Analysis of data systems requirements for global crop production forecasting in the 1985 time frame

Data systems concepts that would be needed to implement the objective of the global crop production forecasting in an orderly transition from experimental to operational status in the 1985 time frame were examined. Information needs of users were converted into data system requirements, and the influence of these requirements on the formulation of a conceptual data system was analyzed. Any potential problem areas in meeting these data system requirements were identified in an iterative process

An analysis of the HR algorithm for computing the eigenvalues of a matrix

AbstractThe HR algorithm, a method of computing the eigenvalues of a matrix, is presented. It is based on the fact that almost every complex square matrix A can be decomposed into a product A = HR of a so-called pseudo-Hermitian matrix H and an upper triangular matrix R. This algorithm is easily seen to be a generalization of the well-known QR algorithm. It is shown how it is related to the power method and inverse iteration, and for special matrices the connection between the LR and HR algorithms is indicated

Social networking sites and gaining political support.

Since the turn of the Century, Social Networking Sites (SNSs) have become a normal part of most modern American lives. As this has happened, we have seen a spillover of the entertainment and informational nature of these sites into the American political system. Specifically, these sites are used to build support, gain votes and seats, and mobilize political movements by gaining attention and recognition on these sites. Much study has gone into how effective these online campaigns are in doing their job of gaining different kinds of support, but few, if any, have studied how these sites could be used as a tool to gain support and votes for a single candidate. In the current study, a review of recent literature is given, and we then study the campaign of a politician seeking a city council seat of a large mid-east American city. Specifically, we use a SNS campaign on Facebook in the months preceding the primary election, sending promotional messages about the candidate to likely voters in the candidate’s district. We then measure if this campaign leads to an increase of “likes” to the candidate’s Facebook page, if this indication had any relation to the likelihood that these voters would actually go vote, and finally if Sex or age plays a part in certain aspects of the data

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work

Extracting non-linear integrate-and-fire models from experimental data using dynamic I–V curves

The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current–voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models—of the refractory exponential integrate-and-fire type—provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons

Valproate-associated reversible encephalopathy in a 3-year-old girl with Pallister-Killian syndrome

Valproic acid (VPA) is considered to be a drug of first choice for the therapy of generalized and focal epilepsies, including special epileptic syndromes. The drug is usually well tolerated, rare serious complications may occur in some patients, including hemorrhagic pancreatitis, coagulapathies, bone marrow suppression, VPA-induced hepatotoxicity and encephalopathy. We report a case of VPA-associated encephalopathy without hyperammonemia in a 3-year-old girl with Pallister-Killian-Syndrom, combined with a mild hepatopathy and thrombopathy. After withdrawal of VPA, the clinical symptoms and the electroencephalography-alterations vanished rapidly

Generalized Rate-Code Model for Neuron Ensembles with Finite Populations

We have proposed a generalized Langevin-type rate-code model subjected to multiplicative noise, in order to study stationary and dynamical properties of an ensemble containing {\it finite} $N$ neurons. Calculations using the Fokker-Planck equation (FPE) have shown that owing to the multiplicative noise, our rate model yields various kinds of stationary non-Gaussian distributions such as gamma, inverse-Gaussian-like and log-normal-like distributions, which have been experimentally observed. Dynamical properties of the rate model have been studied with the use of the augmented moment method (AMM), which was previously proposed by the author with a macroscopic point of view for finite-unit stochastic systems. In the AMM, original $N$-dimensional stochastic differential equations (DEs) are transformed into three-dimensional deterministic DEs for means and fluctuations of local and global variables. Dynamical responses of the neuron ensemble to pulse and sinusoidal inputs calculated by the AMM are in good agreement with those obtained by direct simulation. The synchronization in the neuronal ensemble is discussed. Variabilities of the firing rate and of the interspike interval (ISI) are shown to increase with increasing the magnitude of multiplicative noise, which may be a conceivable origin of the observed large variability in cortical neurons.Comment: 19 pages, 9 figures, accepted in Phys. Rev. E after minor modification

Triggering up states in all-to-all coupled neurons

Slow-wave sleep in mammalians is characterized by a change of large-scale cortical activity currently paraphrased as cortical Up/Down states. A recent experiment demonstrated a bistable collective behaviour in ferret slices, with the remarkable property that the Up states can be switched on and off with pulses, or excitations, of same polarity; whereby the effect of the second pulse significantly depends on the time interval between the pulses. Here we present a simple time discrete model of a neural network that exhibits this type of behaviour, as well as quantitatively reproduces the time-dependence found in the experiments.Comment: epl Europhysics Letters, accepted (2010

Mission possible: Bio hat Zukunft

Der Beitrag zeigt erste Ergebnisse des FiBL-Österreich/Bio-Austria-Projekts zur Wiederkäuergesundheit im Biolandbau auf. Dieses Projekt soll den Landwirten helefen, die Bioverordnung umzusetzen