100,809 research outputs found
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
Sisyphus Effect in Pulse Coupled Excitatory Neural Networks with Spike-Timing Dependent Plasticity
The collective dynamics of excitatory pulse coupled neural networks with
spike timing dependent plasticity (STDP) is studied. Depending on the model
parameters stationary states characterized by High or Low Synchronization can
be observed. In particular, at the transition between these two regimes,
persistent irregular low frequency oscillations between strongly and weakly
synchronized states are observable, which can be identified as infraslow
oscillations with frequencies 0.02 - 0.03 Hz. Their emergence can be explained
in terms of the Sisyphus Effect, a mechanism caused by a continuous feedback
between the evolution of the coherent population activity and of the average
synaptic weight. Due to this effect, the synaptic weights have oscillating
equilibrium values, which prevents the neuronal population from relaxing into a
stationary macroscopic state.Comment: 18 pages, 24 figures, submitted to Physical Review
Why are probabilistic laws governing quantum mechanics and neurobiology?
We address the question: Why are dynamical laws governing in quantum
mechanics and in neuroscience of probabilistic nature instead of being
deterministic? We discuss some ideas showing that the probabilistic option
offers advantages over the deterministic one.Comment: 40 pages, 8 fig
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