Large-scale data and computational framework for circuit investigation

  • Awardees
  • Eduardo J. Chichilnisky, Ph.D. Stanford University
  • David H. Brainard, Ph.D. University of Pennsylvania
  • Fred Rieke, Ph.D. University of Washington
  • Brian Wandell, Ph.D. Stanford University

Decades of research have uncovered fundamental principles about how the eye works. The cornea and the lens act in concert to form an image of the world on the retina, a paper-thin sheet of neural tissue that lines the back of the eye. The retina is a laminar structure with about 60 distinct types of neurons. The input layer contains specialized photoreceptor cells which sense light.  The signals from the photoreceptors are processed by neurons in the other layers, and the processed output is transmitted on the optic nerve to the brain.  Because of this processing, the role of the retina in vision is much more complex than that of reporting camera-like images to the brain.  However, we lack a unified model that integrates the considerable knowledge about the retinal components and offers a means of calculating the flow of signals from the incident image, through the retina, and onto the optic nerve. We are spearheading an effort to develop such a unified computational model, which will be based on experiments from laboratories around the world, including our own. This model will incorporate existing information about the physiological optics of the eye, as well as key features of retinal neural circuits.  By synthesizing and refining our understanding of the eye, this model will provide a foundation for further investigating the human visual system, evaluating approaches to restore eyesight, and other practical applications in science, engineering, and medicine.


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