Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
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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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Proceedings ArticleDOI
Image Recognition Algorithm Based on Spiking Neural Network
TL;DR: In this article , the authors used the spiking neural network to construct a new neural network and applied it in the field of image classification, which achieved very remarkable results in image recognition.
Proceedings ArticleDOI
SFTA: Spiking Neural Networks Vulnerable to Spiking Feature Transferable Attack
Xuanwei Lin,Chen Dong,Ximeng Liu +2 more
TL;DR: Wang et al. as discussed by the authors proposed a transfer-based black box adversarial attack against spike feature space representations, which is explicitly designed to be transferable, and more robust feature gradients are obtained by accumulating the feature gradient of the intermediate layers.
Journal ArticleDOI
PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks
TL;DR: In this article , the authors proposed a novel learning algorithm inspired by predictive coding theory and showed that it can perform supervised learning fully autonomously and successfully as the backpropagation, utilizing only local Hebbian plasticity.
Proceedings ArticleDOI
Genetic Algorithmic Parameter Optimisation of a Recurrent Spiking Neural Network Model
TL;DR: In this article, the authors investigated the use of GAs to search for optimal parameters in recurrent SNNs to reach targeted neuronal population firing rates, e.g. as in experimental observations.
References
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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
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Proceedings Article
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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