ArticleComputational intelligence and neuroscience2021
Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 8 citations in OpenAlex.
- Supervised Learning Algorithm Based on Spike Train Inner Product for Deep Spiking Neural Networks.Brain sciences · 2023Article
- A Novel Approach of Feature Space Reconstruction with Three-Way Decisions for Long-Tailed Text Classification.Computational intelligence and neuroscience · 2022Article
- Machine Learning-Based Shear Wave Elastography Elastic Index (SWEEI) in Predicting Cervical Lymph Node Metastasis of Papillary Thyroid Microcarcinoma: A Comparative Analysis of Five Practical Prediction Models.Cancer management and research · 2022Article
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3 authors at 1 institution in 1 country.
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Abstract
As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking neural networks. In this paper, we present a supervised learning algorithm for multilayer feedforward spiking neural networks; all neurons can fire multiple spikes in all layers. The feedforward network consists of spiking neurons governed by biologically plausible long-term memory spike response model, in which the effect of earlier spikes on the refractoriness is not neglected to incorporate adaptation effects. The gradient descent method is employed to derive synaptic weight updating rule for learning spike trains. The proposed algorithm is tested and verified on spatiotemporal pattern learning problems, including a set of spike train learning tasks and nonlinear pattern classification problems on four UCI datasets. Simulation results indicate that the proposed algorithm can improve learning accuracy in comparison with other supervised learning algorithms.
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