Francisco Aparecido Rodrigues

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The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches and in applications to real-world problems. Complex networks have fared extremely well in both of these aspects, with their sound theoretical basis developed over the years and with a variety of applications. In this survey, we analyze the(More)
parameters. Recently, Luczak (2006) generates neuronal morphologies using a diffusion limited aggregation (DLA) approach. The simulator CX3D (Zubler and Douglas, 2009) aims at simulating cortical development in 3D space, including the morphology of single neurons. Cuntz et al. (2010) apply a minimal spanning tree principle in generating neuronal(More)
– Deviations from the average can provide valuable insights about the organization of natural systems. The present article extends this important principle to the systematic identification and analysis of singular motifs in complex networks. Six measurements quantifying different and complementary features of the connectivity around each node of a network(More)
Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M.(More)
The relationship between the structure and function of cortical networks is analyzed in terms of signal transmission between different cortical regions in the brains of cat and macaque, as modeled by the fundamental dynamics of diffusion. We investigated the relationship between modular network organization and diffused signal reception and verified that(More)
Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible(More)
We discuss potential caveats when estimating topologies of 3D brain networks from surface recordings. It is virtually impossible to record activity from all single neurons in the brain and one has to rely on techniques that measure average activity at sparsely located (non-invasive) recording sites. Effects of this spatial sampling in relation to structural(More)
Many methods have been developed for data clustering, such as k-means, expectation maximiza-tion and algorithms based on graph theory. In this latter case, graphs are generally constructed by taking into account the Euclidian distance as a similarity measure, and partitioned using spectral methods. However, these methods are not accurate when the clusters(More)
In the present study, we propose a theoretical graph procedure to investigate multiple pathways in brain functional networks. By taking into account all the possible paths consisting of h links between the nodes pairs of the network, we measured the global network redundancy R(h) as the number of parallel paths and the global network permeability P(h) as(More)
This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter.(More)