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.

Antenas de microfita com patch quase-fractal para aplicações em Redes WPAN/WLAN

The microstrip antennas are in constant evidence in current researches due to several advantages that it presents. Fractal geometry coupled with good performance and convenience of the planar structures are an excellent combination for design and analysis of structures with ever smaller features and multi-resonant and broadband. This geometry has been applied in such patch microstrip antennas to reduce its size and highlight its multi-band behavior. Compared with the conventional microstrip antennas, the quasifractal patch antennas have lower frequencies of resonance, enabling the manufacture of more compact antennas. The aim of this work is the design of quasi-fractal patch antennas through the use of Koch and Minkowski fractal curves applied to radiating and nonradiating antenna s edges of conventional rectangular patch fed by microstrip inset-fed line, initially designed for the frequency of 2.45 GHz. The inset-fed technique is investigated for the impedance matching of fractal antennas, which are fed through lines of microstrip. The efficiency of this technique is investigated experimentally and compared with simulations carried out by commercial software Ansoft Designer used for precise analysis of the electromagnetic behavior of antennas by the method of moments and the neural model proposed. In this dissertation a study of literature on theory of microstrip antennas is done, the same study is performed on the fractal geometry, giving more emphasis to its various forms, techniques for generation of fractals and its applicability. This work also presents a study on artificial neural networks, showing the types/architecture of networks used and their characteristics as well as the training algorithms that were used for their implementation. The equations of settings of the parameters for networks used in this study were derived from the gradient method. It will also be carried out research with emphasis on miniaturization of the proposed new structures, showing how an antenna designed with contours fractals is capable of a miniaturized antenna conventional rectangular patch. The study also consists of a modeling through artificial neural networks of the various parameters of the electromagnetic near-fractal antennas. The presented results demonstrate the excellent capacity of modeling techniques for neural microstrip antennas and all algorithms used in this work in achieving the proposed models were implemented in commercial software simulation of Matlab 7. In order to validate the results, several prototypes of antennas were built, measured on a vector network analyzer and simulated in software for comparison

Arranjo de antenas de microfita com substrato anisotrópico com patch supercondutor e aplicações em nanotecnologia

The main objective in this work is the analysis of resonance frequency microstrip structures with glass fiber and electromagnetic band gap (EBG/PBG) substrate and analysis of microstrip antennas with rectangular patch of superconductor of high critical temperature (HTS). In this work was used the superconductors YBCO (critical temperature of 90K), SnBaCaCuOy (critical temperature of 160K), and Sn5InCa2Ba4Cu10Oy (critical temperature of 212K) with results in Gigahertz and Terahertz. Was used microstrip antennas arrays planar and linear phase and linear phase planar with patch with superconductor. It presents a study of the major theories that explain superconductivity. In phase arrays were obtained the factors arrays for such configurations, and the criteria of phase and spacing between the elements compound in the array, which were examined in order to get a main lobe with high directivity and high gain. In the analysis we used the method of Transverse Transmission Line (TTL) used in domain of the Fourier Transform (FTD). The LTT is a full wave method, which obtains the electromagnetic field in terms of the components transverse of the structure. The addition of superconductive patch is made using the boundary condition resistive complex. Results are obtained resonance frequency as a function of the parameters of the antenna, radiation patterns of the E and H Planes, for the phase antenna arrays in linear and planar configurations, for different values of the phase and the spacing between elements

Aplicações de arranjos de antenas de microfita com patch supercondutor

This work has as main objective the study of arrays of microstrip antennas with superconductor rectangular patch. The phases and the radiation patterns are analyzed. A study of the main theories is presented that explain the microscopic and macroscopic phenomena of superconductivity. The BCS, London equations and the Two Fluid Model, are theories used in the applications of superconductors, at the microstrip antennas and antennas arrays. Phase Arrangements will be analyzed in linear and planar configurations. The arrangement factors of these configurations are obtained, and the phase criteria and the spacing between the elements, are examined in order to minimize losses in the superconductor, compared with normal conductors. The new rectangular patch antenna, consist of a superconducting material, with the critical temperature of 233 K, whose formula is Tl5Ba4Ca2Cu9Oy, is analyzed by the method of the Transverse nTransmission Line (TTL), developed by H. C. C. Fernandes, applied in the Fourier Transform Domain (FTD). The TTL is a full-wave method, which has committed to obtaining the electromagnetic fields in terms of the transverse components of the structure. The inclusion of superconducting patch is made using the complex resistive boundary condition, using the impedance of the superconductor in the Dyadic Green function, in the structure. Results are obtained from the resonance frequency depending on the parameters of the antenna using superconducting material, radiation patterns in E-Plane and H -Plane, the phased antennas array in linear and planar configurations, for different values of phase angles and different spacing between the elements

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.