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.

Ética na pesquisa em turismo: percepções de discentes de programas de pós-graduação stricto sensu em turismo UFRN, UCS e UNIVALI

This study analyzed the perception of ethics on the part of students of Post-graduation in Tourism, to develop their academic research. It is characterized as exploratory-descriptive, being considered quantitative with some elements analyzed qualitatively. Data were collected through an electronic questionnaire, originated in google docs and sent via e-mail to 54 students in three graduate programs in Tourism: The Tourism Master of the Federal University of Rio Grande do Norte (UFRN), the Master in Tourism and Hospitality of the University of Vale do Itajai (UNIVALI) and Master of Tourism at the University of Caxias do Sul (UCS), resulting in 22 responses. The study presents a profile of the dissertation research of post-graduate students, their perception about research ethics, as well as the difficulties encountered in the research process. Notes that research of dissertation are geared mostly for the historical and cultural aspects of tourism and the choice of topics for the essays came in first place, due to the influence of the guiding lines of research and, secondly, due to social relevance. The methods used in post-graduate research are descriptive, the literature and exploratory. It also notes that most students graduate in tourism (81%) attaches great importance to ethics in carrying out their dissertation research, students claim that ethics brings credibility and seriousness to research. Furthermore, it was realized that they have no knowledge about the practice of misconduct by researchers in the field of Tourism. Demonstrate that the major ethical challenge encountered by students is in relationship with the research subject, what happens during data collection. It also notes that none of the respondents sent any project to be evaluated by an Ethics Committee (CEP), moreover, most do not know the resolution 196/96, which has the main guidelines on research ethics at national level. Concludes that the students post-graduate in tourism have little knowledge about the standards and guidelines on research ethics. They demonstrate ignore the benefits of research ethics in regarding the protection and preservation of the participant, as well as the benefits it generates for society

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.