception
The human visual system does not passively view the world, but actively moves its sensor array through eye, head and body movements. How do neural circuits in the brain control and exploit these movements in order to build a
scene representation that can guide useful behavior? Here we focus on three aspects of this problem: 1) how do we see in the presence of fixational eye movements? 2) what is the optimal spatial layout of the image sampling array for a visual system that must search via eye movements? and 3) how is information integrated across multiple fixations in order to form a holistic scene representation that allows for visual reasoning about compositional structure? We address these questions by optimizing model neural systems to perform active vision tasks. These model systems in turn provide us with new ways to think about structures found in biology, and they point to new experiments that explore the neural mechanisms enabling active vision.