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Full-Text Articles in Engineering
Application Of Neuromorphic Olfactory Approach For High-Accuracy Classification Of Malts, Anup Vanarse, Adam Osseiran, Alexander Rassau, Peter Van Der Made
Application Of Neuromorphic Olfactory Approach For High-Accuracy Classification Of Malts, Anup Vanarse, Adam Osseiran, Alexander Rassau, Peter Van Der Made
Research outputs 2022 to 2026
Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on …
Application Of A Brain-Inspired Spiking Neural Network Architecture To Odor Data Classification, Anup Vanarse, Josafath Israel Espinosa-Ramos, Adam Osseiran, Alexander Rassau, Nikola Kasabov
Application Of A Brain-Inspired Spiking Neural Network Architecture To Odor Data Classification, Anup Vanarse, Josafath Israel Espinosa-Ramos, Adam Osseiran, Alexander Rassau, Nikola Kasabov
Research outputs 2014 to 2021
Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data, classification techniques based on the bio-computations of the higher brain areas, which process the spiking data for identification of odor, remain largely unexplored. This paper argues that brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data processing. Inspired by principles of brain information processing, here we propose the first spiking neural network method and associated deep machine …
Real-Time Classification Of Multivariate Olfaction Data Using Spiking Neural Networks, Arnup Vanarse, Adam Osseiran, Alexander Rassau, Therese O'Sullivan, Jonny Lo, Amanda Devine
Real-Time Classification Of Multivariate Olfaction Data Using Spiking Neural Networks, Arnup Vanarse, Adam Osseiran, Alexander Rassau, Therese O'Sullivan, Jonny Lo, Amanda Devine
Research outputs 2014 to 2021
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 encoding multi-variate data generated by artificial olfactory systems into temporal signatures. However, the utilization of traditional pattern-matching methods and unpredictable shuffling of spikes in the rank-order impedes the performance of the system. In this paper, we present an …