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Real-Time Classification of Multivariate Olfaction Data Using Spiking Neural Networks
2019
Sensors
Recent studies in bioinspired artificial olfaction, especially those detailing the application of spike-based neuromorphic methods, have led to promising developments towards overcoming the limitations of traditional approaches, such as complexity in handling multivariate data, computational and power requirements, poor accuracy, and substantial delay for processing and classification of odors. Rank-order-based olfactory systems provide an interesting approach for detection of target gases by
doi:10.3390/s19081841
pmid:31003417
pmcid:PMC6515392
fatcat:4g6fdvo5ofeevkuku6lewnt7hi