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The mammalian olfactory system learns rapidly from very few examples,presented in unpredictable online sequences, and then recognizes these learnedodors under conditions of substantial interference without exhibitingcatastrophic forgetting. We have developed a brain-mimetic algorithm thatreplicates these properties, provided that sensory inputs adhere to a commonstatistical structure. However, in natural, unregulated environments, thisconstraint cannot be assured. We here present a series of signal conditioningsteps, inspired by the mammalian olfactory system, that transform diversesensory inputs into a regularized statistical structure to which the learningnetwork can be tuned. This pre-processing enables a single instantiated networkto be applied to widely diverse classification tasks and datasets - hereincluding gas sensor data, remote sensing from spectral characteristics, andmulti-label hierarchical identification of wild species - without adjustingnetwork hyperparameters.

Signal Conditioning for Learning in the Wild