Neuronic convolution model for spatiotemporal information representation and processing

How to represent spatiotemporal information in an artificial neuron model has been a problem of long-standing interest in artificial intelligence. After a brief review of recent advances, Caianiello's neuronic convolutional model (1961) is extended in this paper for spatiotemporal information r...

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Bibliographic Details
Main Authors: Li-Yun Fu, Yanda Li
Format: Conference Proceeding
Language:English
Subjects:
Apertures
Artificial neural networks
Convolution
Frequency
Information representation
Integral equations
Kernel
Multi-layer neural network
Neurons
Spatiotemporal phenomena
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Summary:How to represent spatiotemporal information in an artificial neuron model has been a problem of long-standing interest in artificial intelligence. After a brief review of recent advances, Caianiello's neuronic convolutional model (1961) is extended in this paper for spatiotemporal information representation. The kernel functions that correspond to the convolutional neuron's receptive field profile can be described by neural wavelets. The convolutional neuron-based multilayer network and its back propagation algorithm are developed to perform spatiotemporal pattern processing. The results provide a natural framework for the discussion of spatiotemporal information representation in an artificial neural network.
ISSN:1098-7576
1558-3902
DOI:10.1109/IJCNN.2001.938782