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.

翻译：神经形态计算是当前少数有潜力显著降低机器学习与人工智能能耗的方法之一。Imam与Cleland提出了一种基于神经形态架构的气味学习算法，该算法受哺乳动物嗅球回路启发。他们利用一组不同气味呈现的气体传感器记录数据，并通过脉冲噪声进行污染，评估了该算法在气态气味剂与无味气体（简称"气体"）的"快速在线学习与识别"中的性能。我们重复了部分研究并发现了一些影响部分结论的局限性。首先，所用数据集存在传感器漂移且测量协议非随机化，这限制了其在气味识别基准测试中的有效性。其次，我们发现该模型对同种气体重复呈现的泛化能力受限。我们证明该研究所涉及的任务可通过简单的哈希表方法解决，其在准确率和运行时间上均能达到或超过所报告的结果。因此，该模型超越恢复已学习数据样本的验证仍有待证实，尤其其在气味识别任务中的适用性仍需进一步确认。