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Abstract or Additional Information
There are numerous examples of biological systems outperforming human-designed algorithms at engineering tasks. Rather than relying on one central controller, these systems rely on distributed agents following simple local interaction rules that yield complex collective behavior. My research studies these biological systems in order to reverse-engineer optimization algorithms, with a focus on designing robust, efficient routing networks. In this talk I will discuss how neural arbors use distributed computing to design routing networks that effectively manage tradeoffs between multiple objectives. I will describe a mathematical model for the structure of neural arbors and the tradeoffs they face, and demonstrate that neural arbors manage to attain Pareto-optimal tradeoffs using distributed computation.