synchronization and complex networks: are such theories useful for neuroscience?
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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 - PowerPoint PPT PresentationTRANSCRIPT
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/[email protected]
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
Nonlinear Sciences
Start in 1665 by Christiaan Huygens:
Discovery of phase synchronization, called sympathy
Huygens´-Experiment
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
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)
Supplemental tuned mass dampers to reduce the oscillations
GERB Schwingungsisolierungen GmbH, Berlin/Essen
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
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)
Phase Definitions
Analytic Signal Representation (Hilbert Transform)
Direct phase
Phase from Poincare´ plot
(Rosenblum, Pikovsky, Kurths, Phys. Rev. Lett., 1996)
(Phase) Synchronization – good or bad???
Context-dependent
Application:
Cardiovascular System
Cardio-respiratory System
Analysis technique: Synchrogram
Schäfer, Rosenblum, Abel, Kurths: Nature, 1998
Cardiorespiratory SynchronisationNREM REM
Synchrogram
5:1 synchronization during NREM
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)
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)
Networks with Complex
Topology
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
Regular Networks – rings, lattices
Networks with complex topology
• Random graphs/networks (Erdös, Renyi, 1959)
• Small-world networks (Watts, Strogatz, 1998F. Karinthy hungarian writer – SW hypothesis, 1929)
• Scale-free networks (Barabasi, Albert, 1999;D. de Solla Price – number of citations – heavy tail distribution, 1965)
Networks with Complex Topology
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!
Small-world Networks
Nearest neighbour and a few long-range
connections
Nearest neighbourconnections
Regular Complex Topology
Basic Characteristics
• Path length between nodes i and j: - mean path length L
• Degree connectivity – number of connections node i has to all others - mean degree K- degree distribution P(k) Scale-free - power lawRandom - Poisson distribution
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
Properties
• Regular networkslarge 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
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 = < >
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.
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, ...
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)
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)
General Condition for Synchronizability
Stability of synchronized state
N eigenmodes of
ith eigenvalue of G
Main results
Synchronizability universally determined by:
- mean degree K and
- heterogeneity of the intensities
- minimum/ maximum intensities
or
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
Clusters of synchronization
ApplicationNeuroscience
System Brain: Cat Cerebal Cortex
Connectivity
Scannell et al.,
Cereb. Cort., 1999
Modelling
• Intention:
Macroscopic Mesoscopic Modelling
Network of Networks
Density of connections between the four com-munities
•Connections among the nodes: 2 … 35
•830 connections
•Mean degree: 15
Zamora, Zhou, Kurths,
CHAOS 2009
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)
Model for neuron i in area I
FitzHugh Nagumo model
Transition to synchronized firing
g – coupling strength – control parameter
Possible interpretation: functioning of the brain near a 2nd order phase transition
Functional Organization vs. Structural (anatomical) Coupling
Formation of dynamical clusters
Intermediate Coupling
Intermediate Coupling:
3 main dynamical clusters
Strong Coupling
Network topology (anatomy) vs. Functional organization in
networks
• Weak-coupling dynamics non-trivial organization
• Relationship to the underlying network topology
Cognitive Processes
• Processing of visual stimuli
• EEG-measurements (500 Hz, 30 channels)
• Multivariate synchronization analysis to identify clusters
Kanizsa Figures
Challenges
• ECONS: Evolving COmplex NetworkS connectivity is time dependent – strength of connections varies, nodes can be born or die out
• Directed Networks directionality of the connections -
not equal in both directions in general
Identification of connections – How to avoid spurious ones?
Problem of multivariate statistics: distinguish direct and indirect interactions
Extension to Phase Synchronization Analysis
• Bivariate phase synchronization index (n:m synchronization)
• Measures sharpness of peak in histogram of
Schelter, Dahlhaus, Timmer, Kurths: Phys. Rev. Lett. 2006
Summary
Take home messages:
• There are rich synchronization phenomena in complex networks (self-organized structure formation) – hierarchical transitions
• This approach seems to be promising for understanding some aspects in neuroscience and many others (climate, systems biology)
• The identification of direct connections among nodes is non-trivial
Our papers on complex networks
Europhys. Lett. 69, 334 (2005) Phys. Rev. Lett. 98, 108101 (2007)Phys. Rev. E 71, 016116 (2005) Phys. Rev. E 76, 027203 (2007)CHAOS 16, 015104 (2006) New J. Physics 9, 178 (2007)Physica D 224, 202 (2006) Phys. Rev. E 77, 016106 (2008) Physica A 361, 24 (2006) Phys. Rev. E 77, 026205 (2008)Phys. Rev. E 74, 016102 (2006) Phys. Rev. E 77, 027101 (2008)Phys: Rev. Lett. 96, 034101 (2006) CHAOS 18, 023102 (2008)Phys. Rev. Lett. 96, 164102 (2006) J. Phys. A 41, 224006 (2008)Phys. Rev. Lett. 96, 208103 (2006) Phys. Reports 469, 93 (2008)Phys. Rev. Lett. 97, 238103 (2006) Europhys. Lett. 85, 28002 (2009)Phys. Rev. E 76, 036211 (2007) CHAOS 19, 013105 (2009)Phys. Rev. E 76, 046204 (2007) Physica A 388, 2987 (2009)
Europ. J. Phys. B 69, 45 (2009) PNAS (in press) (2009)