Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
TLDR
The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNN's typically require many fewer operations and are the better candidates to process spatio-temporal data.About:
This article is published in Neural Networks.The article was published on 2019-03-01 and is currently open access. It has received 756 citations till now. The article focuses on the topics: Spiking neural network & Artificial neural network.read more
Citations
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Journal ArticleDOI
Impact of the Sub-Resting Membrane Potential on Accurate Inference in Spiking Neural Networks.
TL;DR: The results in this paper indicate that it is essential for neurons to allow the sub-resting membrane potential in order to realize high-performance SNNs.
Journal ArticleDOI
The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future.
Daniela Lazăr,Mihaela Flavia Avram,Alexandra Faur,Adrian Goldis,I. Romosan,Sorina Tăban,Marioara Cornianu +6 more
TL;DR: An overview of the literature and the current knowledge of the usefulness of different types of machine learning systems in the assessment of premalignant and malignant esophageal lesions via conventional and advanced endoscopic procedures is presented.
Journal ArticleDOI
Toward Reflective Spiking Neural Networks Exploiting Memristive Devices
TL;DR: This study forecast that spiking neural networks (SNNs) can achieve the next qualitative leap and predicts that Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions.
Proceedings ArticleDOI
Spiking Graph Convolutional Networks
TL;DR: SpikingGCN is proposed, an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs, and can bring a clear advantage of energy efficiency into graph data analysis, which demonstrates its great potential to construct environment-friendly machine learning models.
Book ChapterDOI
SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking
Yihao Luo,Min Xu,Caihong Yuan,Caihong Yuan,Xiang Cao,Liangqi Zhang,Yan Xu,Tianjiang Wang,Qi Feng +8 more
TL;DR: SiamSNN as discussed by the authors proposes an optimized hybrid similarity estimation method to exploit temporal information in the SNNs, and introduces a novel two-status coding scheme to optimize the temporal distribution of output spike trains for further improvements.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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ImageNet Classification with Deep Convolutional Neural Networks
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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