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Traditional connectionist models place an emphasis on learned weights. Based on neurobiological evidence, a new approach is developed and experimentally shown to be more robust for disambiguating novel combinations of stimuli. It does not require variable weights and avoids many training related issues. This approach is compared with traditional weight-learning methods. The network is better able to function in different scenarios and can recognize multiple stimuli even if it is only trained on single stimuli.