The principles of “complementarity,” “cooperativity,” and “adaptive error control” in pattern learning and recognition: a physiological neural network model tested by computer-simulation
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The parallel ON-and OFF-center signals flowing from retina to brain suggest the operation of a complementarity mechanism. This paper shows what such a mechanism can do in higher-level visual processing. In the proposed mechanism, inhibition and excitation, both feedforward, coequally compete within each hierarchical level to discriminate patterns. A computer model tests complementarity in the context of an adaptive, self-regulating system. Three other mechanisms (gain control, cooperativity, and adaptive error control) are included in the model but are described only briefly. Results from simulations show that complementarity markedly improves both speed and accuracy in pattern learning and recognition. This mechanism may serve not only vision but other types of processing in the brain as well.