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In this paper, we propose a Star cellular neural network (Star CNN) for associative and dynamic memories. A Star CNN consists of local oscillators and a central system. All oscillators are connected to a central system in the shape of a Star, and communicate with each other through a central system. A Star CNN can store and retrieve given patterns in the form of synchronized chaotic states with appropriate phase relations between the oscillators (associative memories). Furthermore, the output pattern can occasionally travel around the stored patterns, their reverse patterns, and new relevant patterns which are called spurious patterns (dynamic memories).

An autoassociative memory is a device which accepts an input pattern and generates an output as the stored pattern which is most closely associated with the input. In this paper, we propose an autoassociative memory cellular neural network, which consists of one-dimensional cells with spatial derivative inputs, thresholds and memories. Computer simulations show that it exhibits good performance in face recognition: The network can retrieve the whole from a part of a face image, and can reproduce a clear version of a face image from a noisy one. For human memory, research on "visual illusions" and on "brain damaged visual perception", such as the Thatcher illusion, the hemispatial neglect syndrome, the split-brain, and the hemispheric differences in recognition of faces, has fundamental importance. We simulate them in this paper using an autoassociative memory cellular neural network. Furthermore, we generate many composite face images with spurious patterns by applying genetic algorithms to this network. We also simulate a morphing between two faces using autoassociative memory.