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Neuromorphic computing is one of the few current approaches that have thepotential to significantly reduce power consumption in Machine Learning andArtificial Intelligence. Imam & Cleland presented an odour-learning algorithmthat runs on a neuromorphic architecture and is inspired by circuits describedin the mammalian olfactory bulb. They assess the algorithm's performance in"rapid online learning and identification" of gaseous odorants and odorlessgases (short "gases") using a set of gas sensor recordings of different odourpresentations and corrupting them by impulse noise. We replicated parts of thestudy and discovered limitations that affect some of the conclusions drawn.First, the dataset used suffers from sensor drift and a non-randomisedmeasurement protocol, rendering it of limited use for odour identificationbenchmarks. Second, we found that the model is restricted in its ability togeneralise over repeated presentations of the same gas. We demonstrate that thetask the study refers to can be solved with a simple hash table approach,matching or exceeding the reported results in accuracy and runtime. Therefore,a validation of the model that goes beyond restoring a learned data sampleremains to be shown, in particular its suitability to odour identificationtasks.

Limitations in odour recognition and generalisation in a neuromorphic olfactory circuit