用于嗅觉感知的高效神经形态-贝叶斯混合模型：检测与分类
Efficient Hybrid Neuromorphic-Bayesian Model for Olfaction Sensing: Detection and Classification
AI总结：
本文提出卷积脉冲神经网络与贝叶斯脉冲神经网络结合的混合模型，用于机器人嗅觉检测与分类，在保证准确率的同时显著提升能效。
摘要：
自主机器人中的嗅觉感知面临动态运行、能源效率和边缘处理方面的挑战。这需要一种机器学习算法，能够应对现实世界的气味干扰，确保移动机器人的资源效率，并准确估计气体特征，以完成气味建图、定位和警报生成等关键任务。本文提出一种混合方法，利用神经形态计算与概率推理相结合来满足这些严苛要求。我们的方法实现了卷积脉冲神经网络用于特征提取，以及贝叶斯脉冲神经网络用于气味检测与识别。所开发的算法在传感器漂移补偿数据集上进行了严格测试，以评估鲁棒性。此外，为评估效率，我们在相同数据集和运行条件下，将模型的能耗与非脉冲机器学习算法进行了比较。我们的方法在表现出卓越效率的同时，取得了可比的准确率结果。
英文摘要：
Olfaction sensing in autonomous robotics faces challenges in dynamic operations, energy efficiency, and edge processing. It necessitates a machine learning algorithm capable of managing real-world odor interference, ensuring resource efficiency for mobile robotics, and accurately estimating gas features for critical tasks such as odor mapping, localization, and alarm generation. This paper introduces a hybrid approach that exploits neuromorphic computing in combination with probabilistic inference to address these demanding requirements. Our approach implements a combination of a convolutional spiking neural network for feature extraction and a Bayesian spiking neural network for odor detection and identification. The developed algorithm is rigorously tested on a dataset for sensor drift compensation for robustness evaluation. Additionally, for efficiency evaluation, we compare the energy consumption of our model with a non-spiking machine learning algorithm under identical dataset and operating conditions. Our approach demonstrates superior efficiency alongside comparable accuracy outcomes.
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