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Synchronization and Complex Networks: Are such Theories Useful for Neuroscience?

Synchronization and Complex Networks: Are such Theories Useful for Neuroscience?. Jürgen Kurths¹ ², N. Wessel¹, G. Zamora¹, and C. S. Zhou³ ¹Potsdam Institute for Climate Impact Research, RD Transdisciplinary Concepts and Methods and

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Synchronization and Complex Networks: Are such Theories Useful for Neuroscience?

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  1. Synchronization and Complex Networks: Are such Theories Useful for Neuroscience? Jürgen Kurths¹ ², N. Wessel¹, G. Zamora¹, and C. S. Zhou³ ¹Potsdam Institute for Climate Impact Research, RD Transdisciplinary Concepts and Methods and Institute of Physics, Humboldt University, Berlin, Germany ² King´s College, University of Aberdeen, Scotland ³ Baptist University, Hong Kong http://www.pik-potsdam.de/members/kurths/ juergen.kurths@pik-potsdam.de

  2. Outline • Introduction • Synchronization of coupled complex systems and applications • Synchronization in complex networks • Structure vs. functionality in complex brain networks – network of networks • How to determine direct couplings? • Conclusions

  3. Nonlinear Sciences Start in 1665 by Christiaan Huygens: Discovery of phase synchronization, called sympathy

  4. Huygens´-Experiment

  5. Modern Example: Mechanics • London´s Millenium Bridge • - pedestrian bridge • 325 m steel bridge over the Themse • Connects city near St. Paul´s Cathedral with Tate Modern Gallery • Big opening event in 2000 -- movie

  6. Bridge Opening • Unstable modes always there • Mostly only in vertical direction considered • Here: extremely strong unstable lateral Mode – If there are sufficient many people on the bridge we are beyond a threshold and synchronization sets in • (Kuramoto-Synchronizations-Transition, book of Kuramoto in 1984)

  7. Supplemental tuned mass dampers to reduce the oscillations GERB Schwingungsisolierungen GmbH, Berlin/Essen

  8. Examples: Sociology, Biology, Acoustics, Mechanics • Hand clapping (common rhythm) • Ensemble of doves (wings in synchrony) • Mexican wave • Organ pipes standing side by side – quenching or playing in unison (Lord Rayleigh, 19th century) • Fireflies in south east Asia (Kämpfer, 17th century) • Crickets and frogs in South India

  9. Types of Synchronization in Chaotic Processes • phase synchronization • phase difference bounded, but amplitudes may remain uncorrelated (Rosenblum, Pikovsky, Kurths 1996) • generalized synchronization • a positive Lyapunov exponent becomes negative, amplitudes and phases interrelated (Rulkov, Sushchik, Tsimring, Abarbanel 1995) • complete synchronization (Fujisaka, Yamada 1983)

  10. Phase Definitions Analytic Signal Representation (Hilbert Transform) Direct phase Phase from Poincare´ plot (Rosenblum, Pikovsky, Kurths, Phys. Rev. Lett., 1996)

  11. (Phase) Synchronization – good or bad???Context-dependent

  12. Application: Cardiovascular System

  13. Cardio-respiratory System Analysis technique: Synchrogram

  14. Schäfer, Rosenblum, Abel, Kurths: Nature, 1998

  15. Cardiorespiratory Synchronisation REM NREM Synchrogram 5:1 synchronization during NREM

  16. Testing the foetal–maternal heart rate synchronization via model-based analyses Riedl M, van Leeuwen P, Suhrbier A, Malberg H, Grönemeyer D, Kurths J, Wessel N. Testing the fetal maternal heart rate synchronisation via model based analysis. Philos Transact A Math Phys Eng Sci. 367, 1407 (2009)

  17. Distribution of the synchronization epochs (SE) over the maternal beat phases in the original and surrogate data with respect to the n:m combinations 3:2 (top), 4:3 (middle) and 5:3 (bottom) in the different respiratory conditions. For the original data, the number of SE found is given at the top left of each graph. As there were 20 surrogate data sets for each original, the number of SE found in the surrogate data was divided by 20 for comparability. The arrows indicate clear phase preferences. p-values are given for histograms containing at least 6 SE. (pre, post: data sets of spontaneous breathing prior to and following controlled breathing.) Special test statistics: twin surrogates van Leeuwen, Romano, Thiel, Kurths, PNAS (2009)

