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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/3908

Title: Automated categorization of drosophila learning and memory behaviors using video analysis
Authors: Reza, Md. Alimoor
Keywords: Computer science;Drosophila melanogaster;Memory
Issue Date: Aug-2011
Abstract: The ability to study learning and memory behavior in living organisms has signi cantly increased our understanding of what genes a ect this behavior, allowing for the rational design of therapeutics in diseases that a ect cognition. The fruit y, Drosophila melanogaster, is a well established model organism used to study the mechanisms of both learning and memory in vivo. The techniques used to assess this behavior in ies, while powerful, su er from a lack of speed and quanti cation. The technical goal of this project is to create an automated method for characterizing this behavior in fruit ies by analyzing video of their movements. A method is developed to replace and improve a labor-intensive, subjective evaluation process with one that is automated, consistent and reproducible; thus allowing for robust, high-throughput analysis of large quantities of video data. The method includes identifying individual ies in a video, quantifying their size (which is correlated with their gender), and tracking their motion. Once the ies are identi ed and tracked, various geometric measures may be computed, for example distance between ies, their relative orientation, velocities and percentage of time the ies are in contact with each other. This data is computed for numerous experimental videos and produces high-dimensional feature vectors that quantify the behavior of the ies. Clustering techniques, e.g., k-means clustering, may then be applied to the feature vectors in order to computationally group each specimen by genotype. Our results show that we are able to automatically di erentiate between normal and defective ies. We also generated a Computed Courtship Index (CCI), a computational equivalent of the existing Courtship Index (CI), and compared CCI with CI. These results demonstrate that our automated analysis provides a numerical scoring of y behavior that is similar to the scoring produced by human observers.
Description: Thesis (M.S., Computer science)--Drexel University, 2011.
URI: http://hdl.handle.net/1860/3908
Appears in Collections:Drexel Theses and Dissertations

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