About: Constraints on Hebbian and STDP learned weights of a spiking neuron

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #85315 Constraints on Hebbian and STDP learned weights of a spiking neuron
sameAs	Constraints on Hebbian and STDP learned weights of a spiking neuron
http://eprints.org/ontology/hasAccepted	Constraints on Hebbian and STDP learned weights of a spiking neuron (PDF)
http://eprints.org/ontology/hasDocument	Constraints on Hebbian and STDP learned weights of a spiking neuron (PDF) Constraints on Hebbian and STDP learned weights of a spiking neuron (Other) Constraints on Hebbian and STDP learned weights of a spiking neuron (Other) Constraints on Hebbian and STDP learned weights of a spiking neuron (Other) Constraints on Hebbian and STDP learned weights of a spiking neuron (Other) Constraints on Hebbian and STDP learned weights of a spiking neuron (Other)
dc:hasVersion	Constraints on Hebbian and STDP learned weights of a spiking neuron (PDF)
Title	Constraints on Hebbian and STDP learned weights of a spiking neuron
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2021-01-02
Creator	Dominique Chu Huy Le Nguyen
status	peer reviewed published
Publisher	Elsevier
abstract	We analyse mathematically the constraints on weights resulting from Hebbian and STDP learning rules applied to a spiking neuron with weight normalisation. In the case of pure Hebbian learning, we find that the normalised weights equal the promotion probabilities of weights up to correction terms that depend on the learning rate and are usually small. A similar relation can be derived for STDP algorithms, where the normalised weight values reflect a difference between the promotion and demotion probabilities of the weight. These relations are practically useful in that they allow checking for convergence of Hebbian and STDP algorithms. Another application is novelty detection. We demonstrate this using the MNIST dataset.
Is Part Of	Neural Networks https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/85315#authors
volume	135
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #85315 Constraints on Hebbian and STDP learned weights of a spiking neuron

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