We present a feedback system where a picture u(t) circulates under successive application of a convolution operation
with the kernel h and of a nonlinearity NL:
t1(t + 1) = NL((u(t)*h)1) . (1)
This system represents a special class of neural networks: it is space invariant. In comparison to space variant neuronal
networks it can be implement much easier, for example by Fast Fourier Transform, or even optically. Nevertheless, it
exhibits a broad spectrum of behavior: there may be deterministic chaos in space and time, i. e. the system is unpredictable
in principle and displays no fixed points, or stable structures may evolve.' Certain convolution kernels may lead to the
evolution of stable structures, i. e. fixed points, that look like patterns from nature, for example like crystals or like
magnetic domains.' In experiments described in 2 we observed that the stable structures can be disturbed quite heavily and
are yet autoassociativly restored during a couple of iteration cycles. Here we show how to adjust the kernel h in order to
obtain a certain desired stable state u of eq. 1, and how to apply the system to shift invariant pattern recognition.
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