1,776 research outputs found
Spontaneous structure formation in a network of chaotic units with variable connection strengths
As a model of temporally evolving networks, we consider a globally coupled
logistic map with variable connection weights. The model exhibits
self-organization of network structure, reflected by the collective behavior of
units. Structural order emerges even without any inter-unit synchronization of
dynamics. Within this structure, units spontaneously separate into two groups
whose distinguishing feature is that the first group possesses many
outwardly-directed connections to the second group, while the second group
possesses only few outwardly-directed connections to the first. The relevance
of the results to structure formation in neural networks is briefly discussed.Comment: 4 pages, 3 figures, REVTe
Short-Term Memory in Orthogonal Neural Networks
We study the ability of linear recurrent networks obeying discrete time
dynamics to store long temporal sequences that are retrievable from the
instantaneous state of the network. We calculate this temporal memory capacity
for both distributed shift register and random orthogonal connectivity
matrices. We show that the memory capacity of these networks scales with system
size.Comment: 4 pages, 4 figures, to be published in Phys. Rev. Let
Simple Lattice-Models of Ion Conduction: Counter Ion Model vs. Random Energy Model
The role of Coulomb interaction between the mobile particles in ionic
conductors is still under debate. To clarify this aspect we perform Monte Carlo
simulations on two simple lattice models (Counter Ion Model and Random Energy
Model) which contain Coulomb interaction between the positively charged mobile
particles, moving on a static disordered energy landscape. We find that the
nature of static disorder plays an important role if one wishes to explore the
impact of Coulomb interaction on the microscopic dynamics. This Coulomb type
interaction impedes the dynamics in the Random Energy Model, but enhances
dynamics in the Counter Ion Model in the relevant parameter range.Comment: To be published in Phys. Rev.
African vegetable diversity in the limelight: project activities by ProNIVA.
Poster presented at Botanical Congress. Hamburg (Germany), 3-7 Sep 200
Towards a neural hierarchy of time scales for motor control
Animals show remarkable rich motion skills which are still far from realizable with robots. Inspired by the neural circuits which generate rhythmic motion patterns in the spinal cord of all vertebrates, one main research direction points towards the use of central pattern generators in robots. On of the key advantages of this, is that the dimensionality of the control problem is reduced. In this work we investigate this further by introducing a multi-timescale control hierarchy with at its core a hierarchy of recurrent neural networks. By means of some robot experiments, we demonstrate that this hierarchy can embed any rhythmic motor signal by imitation learning. Furthermore, the proposed hierarchy allows the tracking of several high level motion properties (e.g.: amplitude and offset), which are usually observed at a slower rate than the generated motion. Although these experiments are preliminary, the results are promising and have the potential to open the door for rich motor skills and advanced control
Hopping Transport in the Presence of Site Energy Disorder: Temperature and Concentration Scaling of Conductivity Spectra
Recent measurements on ion conducting glasses have revealed that conductivity
spectra for various temperatures and ionic concentrations can be superimposed
onto a common master curve by an appropriate rescaling of the conductivity and
frequency. In order to understand the origin of the observed scaling behavior,
we investigate by Monte Carlo simulations the diffusion of particles in a
lattice with site energy disorder for a wide range of both temperatures and
concentrations. While the model can account for the changes in ionic activation
energies upon changing the concentration, it in general yields conductivity
spectra that exhibit no scaling behavior. However, for typical concentrations
and sufficiently low temperatures, a fairly good data collapse is obtained
analogous to that found in experiment.Comment: 6 pages, 4 figure
Internal Friction and Vulnerability of Mixed Alkali Glasses
Based on a hopping model we show how the mixed alkali effect in glasses can
be understood if only a small fraction c_V ofthe available sites for the mobile
ions is vacant. In particular, we reproduce the peculiar behavior of the
internal friction and the steep fall (''vulnerability'') of the mobility of the
majority ion upon small replacements by the minority ion. The single and mixed
alkali internal friction peaks are caused by ion-vacancy and ion-ion exchange
processes. If c_V is small, they can become comparable in height even at small
mixing ratios. The large vulnerability is explained by a trapping of vacancies
induced by the minority ions. Reasonable choices of model parameters yield
typical behaviors found in experiments.Comment: 4 pages, 4 figure
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
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