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Modeling neural networks is a powerful approach to uncover the mechanism of the brain, and the results of the research are ready to use for engineering applications. This paper introduces several models for vision from recent works by the author. (1) Increased recognition rate of the neocognitron: to increase the recognition rate of the neocognitron, several modifications have been applied to the network architecture and the learning method. For example, an inhibitory surround in the connections to C-cells is useful for this purpose. When trained with 3000 patterns, the neocognitron shows a recognition rate of 98.6% for a blind test set randomly sampled from a large database of handwritten digits (ETL-1), and 100% for the training set. (2) A neocognitron that can accept incremental learning, without giving severe damage to old memories or reducing learning speed: It uses competitive learning, and the learning of all stages of the hierarchical network progresses simultaneously. (3) A model that. has an ability to recognize and restore partly occluded patterns: even the identical image is perceived differently by human beings depending on the shape of occluding objects. The model responds in a similar way to human beings. It is a multi-layered hierarchical neural network, in which visual information is processed by interaction of bottom-up and top-down signals. Occluded parts of a pattern are restored mainly by feedback signals from the highest stage of the network, while the unoccluded parts are reproduced mainly by signals from lower stages. The model does not use a simple template matching method. It can recognize and restore even deformed versions of learned patterns.