arXiv subjects

Luciano da Fontoura Costa

Publications and source records attributed to Luciano da Fontoura Costa.

At least 91 records · Page 5Linked to original sources

arXiv2006

Diversity of Cortical States at Non-Equilibrium Simulated by the Ferromagnetic Ising Model Under Metropolis Dynamics

This article investigates the relationship between the interconnectivity and simulated dynamics of the thalamocortical system from the specific perspective of attempting to maximize the diversity of cortical states. This is achieved by designing the dynamics such that they favor opposing activity between adjacent regions, thus promoting dynamic diversity while avoiding widespread activation or de-activation. The anti-ferromagnetic Ising model with Metropolis dynamics is adopted and applied to four variations of the large-scale connectivity of the cat thalamocortical system: (a) considering only cortical regions and connections; (b) considering the entire thalamocortical system; (c) the same as in (b) but with the thalamic connections rewired so as to maintain the statistics of node degree and node degree correlations; and (d) as in (b) but with attenuated weights of the connections between cortical and thalamic nodes. A series of interesting findings are obtained, including the identification of specific substructures revealed by correlations between the activity of adjacent regions in case (a) and a pronounced effect of the thalamic connections in splitting the thalamocortical regions into two large groups of nearly homogenous opposite activation (i.e. cortical regions and thalamic nuclei, respectively) in cases (b) and (c). The latter effect is due to the existence of dense connections between cortical and thalamic regions and the lack of interconnectivity between the latter. Another interesting result regarding case (d) is the fact that the pattern of thalamic correlations tended to mirror that of the cortical regions. The possibility to control the level of correlation between the cortical regions by varying the strength of thalamocortical connections is also identified and discussed.

cond-mat.dis-nn↗

arXiv2006

The Spread of Opinions and Proportional Voting

Election results are determined by numerous social factors that affect the formation of opinion of the voters, including the network of interactions between them and the dynamics of opinion influence. In this work we study the result of proportional elections using an opinion dynamics model similar to simple opinion spreading over a complex network. Erdos-Renyi, Barabasi-Albert, regular lattices and randomly augmented lattices are considered as models of the underlying social networks. The model reproduces the power law behavior of number of candidates with a given number of votes found in real elections with the correct slope, a cutoff for larger number of votes and a plateau for small number of votes. It is found that the small world property of the underlying network is fundamental for the emergence of the power law regime.

physics.soc-ph↗

arXiv2006

Structured thalamocortical connectivity revealed by random walks on complex networks

The segregated regions of the mammalian cerebral cortex and thalamus form an extensive and complex network, whose structure and function are still only incompletely understood. The present article describes an application of the concepts of complex networks and random walks that allows the identification of non-random, highly structured features of thalamocortical connections, and their potential effects on dynamic interactions between cortical areas in the cat brain. Utilizing large-scale anatomical data sets of this thalamocortical system, we investigate uniform random walks in such a network by considering the steady state eigenvector of the respective stochastic matrix. It is shown that thalamocortical connections are organized in such a way as to guarantee strong correlation between the outdegree and occupancy rate (a stochastic measure potentially related to activation) of each cortical area. Possible organizational principles underlying this effect are identified and discussed.

q-bio.NC↗

arXiv2006

Hierarchical characterization of complex networks

While the majority of approaches to the characterization of complex networks has relied on measurements considering only the immediate neighborhood of each network node, valuable information about the network topological properties can be obtained by considering further neighborhoods. The current work discusses on how the concepts of hierarchical node degree and hierarchical clustering coefficient (introduced in cond-mat/0408076), complemented by new hierarchical measurements, can be used in order to obtain a powerful set of topological features of complex networks. The interpretation of such measurements is discussed, including an analytical study of the hierarchical node degree for random networks, and the potential of the suggested measurements for the characterization of complex networks is illustrated with respect to simulations of random, scale-free and regular network models as well as real data (airports, proteins and word associations). The enhanced characterization of the connectivity provided by the set of hierarchical measurements also allows the use of agglomerative clustering methods in order to obtain taxonomies of relationships between nodes in a network, a possibility which is also illustrated in the current article.

