About: Integrate-and-Fire Neurons for Low-Powered Pattern Recognition

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #89756 Integrate-and-Fire Neurons for Low-Powered Pattern Recognition
sameAs	Integrate-and-Fire Neurons for Low-Powered Pattern Recognition
http://eprints.org/ontology/hasAccepted	Integrate-and-Fire Neurons for Low-Powered Pattern Recognition (PDF)
http://eprints.org/ontology/hasDocument	Integrate-and-Fire Neurons for Low-Powered Pattern Recognition (PDF) Integrate-and-Fire Neurons for Low-Powered Pattern Recognition (Other) Integrate-and-Fire Neurons for Low-Powered Pattern Recognition (Other) Integrate-and-Fire Neurons for Low-Powered Pattern Recognition (Other) Integrate-and-Fire Neurons for Low-Powered Pattern Recognition (Other) Integrate-and-Fire Neurons for Low-Powered Pattern Recognition (Other)
dc:hasVersion	Integrate-and-Fire Neurons for Low-Powered Pattern Recognition (PDF)
Title	Integrate-and-Fire Neurons for Low-Powered Pattern Recognition
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2021-10-05
Creator	Dominique Chu Florian Bacho
status	non peer reviewed published
Publisher	Springer
abstract	Embedded systems acquire information about the real world from sensors and process it to make decisions and/or for transmission. In some situations, the relationship between the data and the decision is complex and/or the amount of data to transmit is large (e.g. in biologgers). Artificial Neural Networks (ANNs) can efficiently detect patterns in the input data which makes them suitable for decision making or compression of information for data transmission. However, ANNs require a substantial amount of energy which reduces the lifetime of battery-powered devices. Therefore, the use of Spiking Neural Networks can improve such systems by providing a way to efficiently process sensory data without being too energy-consuming. In this work, we introduce a low-powered neuron model called Integrate-and-Fire which exploits the charge and discharge properties of the capacitor. Using parallel and series RC circuits, we developed a trainable neuron model that can be expressed in a recurrent form. Finally, we trained its simulation with an artificially generated dataset of dog postures and implemented it as hardware that showed promising energetic properties.
Is Part Of	Financial Cryptography and Data Security https://kar.kent.ac.uk/id/repository
Subject	QA 76 Software, computer programming,
list of authors	https://kar.kent.ac.uk/id/eprint/89756#authors
presented at	20th International Conference on Artificial Intelligence and Soft Computing
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #89756 Integrate-and-Fire Neurons for Low-Powered Pattern Recognition

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