doi:10.1016/S0893-6080(01)00091-0
Copyright © 2001 Elsevier Science Ltd. All rights reserved.
Contributed article
An Infomax-based learning rule that generates cells similar to visual cortical neurons
K. Okajima
, 
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
Fig. 1. Neural network model adopted in this paper.
Fig. 2. A basic module of the model neural network.
Fig. 3. An example of a quadrature pair of Gabor functions.
Fig. 4. Weight patterns generated through learning. A periodic boundary condition is used. An example of the training image is shown in Fig. 9(a).
Fig. 5. Weight patterns of two simple cells in a module after the learning is completed.
Fig. 6. Responses of a complex cell after the learning is completed: (a) response to a grating pattern as a function of its orientation; (b) responses to a spot of light. A bright point indicates that the cell's response is strong when a spot of light is shown at that point; (c) responses to a grating pattern of the optimal orientation as a function of its position (γ: wave length of the grating).
Fig. 7. Weight patterns generated through learning. A periodic boundary condition is used. An example of the training image is shown in Fig. 9(b).
Fig. 8. Weight patterns generated through learning using real images for training data.
Fig. 9. Computer-generated images [(a), (b)] and real images (c) used for the training.
Fig. 10. ∂H/∂κ41 as a function of ς′≡(2ς−1)2.