The spiked random neural network: nonlinearity, learning and approximation

We summarize the theoretical foundations of the random neural network model (RNN) and of its learning algorithm, and present a relevant bibliography of its theory and applications. Many applications have resulted from this model, including its use in still image and video compression which has achie...

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Bibliographic Details
Main Author: Gelenbe, E.
Format: Conference Proceeding
Language:English
Subjects:
Bibliographies
Biological neural networks
Gray-scale
Image coding
Image segmentation
Magnetic resonance imaging
Neural networks
PSNR
Recurrent neural networks
Video compression
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Summary:We summarize the theoretical foundations of the random neural network model (RNN) and of its learning algorithm, and present a relevant bibliography of its theory and applications. Many applications have resulted from this model, including its use in still image and video compression which has achieved compression ratios of up to 500:1 for moving gray-scale images, with 30db PSNR quality levels. Another application of the RNN is to image segmentation; the recurrent feature of the network has been used to extract precise morphometric information from magnetic resonance imaging (MRI) scans of the human brain. The RNN has also been successfully applied to optimization and image texture analysis and reconstruction.
DOI:10.1109/CNNA.1998.685674