20,142 research outputs found
Modular structure in C. elegans neural network and its response to external localized stimuli
Synchronization plays a key role in information processing in neuronal
networks. Response of specific groups of neurons are triggered by external
stimuli, such as visual, tactile or olfactory inputs. Neurons, however, can be
divided into several categories, such as by physical location, functional role
or topological clustering properties. Here we study the response of the
electric junction C. elegans network to external stimuli using the partially
forced Kuramoto model and applying the force to specific groups of neurons.
Stimuli were applied to topological modules, obtained by the ModuLand
procedure, to a ganglion, specified by its anatomical localization, and to the
functional group composed of all sensory neurons. We found that topological
modules do not contain purely anatomical groups or functional classes,
corroborating previous results, and that stimulating different classes of
neurons lead to very different responses, measured in terms of synchronization
and phase velocity correlations. In all cases, however, the modular structure
hindered full synchronization, protecting the system from seizures. More
importantly, the responses to stimuli applied to topological and functional
modules showed pronounced patterns of correlation or anti-correlation with
other modules that were not observed when the stimulus was applied to ganglia.Comment: 23 pages, 6 figure
Magnetic remanence of Josephson junction arrays
In this work we study the magnetic remanence exhibited by Josephson junction
arrays in response to an excitation with an AC magnetic field. The effect,
predicted by numerical simulations to occur in a range of temperatures, is
clearly seen in our tridimensional disordered arrays. We also discuss the
influence of the critical current distribution on the temperature interval
within which the array develops a magnetic remanence. This effect can be used
to determine the critical current distribution of an array.Comment: 8 pages, 4 figures, Talk to be presented on 44th Annual Conference on
Magnetism & Magnetic Materials, San Jose, CA, USA Accepted to be published in
Journal of Applied Physic
Comparison of wheat classification accuracy using different classifiers of the image-100 system
Classification results using single-cell and multi-cell signature acquisition options, a point-by-point Gaussian maximum-likelihood classifier, and K-means clustering of the Image-100 system are presented. Conclusions reached are that: a better indication of correct classification can be provided by using a test area which contains various cover types of the study area; classification accuracy should be evaluated considering both the percentages of correct classification and error of commission; supervised classification approaches are better than K-means clustering; Gaussian distribution maximum likelihood classifier is better than Single-cell and Multi-cell Signature Acquisition Options of the Image-100 system; and in order to obtain a high classification accuracy in a large and heterogeneous crop area, using Gaussian maximum-likelihood classifier, homogeneous spectral subclasses of the study crop should be created to derive training statistics
Evaluation of spatial filtering on the accuracy of wheat area estimate
A 3 x 3 pixel spatial filter for postclassification was used for wheat classification to evaluate the effects of this procedure on the accuracy of area estimation using LANDSAT digital data obtained from a single pass. Quantitative analyses were carried out in five test sites (approx 40 sq km each) and t tests showed that filtering with threshold values significantly decreased errors of commission and omission. In area estimation filtering improved the overestimate of 4.5% to 2.7% and the root-mean-square error decreased from 126.18 ha to 107.02 ha. Extrapolating the same procedure of automatic classification using spatial filtering for postclassification to the whole study area, the accuracy in area estimate was improved from the overestimate of 10.9% to 9.7%. It is concluded that when single pass LANDSAT data is used for crop identification and area estimation the postclassification procedure using a spatial filter provides a more accurate area estimate by reducing classification errors
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