128 research outputs found
A Fast and Accurate Nonlinear Spectral Method for Image Recognition and Registration
This article addresses the problem of two- and higher dimensional pattern
matching, i.e. the identification of instances of a template within a larger
signal space, which is a form of registration. Unlike traditional correlation,
we aim at obtaining more selective matchings by considering more strict
comparisons of gray-level intensity. In order to achieve fast matching, a
nonlinear thresholded version of the fast Fourier transform is applied to a
gray-level decomposition of the original 2D image. The potential of the method
is substantiated with respect to real data involving the selective
identification of neuronal cell bodies in gray-level images.Comment: 4 pages, 3 figure
Statistical Mechanics Characterization of Neuronal Mosaics
The spatial distribution of neuronal cells is an important requirement for
achieving proper neuronal function in several parts of the nervous system of
most animals. For instance, specific distribution of photoreceptors and related
neuronal cells, particularly the ganglion cells, in mammal's retina is required
in order to properly sample the projected scene. This work presents how two
concepts from the areas of statistical mechanics and complex systems, namely
the \emph{lacunarity} and the \emph{multiscale entropy} (i.e. the entropy
calculated over progressively diffused representations of the cell mosaic),
have allowed effective characterization of the spatial distribution of retinal
cells.Comment: 3 pages, 1 figure, The following article has been submitted to
Applied Physics Letters. If it is published, it will be found online at
http://apl.aip.org
The complex channel networks of bone structure
Bone structure in mammals involves a complex network of channels (Havers and
Volkmann channels) required to nourish the bone marrow cells. This work
describes how three-dimensional reconstructions of such systems can be obtained
and represented in terms of complex networks. Three important findings are
reported: (i) the fact that the channel branching density resembles a power law
implies the existence of distribution hubs; (ii) the conditional node degree
density indicates a clear tendency of connection between nodes with degrees 2
and 4; and (iii) the application of the recently introduced concept of
hierarchical clustering coefficient allows the identification of typical scales
of channel redistribution. A series of important biological insights is drawn
and discussedComment: 3 pages, 1 figure, The following article has been submitted to
Applied Physics Letters. If it is published, it will be found online at
http://apl.aip.org
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.Comment: 10 pages, 7 figure
Growth-Driven Percolations: The Dynamics of Community Formation in Neuronal Systems
The quintessential property of neuronal systems is their intensive patterns
of selective synaptic connections. The current work describes a physics-based
approach to neuronal shape modeling and synthesis and its consideration for the
simulation of neuronal development and the formation of neuronal communities.
Starting from images of real neurons, geometrical measurements are obtained and
used to construct probabilistic models which can be subsequently sampled in
order to produce morphologically realistic neuronal cells. Such cells are
progressively grown while monitoring their connections along time, which are
analysed in terms of percolation concepts. However, unlike traditional
percolation, the critical point is verified along the growth stages, not the
density of cells, which remains constant throughout the neuronal growth
dynamics. It is shown, through simulations, that growing beta cells tend to
reach percolation sooner than the alpha counterparts with the same diameter.
Also, the percolation becomes more abrupt for higher densities of cells, being
markedly sharper for the beta cells.Comment: 8 pages, 10 figure
Neuromorphometric characterization with shape functionals
This work presents a procedure to extract morphological information from
neuronal cells based on the variation of shape functionals as the cell geometry
undergoes a dilation through a wide interval of spatial scales. The targeted
shapes are alpha and beta cat retinal ganglion cells, which are characterized
by different ranges of dendritic field diameter. Image functionals are expected
to act as descriptors of the shape, gathering relevant geometric and
topological features of the complex cell form. We present a comparative study
of classification performance of additive shape descriptors, namely, Minkowski
functionals, and the nonadditive multiscale fractal. We found that the proposed
measures perform efficiently the task of identifying the two main classes alpha
and beta based solely on scale invariant information, while also providing
intraclass morphological assessment
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
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
Transient dynamics for sequence processing neural networks: effect of degree distributions
We derive a analytic evolution equation for overlap parameters including the
effect of degree distribution on the transient dynamics of sequence processing
neural networks. In the special case of globally coupled networks, the
precisely retrieved critical loading ratio is obtained,
where is the network size. In the presence of random networks, our
theoretical predictions agree quantitatively with the numerical experiments for
delta, binomial, and power-law degree distributions.Comment: 11 pages, 6 figure
- …