The retina is a remarkably complex tissue that serves as the initial step in visual perception. While it contains an array of photoreceptors stimulated by light, the retina also has an intricate microcircuitry that allows for computational processes such as predictive coding, background segregation, and pattern adaptation. The goal of this study will be to build biophysical models that accurately replicate the characteristics of pattern adaptation previously studied. Methods used to build these models include linear-nonlinear models, kinetic systems, and statistical methods. Such biophysical models would provide deeper insights as to how biological mechanisms in the retina adapt to patterns, why they may do so, and how other brain regions could perform similar computational roles.

Through this SuperUROP project, I hope to gain experience in using computational neuroscience techniques to solve complex neurological problems. Through taking 9.40 (Introduction to Neural Computation) I’ ve learned the basis of these techniques, but now I am excited to apply them in a way that could lead to a publication. I anticipate obtaining an intuition about how the brain organizes neurons to effectively compute information by the end of my project.