In this paper we present a modification of the strongly diluted Hopfield model in which the dilution scheme, instead of being random, is inspired on biological grounds. We also show that the resulting sparsely connected neural network shows striking features in terms of performance as an...

This research extends a method previously applied to music and philosophy (Vilson Vieira et al., 2012), representing the evolution of art as a time-series where relations like dialectics are measured quantitatively. For that, a corpus of paintings of 12 well-known artists from baroque and...

In this paper we present a novel technique of studying EEG signals taking into account their essential nonstationarity. The bursts of activity in EEG rhythm are modeled as a superposition of specially designed elementary signals against the background of a real EEG record at rest. To calculate...

We have studied the effect of various kinds of damaging that may occur in a neural network whose synaptic bonds have been trained (before damaging) so as to preserve a definite number of patterns. We have used the Hopfield model of the neural network, and applied the Hebbian rule of training...

We introduce the concept of “negative bubbles” as the mirror (but not necessarily exactly symmetric) image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the...

We introduce a semiempirical theory leading to the ab initio prediction of conducive folding pathways and coarsely resolved native backbone geometries of proteins suddenly exposed to in vitro renaturation conditions. The underlying model incorporates a discrete codification of local steric...

Excitable media dynamics is the lossless active transmission of waves of excitation over a field of coupled elements, such as electrical excitation in heart tissue or nerve fibers, cAMP signaling in the slime mold Dictyostelium discoideum or waves of chemical activity in the...

This work proposes to obtain novel fractal descriptors from gray-level texture images by combining information from interior and boundary measures of the Minkowski dilation applied to the texture surface. At first, the image is converted into a surface where the height of each point is the gray...

We developed two new tools to deal with synthetic aperture radar (SAR) images, based on two different and powerful mathematical formalism: wavelets (WT) and Karhunen Loève transforms (KLT). WT are used to remove the undesirable speckle, which comes from the specific measurement process of SAR...

We study the predictability of extreme events in records with linear and nonlinear long-range memory in the presence of additive white noise using two different approaches: (i) the precursory pattern recognition technique (PRT) that exploits solely the information about short-term precursors,...