1,053 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
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
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} 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 -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
Particle trajectories in linearized irrotational shallow water flows
We investigate the particle trajectories in an irrotational shallow water
flow over a flat bed as periodic waves propagate on the water's free surface.
Within the linear water wave theory, we show that there are no closed orbits
for the water particles beneath the irrotational shallow water waves. Depending
on the strength of underlying uniform current, we obtain that some particle
trajectories are undulating path to the right or to the left, some are looping
curves with a drift to the right and others are parabolic curves or curves
which have only one loop
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
Breaking Synchrony by Heterogeneity in Complex Networks
For networks of pulse-coupled oscillators with complex connectivity, we
demonstrate that in the presence of coupling heterogeneity precisely timed
periodic firing patterns replace the state of global synchrony that exists in
homogenous networks only. With increasing disorder, these patterns persist
until they reach a critical temporal extent that is of the order of the
interaction delay. For stronger disorder these patterns cease to exist and only
asynchronous, aperiodic states are observed. We derive self-consistency
equations to predict the precise temporal structure of a pattern from the
network heterogeneity. Moreover, we show how to design heterogenous coupling
architectures to create an arbitrary prescribed pattern.Comment: 4 pages, 3 figure
Population coding by globally coupled phase oscillators
A system of globally coupled phase oscillators subject to an external input
is considered as a simple model of neural circuits coding external stimulus.
The information coding efficiency of the system in its asynchronous state is
quantified using Fisher information. The effect of coupling and noise on the
information coding efficiency in the stationary state is analyzed. The
relaxation process of the system after the presentation of an external input is
also studied. It is found that the information coding efficiency exhibits a
large transient increase before the system relaxes to the final stationary
state.Comment: 7 pages, 9 figures, revised version, new figures added, to appear in
JPSJ Vol 75, No.
On the particle paths and the stagnation points in small-amplitude deep-water waves
In order to obtain quite precise information about the shape of the particle
paths below small-amplitude gravity waves travelling on irrotational deep
water, analytic solutions of the nonlinear differential equation system
describing the particle motion are provided. All these solutions are not closed
curves. Some particle trajectories are peakon-like, others can be expressed
with the aid of the Jacobi elliptic functions or with the aid of the
hyperelliptic functions. Remarks on the stagnation points of the
small-amplitude irrotational deep-water waves are also made.Comment: to appear in J. Math. Fluid Mech. arXiv admin note: text overlap with
arXiv:1106.382
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