28,778 research outputs found
An associative network with spatially organized connectivity
We investigate the properties of an autoassociative network of
threshold-linear units whose synaptic connectivity is spatially structured and
asymmetric. Since the methods of equilibrium statistical mechanics cannot be
applied to such a network due to the lack of a Hamiltonian, we approach the
problem through a signal-to-noise analysis, that we adapt to spatially
organized networks. The conditions are analyzed for the appearance of stable,
spatially non-uniform profiles of activity with large overlaps with one of the
stored patterns. It is also shown, with simulations and analytic results, that
the storage capacity does not decrease much when the connectivity of the
network becomes short range. In addition, the method used here enables us to
calculate exactly the storage capacity of a randomly connected network with
arbitrary degree of dilution.Comment: 27 pages, 6 figures; Accepted for publication in JSTA
Model reconstruction from temporal data for coupled oscillator networks
In a complex system, the interactions between individual agents often lead to
emergent collective behavior like spontaneous synchronization, swarming, and
pattern formation. The topology of the network of interactions can have a
dramatic influence over those dynamics. In many studies, researchers start with
a specific model for both the intrinsic dynamics of each agent and the
interaction network, and attempt to learn about the dynamics that can be
observed in the model. Here we consider the inverse problem: given the dynamics
of a system, can one learn about the underlying network? We investigate
arbitrary networks of coupled phase-oscillators whose dynamics are
characterized by synchronization. We demonstrate that, given sufficient
observational data on the transient evolution of each oscillator, one can use
machine learning methods to reconstruct the interaction network and
simultaneously identify the parameters of a model for the intrinsic dynamics of
the oscillators and their coupling.Comment: 27 pages, 7 figures, 16 table
Measurement of retinal vessel widths from fundus images based on 2-D modeling
Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel
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