  18. Networks with Complex Topology

  19. Basic Model in Statistical Physics and Nonlinear Sciences for ensembles • Traditional Approach: • Regular chain or lattice of coupled oscillators; global or nearest neighbour coupling • Many natural and engineering systems more complex (biology, transportation, power grids etc.)  networks with complex topology

  20. Regular Networks – rings, lattices

  21. Networks with Complex Topology Networks with complex topology • Random graphs/networks (Erdös, Renyi, 1959) • Small-world networks (Watts, Strogatz, 1998 • F. Karinthyhungarian writer – SW hypothesis, 1929) • Scale-free networks (Barabasi, Albert, 1999; • D. de Solla Price – number of citations – heavy tail distribution, 1965)

  22. Types of complex networks fraction of nodes in the network having at least k connections to other nodes have a power law scaling Warning: do not forget the log-log-lies!

  23. Small-world Networks Nearest neighbour and a few long-range connections Nearest neighbour connections Regular  Complex Topology

  24. Basic Characteristics • Path length between nodes i and j: - mean path lengthL • Degree connectivity – number of connections node i has to all others - mean degree K - degree distribution P(k) Scale-free - power law Random - Poisson distribution

  25. Basic Characteristics Clustering Coefficient C: How many of the aquaintanences (j, m) of a given person i, on average, are aquainted with each other Local clustering cofficient: Clustering Coefficient

  26. Properties • Regular networks large L and medium C • Random networks (ER) rather small L and small C • Small-world (SW) small L and large C • Scale-free (SF) small L and C varies from cases

  27. Basic Networks Betweenness Centrality B Number of shortest paths that connect nodes j and k Number of shortest paths that connect nodes i and j AND path through node i Local betweenness of node i (local and global aspects included!) Betweenness Centrality B = < >

  28. Useful approaches with networks • Immunization problems (spreading of diseases) • Functioning of biological/physiological processes as protein networks, brain dynamics, colonies of thermites and of social networks as network of vehicle traffic in a region, air traffic, or opinion formation etc.

  29. Scale-freee-like Networks • Network resiliance • Highly robust against random failure of a node • Highly vulnerable to deliberate attacks on hubs • Applications • Immunization in networks of computers, humans, ...

  30. Universality in the synchronization of weighted random networks Our intention: What is the influence of weighted coupling for complete synchronization Motter, Zhou, Kurths: Phys. Rev. E 71, 016116 (2005) Europhys. Lett. 69, 334 (2005) Phys. Rev. Lett. 96, 034101 (2006)

  31. Weighted Network of N Identical Oscillators F – dynamics of each oscillator H – output function G – coupling matrix combining adjacency A and weight W - intensity of node i (includes topology and weights)

  32. General Condition for Synchronizability Stability of synchronized state N eigenmodes of ith eigenvalue of G

  33. Main results Synchronizability universally determinedby: - mean degree K and - heterogeneity of the intensities or - minimum/ maximum intensities

  34. Transition to synchronization in complex networks • Hierarchical transition to synchronization via clustering (e.g. non-identical elements, noise) • Hubs are the „engines“ in cluster formation AND they become synchronized first among themselves

  35. Clusters of synchronization

  36. ApplicationNeuroscience

  37. System Brain: Cat Cerebal Cortex

  38. Connectivity Scannell et al., Cereb. Cort., 1999

  39. Modelling • Intention: Macroscopic  Mesoscopic Modelling

  40. Network of Networks

  41. Density of connections between the four com-munities • Connections among the nodes: 2 … 35 • 830 connections • Mean degree: 15

  42. Zamora, Zhou, Kurths, CHAOS 2009

  43. Major features of organization of cortical connectivity • Large density of connections (many direct connections or very short paths – fast processing) • Clustered organization into functional com- munities • Highly connected hubs (integration of multisensory information)

  44. Model for neuron i in area I FitzHugh Nagumo model

  45. Transition to synchronized firing g – coupling strength – control parameter Possible interpretation: functioning of the brain near a 2nd order phase transition

  46. Functional Organization vs. Structural (anatomical) CouplingFormation of dynamical clusters

  47. Intermediate Coupling Intermediate Coupling: 3 main dynamical clusters

  48. Strong Coupling

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