cond-mat.stat-mech↗

arXiv2005

Characterizing Polygonality in Biological Structures

Several systems involve spatial arrangements of elements such as molecules or cells, the characterization of which bears important implications to biological and physical investigations. Traditional approaches to quantify spatial order and regularity have relied on nearest neighbor distances or the number of sides of cells. The current work shows that superior features can be achieved by considering angular regularity. Voronoi tessellations are obtained for each basic element and the angular regularity is then estimated from the differences between the angles defined by adjacent cells and a reference angle. In case this angle is 60 degrees, the measurement quantifies the hexagonality of the system. Other reference angles can be considered in order to quantify other types of spatial symmetries. The performance of the angular regularity is compared with other measurements including the conformity ratio (based on nearest neighbor distances) and the number of sides of the cells, confirming its improved sensitivity and discrimination power. The superior performance of the haxagonality measurement is illustrated also with respect to a real application concerning the characterization of retinal mosaics.

q-bio.TO↗

arXiv2005

A Generalized Approach to Complex Networks

This work describes how the formalization of complex network concepts in terms of discrete mathematics, especially mathematical morphology, allows a series of generalizations and important results ranging from new measurements of the network topology to new network growth models. First, the concepts of node degree and clustering coefficient are extended in order to characterize not only specific nodes, but any generic subnetwork. Second, the consideration of distance transform and rings are used to further extend those concepts in order to obtain a signature, instead of a single scalar measurement, ranging from the single node to whole graph scales. The enhanced discriminative potential of such extended measurements is illustrated with respect to the identification of correspondence between nodes in two complex networks, namely a protein-protein interaction network and a perturbed version of it. The use of other measurements derived from mathematical morphology are also suggested as a means to characterize complex networks connectivity in a more comprehensive fashion.

cond-mat.stat-mech↗

arXiv2005

Spectral Detrended Fluctuation Analysis and Its Application to Heart Rate Variability Assessment

Detrend fluctuation analysis (DFA) has become a choice method for effective analysis of a broad variety of nonstationary signals. We show in the present article that, provided the nonstationary fluctuations occur at a large enough time scale, an alternative approach can be obtained by using the Fourier series of the signal. More specifically, signal reconstructions considering Fourier series with increasing number of higher spectral components are subtracted from the signal, while the dispersion of such a difference is calculated. The slope of the loglog representation of the dispersions in terms of the time scale (reciprocal of the frequency) is calculated and used for the characterization of the signal. The detrend action in this methodology is performed by the early incorporation of the low frequency spectral components in the signal representation. The application of the spectral DFA to the analysis of heart rate variability data has yielded results which are similar to those obtained by traditional DFA. Because of the direct relationship with the spectral content of the analyzed signal, the spectral DFA may be used as a complementary resource for characterization and analysis of some types of nonstationary signals.

q-bio.QM↗

arXiv2005

Hierarchy, Fractality, Small-World and Resilience of Haversian Bone Structure: A Complex Network Study

This article describes the application of recently introduced complex networks concepts and methods to the characterization and analysis of cortical bone structure. Three-dimensional reconstructions of the system of channels underlying bone structure are obtained by using histological and computer graphics methods and then represented in terms of complex networks. Conﬂuences of two or more channels are represented as nodes, while the interconnecting channels are expressed as edges. The hierarchical backbone (the tree with maximum depth) of such a network is obtained and understood to correspond to the main structure underlying the channel system. The remainder of the network is shown to correspond to geographical communities, suggesting that the bone channel structure involves a number of regular communities appended along the hierarchical backbone. It is shown that such additional edges play a crucial role in enhancing the network resilience and in reducing the shortest paths in both topology and geometry.The recently introduced concept of fractal dimension of a network (cond-mat/0503078) is then correlated with the resilience of the several components of the bone channel structure to obstruction and failure, with important implications for the understanding of the organization and robustness of cortical bone structure.

