Abstract
The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses.
Highlights
The need to detect the presence of hazardous volatile organic compounds (VOCs) first arose during the Industrial Revolution and sparked research in gas sensing technology where, initially, the gas sensors were mainly implemented as mechanical devices [1,2]
The to board operated in two modes: adaptation operating are adjusted classification algorithm was implemented on the microprocessor and a predefined baseline voltage; and sensing mode, in which analogue voltage output is translated to an accuracy excess of 95%
This study demonstrated the importance of global feed-forward inhibition, showing that odour discriminability can be enhanced by increasing the vector-angles between odour pairs
Summary
The need to detect the presence of hazardous volatile organic compounds (VOCs) first arose during the Industrial Revolution and sparked research in gas sensing technology where, initially, the gas sensors were mainly implemented as mechanical devices [1,2]. Pre-processing techniques such as dimensionality reduction and feature extraction can be implemented in conjunction with conventional pattern recognition methods for the classification and identification of odours [11,12]. These data-intensive processes require substantial computational power and tend to incur considerable latency, which hinders the real-time operation of the e-nose systems. Spike-based neuromorphic approaches have led to the development of novel processing solutions for sensor systems, especially for vision and auditory applications [13,14]. Low-power spike-based processing and the ability to embed learning algorithms underpin the application of neuromorphic systems for the development of robust real-time e-noses. We review and analyse major contributions in both conventional and neuromorphic e-noses to identify current trends and the scope for future developments
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