Place cells are a type of pyramidal neurons in the hippocampus that activate in a pattern that encodes for space as an animal moves through its environment. Our current project examines how the brain reacts to unexpected visual manipulations by looking at place cell activity. We hypothesize that the greater the visual spatial manipulation, the greater the place cell activity will deviate from normal activity. A genetically encoded indicator recorded neural activity in the mouse’s brain by binding to calcium ions and fluorescing. An algorithm called constrained non-negative matrix factorization identified areas of fluorescence that may represent neurons, but also generated many false positives. Eliminating these false positives is necessary for a more accurate analysis of place cell activity. The percentage of false positives will also allow us to reexamine the effectiveness of the algorithm and identify areas of improvement to generate more accurate neuron selections. By analyzing only the neurons that represent place cells, we hope to better understand how the brain uses vision and how vision is being used to correct for errors. In particular, these findings may have significant impact in disease models, such as Alzheimer’s research, in which the patients have difficulty with episodic memory.