q-bio.TO↗

arXiv2005

The Dynamics of Hierarchical Evolution of Complex Networks

Introduced recently, the concept of hierarchical degree allows a more complete characterization of the topological context of a node in a complex network than the traditional node degree. This article presents analytical characterization and studies of the density of hierarchical degrees in random and scale free networks. The obtained results allowed the identification of a hierarchy-dependent power law for the degrees of nodes in random complex networks, with Poisson density for the first hierarchical degree (obtained through master equation approach). Exact results were obtained for the second hierarchical degree in scale free networks.

cond-mat.stat-mech↗

arXiv2005

Socioeconomic development and stability: A complex network blueprint

The current work discusses how complex networks can be applied in order to aid economical development and stability at several scales and contexts. The following activities are involved: (a) compilation of several types of data related to socioeconomic development; (b) representation of such data in terms of multilayer interacting complex networks (cond-mat/0406369) registered geographically; (c) application of traditional and new methods for complex networks characterization and analysis. Such an approach allows the identification of bottlenecks and deficits/surpluses, simulation of system development under varying constraints and perturbations, as well as the application of optimization methods in order to help identify the most effective strategies leading to social and economic wealth and stability. In addition to its practical implications, such an approach also emphasizes several issues of substantial theoretical interest, including the integration of networks of different natures, the interplay between the dynamics of topological and node state evolution, the effects of geographical constraints, community finding, as well as optimization issues. The discussed methodology is particularly interesting for applications in developing countries because of the greater potential for economic evolution in such nations. This manuscript also includes a brief review of complex networks approaches to socioeconomics modeling.

physics.soc-ph↗

arXiv2005

Characterizing the Hexagonality of Anodic Aluminium Oxide Nanoporous Arrays

Nanoporous anodized alumina oxide have been used as templates for obtaining nanomaterials such as nanowires, which exhibit interesting electronic, magnetic and optical properties. This article presents how the regularity of the spatial distribution of pores in such templates, which affects several of the physical properties of the obtained nanomaterials, has been objectively quantified. The method uses adaptive thresholding, wave propagation image analysis methods, as well as an hexagonality measurement which was found to be particularly suitable because of its locality and invariance to translation, rotation and scaling. A comparison between commercial and laboratory-made samples is presented in order to test the method, resulting higher hexagonality for the latter type of templates.

cond-mat.mtrl-sci↗

arXiv2005

Geometrical constraints in a gene network model and pattern formation

A fundamental task in developmental biology is to identify the mechanisms which drive morphogenesis. In many cases, pattern formation is driven by the positional information determined by both the gradient of maternal factors and hard-wired mechanisms embedded in the genome. Alternative mechanisms of positional information that contribute to patterning are the influence of signals derived from surrounding tissues. In this paper, we show that the interplay of geometrical constraints imposed by tissue shapes and hard-wired mechanisms, computationally implemented by a gene network model, can able to induce stable complex patterns. The rise of these patterns depends strongly on the geometrical constraints such as the shape of the surrounding tissues.

q-bio.MN↗

arXiv2005

Morphological complex networks: Can individual morphology determine the general connectivity and dynamics of networks?

This article discusses how the individual morphological properties of basic objects (e.g. neurons, molecules and aggregates), jointly with their particular spatial distribution, can determine the connectivity and dynamics of systems composed by those objects. This problem is characterized as a particular case of the more general shape and function paradigm, which emphasizes the interplay between shape and function in nature and evolution. Five key issues are addressed: (a) how to measure shapes; (b) how to obtain stochastic models of classes of shapes; (c) how to simulate morphologically realistic systems of multiple objects; (d) how to characterize the connectivity and topology of such systems in terms of complex network concepts and measurements; and (e) how the dynamics of such systems can be ultimately affected, and even determined, by the individual morphological features of the basic objects. Although emphasis is placed on neuromorphic systems, the presented concepts and methods are useful also for several other multiple object systems, such as protein-protein interaction, tissues, aggregates and polymers.

