Kareem Zaghloul, PhD
Vision: Retina
Personal Background Egyptian. Moved to the states when I was young, grew up in the DC area, went to school at MIT where I majored in electrical engineering, thought Boston was way too cold, came to Penn because it seemed like a good place to be, spent a year in Egypt before getting "serious," and have now been here since then, finishing my PhD and MD degrees. That's about all the personal info you'll find here. Like Kai said, if you really wanna know more than that about me, come on by. |
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My research involves quantifying some of the computations realized by the mammalian retina in order to model this first stage of visual processing in silicon. The retina, an outgrowth of the brain, is the most studied and best understood neural system. A study of its seemingly simple architecture reveals several layers of complexity that underlie its ability to convey visual information to higher cortical structures. The retina efficiently encodes this information by using multiple representations of the visual scene, each communicating a specific feature found within that scene. Our strategy in developing a simplified model for retinal processing entails a multi-disciplinary approach. We use scientific data gathering and analysis methods to gain a better understanding of retinal processing. By recording the response behavior of mammalian retina, we are able to represent retinal filtering with a simple model we can analyze to determine how the retina changes its processing under different stimulus conditions. We also use theoretical methods to predict how the retina processes visual information. This approach, grounded in information theory, allows us to gain intuition as to why the retina processes visual information in the manner it does. Finally, we use engineering methods to design circuits that realize these retinal computations while considering some of the same design constraints that face the mammalian retina. This approach not only confirms some of the intuitions we gain through the other two methods, but it begins to address more fundamental issues related to how we can replicate neural function in artifical systems. Our model for the mammalian retina, and the silicon implementation of that model, produces four parallel representations of the visual scene that reproduce the retina's major output pathways and that incorporate fundamental retinal processing and nonlinear adjustments of that processing, including luminance adaptation, contrast gain control, and nonlinear spatial summation. Our results suggest that by carefully studying the underlying biology of neural circuits, we can replicate some of the complex processing realized by these circuits in silicon.
Done. Come by and check it out, or see the video on the lab homepage.
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