1,348 research outputs found
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
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
Stability of Negative Image Equilibria in Spike-Timing Dependent Plasticity
We investigate the stability of negative image equilibria in mean synaptic
weight dynamics governed by spike-timing dependent plasticity (STDP). The
neural architecture of the model is based on the electrosensory lateral line
lobe (ELL) of mormyrid electric fish, which forms a negative image of the
reafferent signal from the fish's own electric discharge to optimize detection
of external electric fields. We derive a necessary and sufficient condition for
stability, for arbitrary postsynaptic potential functions and arbitrary
learning rules. We then apply the general result to several examples of
biological interest.Comment: 13 pages, revtex4; uses packages: graphicx, subfigure; 9 figures, 16
subfigure
Kriminalgeographie Baden-Württembergs (2003-2007) : Sozioökonomische und räumliche Determinanten der registrierten Kriminalität
Dieser Arbeitsbericht stellt wesentliche Ergebnisse des Forschungsprojekts "Regionalanalysen der registrierten Kriminalität" vor. Das Projekt nutzt die Daten der Polizeilichen Kriminalstatistik der Jahre 2003 bis 2007 für eine detaillierte Analyse der regionalen Verteilungen der Kriminalität in Baden-Württemberg auf der Ebene der Städte ab 20.000 Einwohner und der Landkreise. Zudem erfolgt eine kleinräumige Analyse der Tatverdächtigenbelastungsziffern in den über 1000 Gemeinden des Landes. Die Regressionsanalysen können detaillierte Aufschlüsse darüber geben, welche strukturellen Faktoren mit Belastungen unterschiedlicher Kriminalitätsformen in Beziehung stehen. Sowohl soziale Benachteiligungen als auch Gelegenheitsstrukturen sowie deren Wechselwirkungen sind für die Erklärung hoher Kriminalitätsbelastungen relevant. Die Untersuchung zeigt die große Bedeutung der Tätermobilität für die hohe Kriminalitätsbelastung in größeren Städten auf. Die Vielzahl der Ergebnisse aus den Modellrechnungen wird in diesem Bericht verständlich beschrieben und durch Karten und Graphiken anschaulich aufbereitet
Supervised Learning in Multilayer Spiking Neural Networks
The current article introduces a supervised learning algorithm for multilayer
spiking neural networks. The algorithm presented here overcomes some
limitations of existing learning algorithms as it can be applied to neurons
firing multiple spikes and it can in principle be applied to any linearisable
neuron model. The algorithm is applied successfully to various benchmarks, such
as the XOR problem and the Iris data set, as well as complex classifications
problems. The simulations also show the flexibility of this supervised learning
algorithm which permits different encodings of the spike timing patterns,
including precise spike trains encoding.Comment: 38 pages, 4 figure
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
Adaptation Reduces Variability of the Neuronal Population Code
Sequences of events in noise-driven excitable systems with slow variables
often show serial correlations among their intervals of events. Here, we employ
a master equation for general non-renewal processes to calculate the interval
and count statistics of superimposed processes governed by a slow adaptation
variable. For an ensemble of spike-frequency adapting neurons this results in
the regularization of the population activity and an enhanced post-synaptic
signal decoding. We confirm our theoretical results in a population of cortical
neurons.Comment: 4 pages, 2 figure
Non-equilibrium dynamics of stochastic point processes with refractoriness
Stochastic point processes with refractoriness appear frequently in the
quantitative analysis of physical and biological systems, such as the
generation of action potentials by nerve cells, the release and reuptake of
vesicles at a synapse, and the counting of particles by detector devices. Here
we present an extension of renewal theory to describe ensembles of point
processes with time varying input. This is made possible by a representation in
terms of occupation numbers of two states: Active and refractory. The dynamics
of these occupation numbers follows a distributed delay differential equation.
In particular, our theory enables us to uncover the effect of refractoriness on
the time-dependent rate of an ensemble of encoding point processes in response
to modulation of the input. We present exact solutions that demonstrate generic
features, such as stochastic transients and oscillations in the step response
as well as resonances, phase jumps and frequency doubling in the transfer of
periodic signals. We show that a large class of renewal processes can indeed be
regarded as special cases of the model we analyze. Hence our approach
represents a widely applicable framework to define and analyze non-stationary
renewal processes.Comment: 8 pages, 4 figure
A bio-inspired image coder with temporal scalability
We present a novel bio-inspired and dynamic coding scheme for static images.
Our coder aims at reproducing the main steps of the visual stimulus processing
in the mammalian retina taking into account its time behavior. The main novelty
of this work is to show how to exploit the time behavior of the retina cells to
ensure, in a simple way, scalability and bit allocation. To do so, our main
source of inspiration will be the biologically plausible retina model called
Virtual Retina. Following a similar structure, our model has two stages. The
first stage is an image transform which is performed by the outer layers in the
retina. Here it is modelled by filtering the image with a bank of difference of
Gaussians with time-delays. The second stage is a time-dependent
analog-to-digital conversion which is performed by the inner layers in the
retina. Thanks to its conception, our coder enables scalability and bit
allocation across time. Also, our decoded images do not show annoying artefacts
such as ringing and block effects. As a whole, this article shows how to
capture the main properties of a biological system, here the retina, in order
to design a new efficient coder.Comment: 12 pages; Advanced Concepts for Intelligent Vision Systems (ACIVS
2011
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