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Hierarchical structure and computation of brain networks
Mathematical Biology| Speaker: | Hannah Choi, University of Washington / Georgia Tech |
| Related Webpage: | https://sites.google.com/site/hannahchoiresearch/ |
| Location: | Online (Zoom) |
| Start time: | Mon, Apr 27 2020, 3:10PM |
The complex connectivity structure unique to the brain network is believed to underlie its robust and efficient coding capability. Specifically, neuronal networks at multiple scales utilize their structural complexities to achieve different computational goals. I will first introduce functional implications that can be inferred from a weighted and directed “single” network representation of the brain. Then, I will consider a more detailed and realistic network representation of the brain featuring multiple types of connection between a pair of brain regions, which enables us to uncover the hierarchical structure of the brain network using an unsupervised method. Finally, I will discuss computational implications of the hierarchical organization of the brain network, focusing on a specific type of visual computation — robust encoding of noisy visual stimuli.
Note that this seminar is online at https://ucdavisdss.zoom.us/j/99480513904. Please email Rishidev Chaudhuri for the password.