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Neural Networks
Volume 14, Issue 9, November 2001, Pages 1173-1180
 
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doi:10.1016/S0893-6080(01)00091-0    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2001 Elsevier Science Ltd. All rights reserved.

Contributed article

An Infomax-based learning rule that generates cells similar to visual cortical neurons

K. OkajimaCorresponding Author Contact Information, E-mail The Corresponding Author

Fundamental Research Laboratories, System Device and Fundamental Research, NEC Corporation, 34 Miyukigaoka, Tsukuba, Ibaraki 305-8501, Japan

Received 18 December 2000;
revised 18 May 2001;
accepted 18 May 2001
Available online 8 October 2001.

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Abstract

A learning rule for a visual neural network is derived according to an information-maximization approach. Each basic module of the assumed neural network consists of two simple units and one complex unit. Each simple unit calculates a linear summation of its input by using its synaptic weights, and the complex unit calculates a squared sum of the outputs of the simple units. The learning algorithm updates the synaptic weights of simple units so that the information obtained from the output of the complex unit is increased. Simulation of the algorithm showed that it generates Gabor-wavelet-like weights similar to those observed in visual cortical neurons (simple cells). It also showed that, after the training, the responses of the complex unit are similar to those reported for a complex cell.

Author Keywords: Infomax; Visual cortex; Learning; Energy model; Simple cell; Complex cell

Article Outline

1. Introduction
2. Neural network model
3. Learning algorithm
3.1. Learning algorithm for a module
3.2. Interaction between modules
4. Simulations
5. Discussion
Acknowledgements
Appendix A. MI[C;Γ] in low SNR limit [derivation of Eq. (8)]
References











Neural Networks
Volume 14, Issue 9, November 2001, Pages 1173-1180
 
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