Learning invariance from transformation sequences
Neural Computation
Neural network model of the visual system: binding form and motion
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Self-organization of shift-invariant receptive fields
Neural Networks
Training Neocognitron to Recognize Handwritten Digits in the Real World
PAS '97 Proceedings of the 2nd AIZU International Symposium on Parallel Algorithms / Architecture Synthesis
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To capture and process visual information flexibly and efficiently from changing external world, the function of active and adaptive information processing is indispensable. Visual information processing in the brain can be interpreted as a process of eliminating irrelevant information from a flood of signals received by the retina. Selective attention is one of the essential mechanisms for this kind of active processing. Self-organization of the neural network is another important function for flexible information processing. This paper introduces some neural network models for these mechanisms from the works of the author: such as "recognition of partially occluded patterns", "recognition and segmentation of face with selective attention", "binding form and motion with selective attention" and "self-organization of shift-invariant receptive fields".