Estudo da densidade de corrente crítica para reversão da magnetização de nanoelementos ferromagnéticos

The discovery that a spin-polarized current is capable of exerting a torque in a ferromagnetic material, through spin transfer, might provide the development of new technological devices that store information via the direction of magnetization. The reduction of current density to revert the magnetization is a primary issue to potential applications on non volatile random access memories (MRAM). We report a theorical study of the dipolar and shape effects on the critical current density for reversal of magnetization, via spin transfer torque (STT), on ferromagnetic nanoelements. The nanostructured system consists on a reference layer, in which the current will be spin-polarized, and a free layer of magnetization reversal. We observed considerable changes on the critical current density as a function of the element’s reversion layer thickness (t = 1.0 nm, 1.5 nm, 2.0 nm e 2.5 nm) and geometry (circular and elliptical), the material kind of the system free layer (Iron and Permalloy) and according to the orientation of the magnetization and the spin polarization with the major axis. We show that the critical current density may be reduced about 50% by reducing the Fe free layer thickness and around 75% when we change the saturation magnetization of circular nanoelements with 2.5 nm of thickness. We still observed a reduction as much as 90% on the current density of reversion for thin nanoelements magnetized along the minor axis direction, using in-plane spin polarization parallel to the magnetization.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.