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Linking Structure and Computation of Data-driven Brain Networks
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Hannah Choi, Ph.D.
School of Mathematics
Georgia Institute of Technology
ABSTRACT
The complex connectivity structure unique to the brain network is believed to underlie its robust and efficient coding capability. One of many unique features of the mammalian cortico-thalamic network is its hierarchical organization. I will discuss functional implications of the hierarchical structure of mammalian cortical network in the framework of predictive coding. Specifically, I will focus on a hierarchical predictive coding model of visual cortex to understand how robust encoding of noisy visual stimuli emerges, and further discuss distinct computations carried out by layer-specific feedforward and feedback connections in the cortical hierarchy. In the second part of the talk, I will discuss how various visual stimuli shape the complexity of functional networks of neural activity by analyzing their network properties.
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- Workflow Status:Published
- Created By:Jasmine Martin
- Created:10/24/2021
- Modified By:Jasmine Martin
- Modified:10/24/2021
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