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COMPLEX TIME: Adaptation, Aging, & Arrow of Time

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Difference between revisions of "Cognitive Regime Shift I - When the Brain Breaks/Network Breakdown Phenomena"

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|Start time=July 23, 2018 03:30:00 PM
 
|Start time=July 23, 2018 03:30:00 PM
 
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|Is presentation=Yes
 
|Presenter=SidneyRedner
 
|Presenter=SidneyRedner
|Pre-meeting notes=I gave a basic review of percolation theory on lattices and outlined the behavior of physical observables, such as the correlation length, the mean cluster size, and the percolation probability on the bond occupation probability. I then discussed the analogous percolation transition on complex networks, where the degree distribution can be broad. The basic new feature of complex networks is that they are relatively robust to random removal of nodes or links and quite vulnerable to the removal of the highest-degree nodes.<brclass="mw_emptyline_first"><brclass="mw_emptyline">Finally, I presented two examples of network breakdown phenomena: the electrical failure of electrical networks of fuse elements and the external voltage is increased, and the clogging of fluid networks during the process of filtration.
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|Pre-meeting notes=I gave a basic review of percolation theory on lattices and outlined the behavior of physical observables, such as the correlation length, the mean cluster size, and the percolation probability on the bond occupation probability. I then discussed the analogous percolation transition on complex networks, where the degree distribution can be broad. The basic new feature of complex networks is that they are relatively robust to random removal of nodes or links and quite vulnerable to the removal of the highest-degree nodes.
|Post-meeting notes=I'm interested in datasets that could serve as a diagnostic for failure of brain networks. It was mentioned at the meeting that there exists data for response time as a function of age. Perhaps this could be used to understand the performance of the brain network as a function of age.<brclass="mw_emptyline_first"><brclass="mw_emptyline"><brclass="mw_emptyline"><brclass="mw_emptyline">More general question: is there an unambiguous way to determine age by measuring some aspect of brain function?
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|Presentation file=Network-breakdown.key
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Finally, I presented two examples of network breakdown phenomena: the electrical failure of electrical networks of fuse elements and the external voltage is increased, and the clogging of fluid networks during the process of filtration.
 
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Latest revision as of 22:21, January 20, 2019

July 23, 2018
3:30 pm - 4:20 pm

Presenter

Sidney Redner (SFI)

Abstract

I gave a basic review of percolation theory on lattices and outlined the behavior of physical observables, such as the correlation length, the mean cluster size, and the percolation probability on the bond occupation probability. I then discussed the analogous percolation transition on complex networks, where the degree distribution can be broad. The basic new feature of complex networks is that they are relatively robust to random removal of nodes or links and quite vulnerable to the removal of the highest-degree nodes.

Finally, I presented two examples of network breakdown phenomena: the electrical failure of electrical networks of fuse elements and the external voltage is increased, and the clogging of fluid networks during the process of filtration.

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Post-meeting Reflection

Sidney Redner (SFI) Link to the source page

I'm interested in datasets that could serve as a diagnostic for failure of brain networks. It was mentioned at the meeting that there exists data for response time as a function of age. Perhaps this could be used to understand the performance of the brain network as a function of age.

More general question: is there an unambiguous way to determine age by measuring some aspect of brain function?

Reference Material