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Indo para Dresden dia 20

International Workshop
23 – 27 April 2018

Complex dynamical systems can show sudden transitions to very diverse regimes including dangerous ones such as desertification or epileptic seizures. The workshop will focus on new advances in data-driven approaches for identifying,  characterizing and predicting regime transitions, and promoting cross-fertilization across disciplines.

Topics include

  • ecology
  • biophysics
  • data analysis
  • econophysics
  • brain dynamics
  • social networks
  • extreme events
  • complex systems
  • climate dynamics
  • critical transitions
  • information theory
  • nonlinear dynamics
  • time-series analysis
  • stochastic processes
  • early-warning signals

Invited speakers

Kazuyuki Aihara (JP)
José María Amigó García (ES)
Dante Chialvo (AR)
Jörn Davidsen (CA)
Henk Dijkstra (NL)
Ulrike Feudel (DE)
Petra Friederichs (DE)
Jordi García Ojalvo (ES)
Emilio Hernández-García (ES)
Holger Kantz (DE)
Mark Kramer (US)
Christian Kühn (DE)
Jürgen Kurths (DE)
Inmaculada Leyva (ES)
Milan Palus (CZ)
Ulrich Parlitz (DE)
Martin Rasmussen (GB)
Hui Ren (CN)
Osvaldo A. Rosso (BR)
Maria V. Sanchez-Vives (ES)
Eckehard Schöll (DE)
Michael Small (AU)
Linda Sommerlade (GB)
Aneta Stefanovska (GB)
Piotr Suffczynski (PL)
John Terry (GB)
Raul Toral (ES)
Lev Tsimring (US)
Michal Zochowski (US)


SCIENTIFIC COORDINATORS

Jaroslav Hlinka
(Academy of Sciences of the Czech Republic, Czech Republic)

Klaus Lehnertz
(University of Bonn, Germany)

Cristina Masoller
(Universitat Politecnica de Catalunya, Spain)

ORGANISATION

Mandy Lochar
(Max Planck Institute for the Physics of Complex Systems, Dresden, Germany)

Novo paper submetido para ENTROPY

Self-Organized Supercriticality and Oscillations in Networks of Stochastic Spiking Neurons

Networks of stochastic spiking neurons are interesting models in the area of Theoretical Neuroscience, presenting both continuous and discontinuous phase transitions. Here we study fully connected networks analytically, numerically and by computational simulations. The neurons have dynamic gains that enable the network to converge to a stationary slightly supercritical state (self-organized supercriticality or SOSC) in the presence of the continuous transition. We show that SOSC, which presents power laws for neuronal avalanches plus some large events, is robust as a function of the main parameter of the neuronal gain dynamics. We discuss the possible applications of the idea of SOSC to biological phenomena like epilepsy and dragon king avalanches. We also find that neuronal gains can produce collective oscillations that coexists with neuronal avalanches, with frequencies compatible with characteristic brain rhythms.

Comments: 16 pages, 16 figures divided into 7 figures in the article
Subjects: Adaptation and Self-Organizing Systems (nlin.AO)
Cite as: arXiv:1705.08549 [nlin.AO]
(or arXiv:1705.08549v1 [nlin.AO] for this version)

Meu seminário no Imperial College

imperial-college-001

Range dinâmico olfatório

Figure 1 Ca2+ imaging shows that each pheromone component activates a single glomerulus in the MGC.

RESEARCH ARTICLE

Heterogeneity and Convergence of Olfactory First-Order Neurons Account for the High Speed and Sensitivity of Second-Order Neurons

 

  • Jean-Pierre Rospars mail,
  • Alexandre Grémiaux,
  • David Jarriault,
  • Antoine Chaffiol,
  • Christelle Monsempes,
  • Nina Deisig,
  • Sylvia Anton,
  • Philippe Lucas,
  • Dominique Martinez
  • Published: December 04, 2014
  • DOI: 10.1371/journal.pcbi.1003975

Read more [+]

Paper novo nas mãos dos referees do PRL

Self-Organized Criticality and Neuronal Avalanches in SIRS Networks with Depressing Synapses

Neuronal networks can present activity described by power-law distributed avalanches presumed to be a signature of a critical state. Here we study a random-neighbor network of excitable (SIRS) cellular automata coupled by dynamical (depressing) synapses that exhibits bona ?de self-organized criticality (SOC) even with dissipative bulk dynamics. This occurs because in the stationary regime the model is conservative on average and in the thermodynamic limit the probability distribution for the global branching ratio converges to a delta-function centered at its critical value. Analytical results show perfect agreement with annealed simulations of the model and enable us to study the emergence of SOC as a function of the parametric derivatives of the stationary branching ratio.

Comments: 4 pages, 5 figures
Subjects: Adaptation and Self-Organizing Systems (nlin.AO); Statistical Mechanics (cond-mat.stat-mech)
Cite as: arXiv:1405.7740 [nlin.AO]
(or arXiv:1405.7740v1 [nlin.AO] for this version)