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Constraints on Hebbian and STDP learned weights of a spiking neuron

Chu, Dominique, Nguyen, Huy Le (2021) Constraints on Hebbian and STDP learned weights of a spiking neuron. Neural Networks, 135 . pp. 192-200. ISSN 0893-6080. (doi:10.1016/j.neunet.2020.12.012) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:85315)

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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.

Item Type: Article
DOI/Identification number: 10.1016/j.neunet.2020.12.012
Uncontrolled keywords: Hebbian learning, Spike-timing dependent plasticity, Stochastic systems, Novelty detection, MNIST
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Huy Nguyen
Date Deposited: 04 Jan 2021 23:38 UTC
Last Modified: 16 Feb 2021 14:17 UTC
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