Time After Time: Notes on Delays In Spiking Neural P Systems

Spiking Neural P systems, SNP systems for short, are biologically inspired computing devices based on how neurons perform computations. SNP systems use only one type of symbol, the spike, in the computations. Information is encoded in the time differences of spikes or the multiplicity of spikes produced at certain times. SNP systems with delays (associated with rules) and those without delays are two of several Turing complete SNP system variants in literature. In this work we investigate how restricted forms of SNP systems with delays can be simulated by SNP systems without delays. We show the simulations for the following spike routing constructs: sequential, iteration, join, and split.Comment: 11 pages, 9 figures, 4 lemmas, 1 theorem, preprint of Workshop on Computation: Theory and Practice 2012 at DLSU, Manila together with UP Diliman, DLSU, Tokyo Institute of Technology, and Osaka universit

Late Quaternary monogenetic volcanoes along Río Salado, Sothwest Mendoza Province, Argentina

On the eastem flank of the Andes, to the north of Río Salado in southwest Mendoza Province (35º07'S-35º10'S), there are 4 monogenetic cones with blocky lava flows. A western group of small volcanoes, Hoyada, Lagunita and Loma Negra, with a total volume of -0.2 km3, are composed of amphibole-bearing basaltic andesite, and the eastem, more voluminous Hoyo Colorado volcano, with 0.44 km3 is composed of olivine (+ oxidised amphibole) basaltic andesite. Although data indicate they were emitted through successive, strombolian eruptions, they are overall coeval and the youngest Late Pleistocene volcanoes located in an "extra-Andean" setting, -70 km east of the main volcanic front. The magmas of the westem group of monogenetic cones show petrographic and geochemical characteristics that support processes of crustal interaction during ascent. In contrast, the magmas of the Hoyo Colorado volcano had a more direct ascent. Structural characteristics of the basement rocks to the volcanoes and the current seismotectonic activity of the Andes at this latitude indicate that the monogenetic cones of Río Salado were emplaced in a dominantly compressive tectonic regime

Sincronización de la actividad eléctrica neuronal, utilizando el modelo de Hodgkin-Huxley y el circuito RCLSJ

We simulated the neuronal electrical activity using the Hodgkin-Huxley model (HH) and a superconductor circuit, containing Josephson junctions. These HH model make possible simulate the main neuronal dynamics characteristics such as action potentials, firing threshold and refractory period. The purpose of the manuscript is show a method to syncronize a RCLshunted Josephson junction to a neuronal dynamics represented by the HH model. Thus the RCLSJ circuit is able to mimics the behavior of the HH neuron. We controlated the RCLSJ circuit, using and improved adaptative track scheme, that with the improved Lyapunov functions and the two controllable gain coefficients allowing synchronization of two neuronal models. Results will provide the path to follow forward the understanding neuronal networks synchronization about, generating the intrinsic brain behavior.Simulamos la actividad eléctrica neuronal mediante el modelo de Hodgkin-Huxley (HH) y un circuito superconductor, que contiene uniones Josephson. El modelo HH simulan las características principales de la dinámica neuronal tales como potenciales de acción, umbrales de disparo y el períodos refractarios. El propósito del manuscrito es mostrar un método para sincronizar un circuito con union Josephson RCLSJ a una dinámica neuronal representado por el modelo HH. Así, el circuito RCLSJ es capaz de imitar el comportamiento de la neurona HH. Controlamos el circuito RCLSJ, utilizando un esquema de control adaptativo, que con funciones de Lyapunov y dos coeficientes de ganancia controlables nos permiten la sincronización de los dos modelos neuronales. Los resultados proporcionan una ruta a seguir adelante en el entendimiento de la sincronización de redes neuronales, generadas por el comportamiento intrinseco del cerebro

A comparison of analytical and numerical model predictions of shallow soil temperature variation with experimental measurements

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In several fields of enquiry such as geothermal energy, geology and agriculture, it is of interest to study the thermal behaviour of shallow soils. For this, several analytical and numerical methodologies have been proposed to analyse the temperature variation of the soil in the short and long term. In this paper, a comparative study of different models (sinusoidal, semi-infinite and finite difference method) is conducted to estimate the shallow soil temperature variation in the short and long term. The models were compared with hourly experimental measured data of soil temperature in Leicester, UK, at depths between 0.75 and 2.75 m. The results show that the sinusoidal model is not appropriate to evaluate the short-term temperature variations, such as hourly or daily fluctuations. Likewise, this model is highly affected by the undisturbed ground temperature and can lead to very high errors. Regarding the semi-infinite model, it is accurate enough to predict the short-term temperature variation. However, it is useless to predict the long-term variation at depths greater than 1 m. The finite difference method (FDM) considering the air temperature as a boundary condition for the soil surface is the most accurate approach for estimating both short and long-term temperature variations while the FDM with heat flux as boundary condition is the least accurate approach due to the uncertainty of the assumed parameters. The ranges of errors for the sinusoidal, semi-infinite and FDM are found to be from 76.09 to 142.13%, 12.11 to 104.88% and 1.82 to 28.14% respectively