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Rather than model this using networks built from single neuron models, it is has proven especially useful to develop low-dimensional models to mimic the collection of thousands of near identical interconnected neurons with a preference to operate in synchrony.
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A two dimensional computational fluid dynamics simulation is used to develop low-dimensional models for estimator design.
In order to better define the spectrum of mixed states, it may be useful to develop a dimensional approach.
It is part of our suggestion here that the structure of CWA in its abstract intention continues to be useful to develop a multi-dimensional view of teamwork.
To improve location-estimation efficiency, it would be useful to develop an algorithm with high location accuracy and low computational complexity.
Additionally, these bounds can be useful to develop new algorithms.
The early phase images can be used to develop a three-dimensional (3D) vascular map.
These dimensions were used to develop a multi-dimensional assessment tool.
Another method described in [4] uses ideas from latent semantic analysis for cross lingual modeling to develop a single low-dimensional representation shared by words and documents in both languages.
To reduce the computational cost of turbulent combustion simulations with a detailed chemical mechanism, it is useful to find a low-dimensional manifold in composition space.
To allow analytical solutions, the methodology, using Pontryagin's minimum principle, is initially developed for low-dimensional non-linear systems that are affine in their input.
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