Circuit and method of error correcting with an artificial neural

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395 21, 395 23, G06E 100, G06E 300, G06F 1518

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active

056320060

ABSTRACT:
An artificial neural network performs error correction on an input signal vector. The input signal vector is process in a forward direction through synapses in each of a plurality of neurons for providing an output signal from each of the neurons. The output signals from the neurons are monitored until the one having the greatest activity level is identified. A reverse flow signal having a predetermined magnitude is processed in the reverse direction through the neuron having the greatest activity level for updating the input signal vector. Alternately, the output signals of competing neurons may be applied through synapses weighted to favor the neuron having the greatest output signal activity. Thus, the neuron with synapses most closely matched to the elements of the input signal vector overpowers the remaining neurons and wins the competition. Once the winning neuron is identified, its output signal is reverse processed through the respective neuron for providing an improved input signal vector with reduced noise and error. The improved signal is reprocessed in the forward direction through the neural network for providing higher confidence output signals.

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