Neuronic convolution model for spatiotemporal information representation and processing

Author

Fu, Li-Yun ; Li, Yanda

Author_Institution

CSIRO, Bentley, WA, Australia

Volume

4

fYear

2001

fDate

2001

Firstpage

2614

Abstract

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

Keywords

backpropagation; convolution; multilayer perceptrons; neural nets; spatial data structures; wavelet transforms; AI; artificial intelligence; back propagation; backpropagation; convolutional neuron; convolutional neuron-based multilayer network; neural wavelets; neuronic convolution model; receptive field profile; spatiotemporal information processing; spatiotemporal information representation; spatiotemporal pattern processing; Apertures; Artificial neural networks; Convolution; Frequency; Information representation; Integral equations; Kernel; Multi-layer neural network; Neurons; Spatiotemporal phenomena;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.938782

Filename

938782