Title: "Understanding vision through the lens of prediction"
Abstract: Prediction is necessary for overcoming short timescale sensory and motor delays present in all neural systems. In order to interact appropriately with a changing environment, the brain must respond not only to the current state of sensory inputs but must also make rapid predictions of these inputs' future state. To test whether the visual system performs optimal predictive compression and computation, we compute the past and future stimulus information in populations of retinal ganglion cells, the output cells of the retina, in salamanders and rats. For some simple stimuli with mixtures of predictive and random components to their motion, we can derive the optimal tradeoff between compressing information about the past stimulus while retaining as much information as possible about the future stimulus. By changing parameters in the input motion, we can explore qualitatively different motion prediction problems. This allows us to begin to ask which prediction problems the retina has evolved to solve optimally. Furthermore, we explore the tradeoffs between optimally representing predictive information in the stimulus, and decorrelating neural responses in time.