Vision begins in the retina, where light is converted into electrical signals, processed to extract and compress visual information, and transmitted through the optic nerve to the brain. Despite decades of research, a full understanding of these transformations remains incomplete. In particular, most studies have documented specific properties of the responses of single retinal cells in isolation, using specialized artificial visual stimuli. The research performed under this grant aims to develop a full, unified computational model of retinal processing, including spatial and temporal filtering, nonlinear transformations, and adaptation to local luminance and contrast, in complete populations of neurons. The model will be tested by comparing its predictions to data from large-scale multi-electrode recordings of primate retinal ganglion cells (RGCs), verifying that it can mimic known retinal responses, and critically, testing its ability to explain responses to natural visual images, including the effects of fixational and saccadic eye movements. The resulting model will provide a compact encapsulation of the "neural code" of the retina, which will serve as a substrate for understanding all subsequent visual processing in the brain. In addition, the model will provide an essential component in the development of high-acuity retinal prostheses for people blinded by diseases of photoreceptor degeneration. Finally, the model will offer a useful tool for the development and testing of new display technologies.
The research has two main aims: (1) Develop and test a model of nonlinear subunits in RGC populations-- No current model captures the effects of nonlinear computations in a complete sensory neural circuit. The researchers will develop a model incorporating nonlinear subunits that captures the stimulus encoding properties of complete populations of RGCs at the resolution of photoreceptors, and will quantify the implications of these nonlinearities for encoding naturally-occurring visual stimuli. The researchers will develop methods to reliably fit the model to RGC responses to targeted stimuli that stringently constrain model structure, and verify model predictions in closed-loop experiments. (2) Incorporate adaptation; test model with targeted and naturalistic stimuli-- RGC responses adapt to luminance and stimulus contrast. No current model of the RGC population response incorporates adaptation with subunit nonlinearities, natural scenes, and eye movements. The researchers will incorporate adaptation in the model, fit the adaptive model using stochastic stimuli with varying mean and contrast, and test the model using stimuli that produce adaptation within and across subunits.
