One of the most basic goals in vision research is to understand how visual information is represented at the level of the retinal output cells, the ganglion cells, as these cells provide all the information about the visual world the brain receives. These cells are made up of many different classes, each with its own sensitivities to visual stimuli, and each producing its own set of signals. How these cells work together to collectively form visual representations has been a long-standing critical question - one whose answer is needed both for basic science (for understanding fundamentals of visual processing) and for applied science (for developing algorithms to drive visual prosthetics). We recently developed a tool for addressing this and use it for both these purposes. Briefly, the tool is a retinal input/output model. It differs from other models in that it's effective on a broad range of image statistics, including those of white noise, gratings, natural scenes (landscapes, faces, etc.) With the model we can make rapid advances on both goals.
Our Specific Aims are the following:
Aim 1 is to test hypotheses about the roles of the different ganglion cell classes in representing visual images. We use the model to build the hypotheses, and then electrophysiology (multi-electrode recording) experiments to test them.
Aim 2 focuses on the development of a new retinal prosthetic strategy. We used the model combined with optogenetics to develop a system that can produce normal retinal output, that is, it can make blind, degenerated retinas produce normal firing patterns to a broad range of stimuli, including spatiotemporally varying natural scenes. Here we will develop and expand the method, specifically, so that it is effective not just for ganglion cells but also for bipolar cells, as thse are the two major stimulation targets for retinal prosthetics, and each has its own strengths. This approach produces substantially better (near-normal) representation of visual images than existing methods.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Neurotransporters, Receptors, and Calcium Signaling Study Section (NTRC)
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Greenwell, Thomas
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Weill Medical College of Cornell University
Schools of Medicine
New York
United States
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Yan, Boyuan; Nirenberg, Sheila (2018) An Embedded Real-Time Processing Platform for Optogenetic Neuroprosthetic Applications. IEEE Trans Neural Syst Rehabil Eng 26:233-243
Cideciyan, Artur V; Roman, Alejandro J; Jacobson, Samuel G et al. (2016) Developing an Outcome Measure With High Luminance for Optogenetics Treatment of Severe Retinal Degenerations and for Gene Therapy of Cone Diseases. Invest Ophthalmol Vis Sci 57:3211-21
Yan, Boyuan; Vakulenko, Maksim; Min, Seok-Hong et al. (2016) Maintaining ocular safety with light exposure, focusing on devices for optogenetic stimulation. Vision Res 121:57-71
Aitchison, Laurence; Corradi, Nicola; Latham, Peter E (2016) Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables. PLoS Comput Biol 12:e1005110
Nichols, Zachary; Nirenberg, Sheila; Victor, Jonathan (2013) Interacting linear and nonlinear characteristics produce population coding asymmetries between ON and OFF cells in the retina. J Neurosci 33:14958-73
Bomash, Illya; Roudi, Yasser; Nirenberg, Sheila (2013) A virtual retina for studying population coding. PLoS One 8:e53363
Pandarinath, Chethan; Carlson, Eric T; Nirenberg, Sheila (2013) A system for optically controlling neural circuits with very high spatial and temporal resolution. Proc IEEE Int Symp Bioinformatics Bioeng 2013:
Meytlis, Marsha; Nichols, Zachary; Nirenberg, Sheila (2012) Determining the role of correlated firing in large populations of neurons using white noise and natural scene stimuli. Vision Res 70:44-53
Nirenberg, Sheila; Pandarinath, Chethan (2012) Retinal prosthetic strategy with the capacity to restore normal vision. Proc Natl Acad Sci U S A 109:15012-7
Pandarinath, Chethan; Victor, Jonathan D; Nirenberg, Sheila (2010) Symmetry breakdown in the ON and OFF pathways of the retina at night: functional implications. J Neurosci 30:10006-14

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