Disorder-induced mechanism for positive exchange bias fields

We propose a mechanism to explain the phenomenon of positive exchange bias on magnetic bilayered systems. The mechanism is based on the formation of a domain wall at a disordered interface during field cooling (FC) which induces a symmetry breaking of the antiferromagnet, without relying on any ad hoc assumption about the coupling between the ferromagnetic (FM) and antiferromagnetic (AFM) layers. The domain wall is a result of the disorder at the interface between FM and AFM, which reduces the effective anisotropy in the region. We show that the proposed mechanism explains several known experimental facts within a single theoretical framework. This result is supported by Monte Carlo simulations on a microscopic Heisenberg model, by micromagnetic calculations at zero temperature and by mean field analysis of an effective Ising like phenomenological model.Comment: 5 pages, 4 figure

Stability as a natural selection mechanism on interacting networks

Biological networks of interacting agents exhibit similar topological properties for a wide range of scales, from cellular to ecological levels, suggesting the existence of a common evolutionary origin. A general evolutionary mechanism based on global stability has been proposed recently [J I Perotti, O V Billoni, F A Tamarit, D R Chialvo, S A Cannas, Phys. Rev. Lett. 103, 108701 (2009)]. This mechanism is incorporated into a model of a growing network of interacting agents in which each new agent's membership in the network is determined by the agent's effect on the network's global stability. We show that, out of this stability constraint, several topological properties observed in biological networks emerge in a self organized manner. The influence of the stability selection mechanism on the dynamics associated to the resulting network is analyzed as well.Comment: 10 pages, 9 figure

A scale-free neural network for modelling neurogenesis

In this work we introduce a neural network model for associative memory based on a diluted Hopfield model, which grows through a neurogenesis algorithm that guarantees that the final network is a small-world and scale-free one. We also analyze the storage capacity of the network and prove that its performance is larger than that measured in a randomly dilute network with the same connectivity