Synchronization and Connectivity of Discrete Complex Systems
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Transcript of Synchronization and Connectivity of Discrete Complex Systems
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Synchronization and Connectivity of
Discrete Complex Systems
Michael Holroyd
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The neural mechanisms of
breathing in mammals
Christopher A. Del Negro, Ph.D.John A. Hayes, M.S. Ryland W. Pace, B.S.
Dept. of Applied ScienceThe College of William and Mary
Del Negro, Morgado-Valle, Mackay, Pace, Crowder, and Feldman. The Journal of Neuroscience 25, 446-453, 2005.
Feldman and Del Negro. Nature Reviews Neuroscience, In press, 2006.
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Neural basis for behavior
Behavior
Networks
Cells
Molecules
Genes
Networks
Cells
Molecules
Networks
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In vitro breathingNeonatal
rodent
Smith et al. J.Neurophysiol. 1990
500 µm
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In vitro breathing
PreBötzingerComplex
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Experimental Preparation
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Questions
• What does the PreBötzinger Complex network look like?
• What type of networks are best at synchronizing?
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Laplacian Matrix
• Laplacian = Degree – Adjacency matrix
• Positive semi-definite matrix– All eigenvalues are real numbers greater than or
equal to 0.
nk
k
k
}1,0{
}1,0{
2
1
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Algebraic Connectivity
• λ1 = 0 is always an eigenvalue of a Laplacian matrix
• λ2 is called the algebraic connectivity, and is a good measure of synchronizability.
Despite having the same degree sequence, the graph on the left seems weakly connected. On the left λ2 = 0.238 and on the right λ2 = 0.925
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Geometric graphs
Construction: Place nodes at random locations inside the unit circle, and connect any nodes within a radius r of each other.
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λ2 of Poisson random graphs
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λ2 of preferential attachment graphs
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λ2 of geometric graphs
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Degree preserving rewiring
A
B D
C A
B D
C
This allows us to sample from the set of graphs with the same degree sequence.
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Scale-free metric -- s(G)
Eji
ji kkGs),(
)()(
•First defined by Li et. al. in Towards a Theory of Scale-free Graphs
•Graphs with low s(G) are scale-free, while graphs with high s(G) are scale-rich.
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λ2 vs. s(G)
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λ2 vs. clustering coefficient
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Back to the PreBötzinger Complex
• Using a simulation of the PreBötzinger Complex, we can simulate networks with different λ2 values.
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Synchronizability
•Neuron output from PreBötzinger complex simulation. Synchronization when λ2=0.024913 (left) is relatively poor compared to λ2=0.97452 (right).
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Correlation analysis
•Closer values of λ2 can be difficult to distinguish from a raster plot.
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Autocorrelation analysis
Autocorrelation analysis confirms that the higher λ2 network displays better synchronization.
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Further work
• Find a physical network characteristic associated with high algebraic connectivity.
• Maximal shortest path looks like a good candidate: