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Pattern recognition with Spiking Neural Networks: a simple ...
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由 F Christophe 著作被引用 14 次 — To a certain extent, the simple training method proposed in this paper follows the idea of training only the output layer as STDP will act alone for reinforcing.
13 頁
(PDF) Pattern recognition with Spiking Neural Networks
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2016年3月15日 — This paper addresses the learnability of software in the context of platforms integrating biological components. A method for training Spiking ...
[PDF] Pattern recognition with spiking neural networks
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This paper addresses the learnability of software in the context of platforms integrating biological components. A method for training Spiking Neural Networks ( ...
Pattern Recognition with Spiking Neural Networks
Springer
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由 IT Paz 著作2013被引用 6 次 — This manuscript is focused on some applications of method Spikeprop of Spiking Neural Networks (SNN) using an especific hardware for parallel programming in ...
Pattern recognition with spiking neural networks and the ...
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由 A Ternstedt 著作被引用 1 次 — The main focus of this project is to investigate spiking neural network models and im- plementations for a low-power sensor system that can ...
Pattern Recognition of Spiking Neural Networks Based on ...
National Institutes of Health (NIH) (.gov)
https://pmc.ncbi.nlm.nih.gov › articles
National Institutes of Health (NIH) (.gov)
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由 X Li 著作2020被引用 8 次 — We propose a hierarchical spiking neural network (SNN) for image classification. Grayscale input images are fed through a feed-forward network consisting of ...
Pattern recognition in Spiking Neural Nets using Brian2
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2020年9月11日 — I would like to work on implementing simple neuron models used for pattern recognition from research papers.
Training Spiking Neural Networks with Local Tandem ...
PolyU Institutional Research Archive
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PolyU Institutional Research Archive
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由 Q Yang 著作2022被引用 38 次 — Progressive tandem learning for pattern recognition with deep spiking neural networks. IEEE. Transactions on Pattern Analysis and Machine Intelligence, 2021.
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Learning Spiking Neural Network from Easy to Hard task
arXiv
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arXiv
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由 L Tang 著作2023 — Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current. SNNs models treat all samples equally, which does not align with.
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SSF: Accelerating Training of Spiking Neural Networks with ...
CVF Open Access
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由 J Wang 著作2023被引用 4 次 — Surrogate gradient (SG) is one of the most effective approaches for training spiking neural networks (SNNs). While assisting SNNs to achieve classification ...
10 頁
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