  Neuromorphic computing is one of the few current approaches that have the
potential to significantly reduce power consumption in Machine Learning and
Artificial Intelligence. Imam & Cleland presented an odour-learning algorithm
that runs on a neuromorphic architecture and is inspired by circuits described
in the mammalian olfactory bulb. They assess the algorithm's performance in
"rapid online learning and identification" of gaseous odorants and odorless
gases (short "gases") using a set of gas sensor recordings of different odour
presentations and corrupting them by impulse noise. We replicated parts of the
study and discovered limitations that affect some of the conclusions drawn.
First, the dataset used suffers from sensor drift and a non-randomised
measurement protocol, rendering it of limited use for odour identification
benchmarks. Second, we found that the model is restricted in its ability to
generalise over repeated presentations of the same gas. We demonstrate that the
task 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 sample
remains to be shown, in particular its suitability to odour identification
tasks.
