We then experimented with introducing a series of small variations in the overall system. For example, we would make perturbations (minor random changes) to the input. Another such change was to have adjacent Markov models “leak” into one another by causing the results of one Markov model to influence models that are “nearby.” Although we did not realize it at the time, the sorts of adjustments we were experimenting with are very similar to the types of modifications that occur in biological cortical structures. At first, such changes hurt performance (as measured by accuracy of recognition).
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