A Complex Network Approach to Topographical Connections

The neuronal networks in the mammals cortex are characterized by the coexistence of hierarchy, modularity, short and long range interactions, spatial correlations, and topographical connections. Particularly interesting, the latter type of organization implies special demands on the evolutionary and ontogenetic systems in order to achieve precise maps preserving spatial adjacencies, even at the expense of isometry. Although object of intensive biological research, the elucidation of the main anatomic-functional purposes of the ubiquitous topographical connections in the mammals brain remains an elusive issue. The present work reports on how recent results from complex network formalism can be used to quantify and model the effect of topographical connections between neuronal cells over a number of relevant network properties such as connectivity, adjacency, and information broadcasting. While the topographical mapping between two cortical modules are achieved by connecting nearest cells from each module, three kinds of network models are adopted for implementing intracortical connections (ICC), including random, preferential-attachment, and short-range networks. It is shown that, though spatially uniform and simple, topographical connections between modules can lead to major changes in the network properties, fostering more effective intercommunication between the involved neuronal cells and modules. The possible implications of such effects on cortical operation are discussed.Comment: 5 pages, 5 figure

Intermittent exploration on a scale-free network

We study an intermittent random walk on a random network of scale-free degree distribution. The walk is a combination of simple random walks of duration $t_w$ and random long-range jumps. While the time the walker needs to cover all the nodes increases with $t_w$, the corresponding time for the edges displays a non monotonic behavior with a minimum for some nontrivial value of $t_w$. This is a heterogeneity-induced effect that is not observed in homogeneous small-world networks. The optimal $t_w$ increases with the degree of assortativity in the network. Depending on the nature of degree correlations and the elapsed time the walker finds an over/under-estimate of the degree distribution exponent.Comment: 12 pages, 3 figures, 1 table, published versio

Performance of networks of artificial neurons: The role of clustering

The performance of the Hopfield neural network model is numerically studied on various complex networks, such as the Watts-Strogatz network, the Barab{\'a}si-Albert network, and the neuronal network of the C. elegans. Through the use of a systematic way of controlling the clustering coefficient, with the degree of each neuron kept unchanged, we find that the networks with the lower clustering exhibit much better performance. The results are discussed in the practical viewpoint of application, and the biological implications are also suggested.Comment: 4 pages, to appear in PRE as Rapid Com

Learning about knowledge: A complex network approach

This article describes an approach to modeling knowledge acquisition in terms of walks along complex networks. Each subset of knowledge is represented as a node, and relations between such knowledge are expressed as edges. Two types of edges are considered, corresponding to free and conditional transitions. The latter case implies that a node can only be reached after visiting previously a set of nodes (the required conditions). The process of knowledge acquisition can then be simulated by considering the number of nodes visited as a single agent moves along the network, starting from its lowest layer. It is shown that hierarchical networks, i.e. networks composed of successive interconnected layers, arise naturally as a consequence of compositions of the prerequisite relationships between the nodes. In order to avoid deadlocks, i.e. unreachable nodes, the subnetwork in each layer is assumed to be a connected component. Several configurations of such hierarchical knowledge networks are simulated and the performance of the moving agent quantified in terms of the percentage of visited nodes after each movement. The Barab\'asi-Albert and random models are considered for the layer and interconnecting subnetworks. Although all subnetworks in each realization have the same number of nodes, several interconnectivities, defined by the average node degree of the interconnection networks, have been considered. Two visiting strategies are investigated: random choice among the existing edges and preferential choice to so far untracked edges. A series of interesting results are obtained, including the identification of a series of plateaux of knowledge stagnation in the case of the preferential movements strategy in presence of conditional edges.Comment: 18 pages, 19 figure

What are the Best Hierarchical Descriptors for Complex Networks?

This work reviews several hierarchical measurements of the topology of complex networks and then applies feature selection concepts and methods in order to quantify the relative importance of each measurement with respect to the discrimination between four representative theoretical network models, namely Erd\"{o}s-R\'enyi, Barab\'asi-Albert, Watts-Strogatz as well as a geographical type of network. The obtained results confirmed that the four models can be well-separated by using a combination of measurements. In addition, the relative contribution of each considered feature for the overall discrimination of the models was quantified in terms of the respective weights in the canonical projection into two dimensions, with the traditional clustering coefficient, hierarchical clustering coefficient and neighborhood clustering coefficient resulting particularly effective. Interestingly, the average shortest path length and hierarchical node degrees contributed little for the separation of the four network models.Comment: 9 pages, 4 figure

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.Comment: 19 pages, 23 figure

EXPERIMENTAL AND NUMERICAL EVALUATION OF AN IN-FLIGHT ANGLE OF ATTACK MEASUREMENT SYSTEM FOR HIGH POWER MODEL ROCKETS

The angle of attack (α) affects the drag, flight path, and flight stability during rocket ascent. This work proposes an in-flight α measurement system based on pressure measurements at the surface of the nosecone for low apogee rockets. An electronic micro differential pressure transducer was selected to measure the pressure difference between selected points in the rocket’s nosecone. Wind tunnel tests were performed to correlate the α with the sensor output at low Mach numbers (Ma ≃ 0.08). The experimental results were further used as a reference for the construction of CFD models of the external flow in the rocket’s nosecone with the aim of predicting the measurements in an extended Mach number range (up to Ma ≃ 0.7). The numerical results allowed for an extended model correlating α with the differential pressure transducer output (Ch). The estimate of model’s errors completes the analysis

Automatic Network Fingerprinting through Single-Node Motifs

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. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Recuperação de campos nativos suprimidos por agricultura no bioma Pampa: manejo versus regeneração natural.

Os valores médios de riqueza nas áreas de regeneração natural foram inferiores aos das áreas manejadas, e em termos de composição, áreas manejadas tiveram maior similaridade às áreas de referência, indicando que o manejo propiciou aumento na riqueza e composição de espécies em relação às áreas de exclusão, e melhor trajetória dere cuperação