Mark Agrios (3283)

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Simplicial Homology and Neural Networks 

Hometown: Fairfax, Virginia

Department: Neuroscience 

Advisor: Drew LaMar 

 

In this study we use techniques from the mathematical field of algebraic topology to analyze biological neural networks. It has been shown that neural networks that occur in nature have a structure very different than networks generated randomly. Here, we aim to use simplicial homology to extract algebraic information from these networks in order to uncover hidden aspects of their structure that are beneficial to the dynamic system they perpetuate.

 

All William & Mary Honors Fellowships fundraising supports the Charles Center Honors Fellowships Fund. Direct support for individual undergraduate research projects is distributed by the Roy R. Charles Center for Academic Excellence

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