回复：气味识别的局限性和神经形态嗅觉回路的泛化

IF 18.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Nature Machine Intelligence Pub Date : 2024-12-16 DOI:10.1038/s42256-024-00951-2

Roy Moyal, Nabil Imam, Thomas A. Cleland

引用次数: 0

摘要

回复N. Dennler等人。自然机器智能https://doi.org/10.1038/s42256-024-00952-1（2024）在他们的评论中，Dennler等人1提交他们已经发现了影响我们2020年论文中得出的一些结论的局限性，“神经形态嗅觉回路中的快速在线学习和强大回忆”2。具体来说，他们断言(1)我们使用的公共数据集受到传感器漂移和非随机测量协议的影响，(2)我们的神经形态外部丛状层（EPL）网络在气味重复呈现的泛化能力方面受到限制，(3)我们的EPL网络结果可以通过使用计算效率更高的距离度量来匹配性能。尽管他们对公共数据局限性的描述是正确的，但在他们的前两个断言中，他们没有承认我们对这些数据的利用是如何避开这些局限性的。他们的第三个主张源于产生距离测量方法的缺陷。我们在下面依次回应这三种说法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Machine Intelligence Multiple-

CiteScore

36.90

自引率

2.10%

发文量

127

期刊介绍： Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.