Annual variability in a conceptual climate model: Snapshot attractors, hysteresis in extreme events, and climate sensitivity

Tamás Bódai and Tamás Tél

Within Lorenz’s model of global atmospheric circulation (L84), the climate is sensitive if under slightly different conditions, the climate snapshot attractor takes substantially different shapes. This paper focuses on extreme event return time statistics, climate sensitivity, and overall variability in view of snapshot attractors.

Chaos 22 , 023110 (2012)

Chimeras in random non-complete networks of phase oscillators

Carlo R. Laing, Karthikeyan Rajendran, and Ioannis G. Kevrekidis

Chimera states are known to occur in networks of identical phase oscillators and are characterised by some fraction of the oscillators being synchronised, while the remainder are asynchronous. Here, we systematically investigate these systems for what is arguably the simplest network that shows chimera states, considering two different systematic ways of perturbing an all-to-all connected network.

Chaos 22 , 013132 (2012)

The impact of awareness on epidemic spreading in networks

Qingchu Wu, Xinchu Fu, Michael Small, and Xin-Jian Xu

The authors explore the impact of awareness on epidemic spreading through a population represented by a scale-free network. Using a network mean-field approach, a mathematical model for epidemic spreading with awareness reactions is proposed and analyzed. Theoretical analysis and simulation shows that global awareness cannot decrease the likelihood of an epidemic outbreak while both local awareness and contact awareness can.

Chaos 22, 013101 (2012)

Propagation of spiking regularity and double coherence resonance in feedforward networks

Cong Men, Jiang Wang, Ying-Mei Qin, Bin Deng, Kai-Ming Tsang, and Wai-Lok Chan

Multi-layer feedforward networks (FFNs) are related to functional groups of neurons where information is transmitted from one group to the next. Since much information in neural systems is carried by interspike intervals time series, the propagation of spike regularity in the brain is an essential problem. This paper investigates the effects of noise on the propagation of spike regularity in FFN.

Chaos 22, 013104 (2012)