Information that leaves the eye first encounters a structure called the lateral geniculate nucleus (LGN). From the LGN, information is relayed to the cortex, where neurons have properties closely related to our perceptual experience. To understand how the cortex acquires its remarkable abilities, data on its inputs from the LGN are needed. When we view objects in the world, the neurons in the lateral geniculate nucleus (LGN) are stimulated by small regions of space called their receptive fields. This project will record the electrical responses of the LGN neurons of monkeys performing a visual task. Linear mathematical models of the receptive fields of LGN neurons will be constructed based on their responses to simple forms. These models will be used to predict responses of the LGN cells to natural scenes. In addition, the effects of regions of space surrounding the receptive fields will be assessed. These surrounding regions are expected to reduce the activity of the LGN neurons so that they are more efficient and they conserve energy. In addition to improved understanding of the mechanisms underlying human perception, the results may help to generate ideas about improved designs of artificial vision systems that have wide applications in industry. This collaborative project will provide training for a graduate student in neuroscience at the University of Texas and will also provide training and data for graduate students doing mathematical modeling at Boston University.
