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.

神经形态计算是一种能够显著降低机器学习和人工智能功耗的少数几种方法之一。本文提出了一种在神经型架构上运行的受哺乳动物嗅球电路启发的气味学习算法，并在评估其对不同气味感知的快速在线学习和识别性能时，使用了一组气体传感器的记录数据并通过脉冲噪声进行破坏。本文复制了部分研究，并发现了影响某些结论的局限性，其包括使用的数据集存在传感器漂移和非随机测量协议，从而在气味识别基准测试中具有有限的用途。此外，我们发现该模型在对相同气体的重复展示上具有推广能力的限制。我们证明该研究所涉及的任务可以通过简单的哈希表方法解决，匹配或超过了报告中的准确性和运行时间。因此，需要显示模型的验证超出恢复学习数据样本的范畴，特别是其对气味识别任务的适用性。

神经形态嗅觉电路中的气味识别和推广的局限性

Limitations in odour recognition and generalisation in a neuromorphic  olfactory circuit

The proliferation of IoT sensors and their deployment in various industries
and applications has brought about numerous analysis opportunities in this Big
Data era. However, drift of those sensor measurements poses major challenges to
automate data analysis and the ability to effectively train and deploy models
on a continuous basis. In this paper we study and test several approaches from
the literature with regard to their ability to cope with and adapt to sensor
drift under realistic conditions. Most of these approaches are recent and thus
are representative of the current state-of-the-art. The testing was performed
on a publicly available gas sensor dataset exhibiting drift over time. The
results show substantial drops in sensing performance due to sensor drift in
spite of the approaches. We then discuss several issues identified with current
approaches and outline directions for future research to tackle them.

本文研究了多种机器学习方法处理各种行业和应用中传感器漂移问题的能力，并在公开可获得的气体传感器数据集上测试了这些方法，结果表明这些方法在传感器漂移问题上存在明显的性能下降。