q-bio.MN↗

arXiv2005

Complex Grid Computing

This article investigates the performance of grid computing systems whose interconnections are given by random and scale-free complex network models. Regular networks, which are common in parallel computing architectures, are also used as a standard for comparison. The processing load is assigned to the processing nodes on demand, and the efficiency of the overall computing is quantified in terms of the respective speed-ups. It is found that random networks allow higher computing efficiency than their scale-free counterparts as a consequence of the smaller number of isolated clusters implied by the former model. At the same time, for fixed cluster sizes, the scale free model tend to provide slightly better efficiency. Two modifications of the random and scale free paradigms, where new connections tend to favor more recently added nodes, are proposed and shown to be more effective for grid computing than the standard models. A well-defined correlation is observed between the topological properties of the network and their respective computing efficiency.

cond-mat.stat-mech↗

arXiv2005

Scale Free Subnetworks by Design and Dynamics

This article addresses the degree distribution of subnetworks, namely the number of links between the nodes in each subnetwork and the remainder of the structure (cond-mat/0408076). The transformation from a subnetwork-partitioned model to a standard weighted network, as well as its inverse, are formalized. Such concepts are then considered in order to obtain scale free subnetworks through design or through a dynamics of node exchange. While the former approach allows the immediate derivation of scale free subnetworks, in the latter nodes are sequentially selected with uniform probability among the subnetworks and moved into another subnetwork with probability proportional to the degree of the latter. Comparison of the designed scale-free subnetworks with random and Barabási-Albert counterparts are performed in terms of a set of hierarchical measurements.

cond-mat.dis-nn↗

arXiv2005

Active percolation analysis of pyramidal neurons of somatosensory cortex: A comparison of wildtype and p21H-ras^Val12 transgenic mice

This article describes the investigation of morphological variations among two set of neuronal cells, namely a control group of wild type rat cells and a group of cells of a trangenic line. Special attention is given to sigular points in the neuronal structure, namely the branching points and extremities of the dendritic processes. The characterization of the spatial distribution of such points is obtained by using a recently reported morphological technique based on forced percolation and window-size compensation, which is particularly suited to the analysis of scattered points presenting several coexisting densities. Different dispersions were identified in our statistical analysis, suggesting that the transgenic line of neurons is characterized by a more pronounced morphological variation. A classification scheme based on a canonical discriminant function was also considered in order to identify the morphological differences.

q-bio.NC↗

arXiv2005

Strength Distribution in Derivative Networks

This article describes a complex network model whose weights are proportional to the difference between uniformly distributed ``fitness'' values assigned to the nodes. It is shown both analytically and experimentally that the strength density (i.e. the weighted node degree) for this model, called derivative complex networks, follows a power law with exponent $γ<1$ if the fitness has an upper limit and $γ>1$ if the fitness has no upper limit but a positive lower limit. Possible implications for neuronal networks topology and dynamics are also discussed.

cond-mat.stat-mech↗

arXiv2005

Sznajd Complex Networks

The Sznajd cellular automata corresponds to one of the simplest and yet most interesting models of complex systems. While the traditional two-dimensional Sznajd model tends to a consensus state (pro or cons), the assignment of the contrary to the dominant opinion to some of its cells during the system evolution is known to provide stabilizing feedback implying the overall system state to oscillate around null magnetization. The current article presents a novel type of geographic complex network model whose connections follow an associated feedbacked Sznajd model, i.e. the Sznajd dynamics is run over the network edges. Only connections not exceeding a maximum Euclidean distance $D$ are considered, and any two nodes within such a distance are randomly selected and, in case they are connected, all network nodes which are no further than $D$ are connected to them. In case they are not connected, all nodes within that distance are disconnected from them. Pairs of nodes are then randomly selected and assigned to the contrary of the dominant connectivity. The topology of the complex networks obtained by such a simple growth scheme, which are typically characterized by patches of connected communities, is analyzed both at global and individual levels in terms of a set of hierarchical measurements introduced recently. A series of interesting properties are identified and discussed comparatively to random and scale-free models with the same number of nodes and similar connectivity.

cond-mat.dis-nn↗