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Matching animal-like flexibility in recognition and the ability to quicklyincorporate new information remains difficult. Limits are yet to be adequatelyaddressed in neural models and recognition algorithms. This work proposes aconfiguration for recognition that maintains the same function of conventionalalgorithms but avoids combinatorial problems. Feedforward recognitionalgorithms such as classical artificial neural networks and machine learningalgorithms are known to be subject to catastrophic interference and forgetting.Modifying or learning new information (associations between patterns andlabels) causes loss of previously learned information. I demonstrate usingmathematical analysis how supervised generative models, with feedforward andfeedback connections, can emulate feedforward algorithms yet avoid catastrophicinterference and forgetting. Learned information in generative models is storedin a more intuitive form that represents the fixed points or solutions of thenetwork and moreover displays similar difficulties as cognitive phenomena.Brain-like capabilities and limits associated with generative models suggestthe brain may perform recognition and store information using a similarapproach. Because of the central role of recognition, progress understandingthe underlying principles may reveal significant insight on how to better studyand integrate with the brain.

Supervised Generative Reconstruction: An Efficient Way To Flexibly Store and Recognize Patterns