University of Pennsylvania |
Encoding neural wiring and activity as a network welcomes the application of various tools from theoretical mathematics in the analysis of brain data, thus progressing the understanding of the structure-function relationship as well as the learning process. My work in the Complex Systems group focused on evolving mathematical techniques for computational network analysis. I then used these methods to determine a classification of previously reported models based on their structural features and considered the effect of such architecture on network function. The code and reference for this work can be found on the Network Toolbox page. In December 2015 I completed a Master's thesis on this work, titled "Cliques and Cycles: A Complementary Pair of Homological Features Detects Structure in Weighted Networks."
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