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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Journal ArticleDOI
Demonstration of Integrate-and-fire Neuron Circuit for Spiking Neural Networks
Sung Yun-Woo,Won-Mook Kang,Young-Tak Seo,Soochang Lee,Dongseok Kwon,Seongbin Oh,Jong-Ho Bae,Jong-Ho Lee +7 more
TL;DR: In this paper , an integrate-and-fire (IF) neuron and a voltage level shifter circuit were fabricated and investigated for hardware-based SNN architectures, and the number of output spikes is 2, 5, 10, and 20 at tpulses of 0.4 μs, 1 μs and 4 μs.
Proceedings ArticleDOI
A Deep Spiking Convolutional Conversion Scheme for Robust Vertebrae Segmentation & Identification
TL;DR: In this article , the learned parameters of deep convolutional analog networks are transferred to equivalent-accurate spiking ones to avoid native spiking neural network (SNN) design.
Proceedings ArticleDOI
lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents
TL;DR: Li et al. as discussed by the authors proposed lpSpikeCon, a methodology to enable low-precision SNN processing for efficient unsupervised continual learning on resource-constrained autonomous agents/systems.
Proceedings ArticleDOI
An Unsupervised Learning Algorithm for Deep Recurrent Spiking Neural Networks
TL;DR: In this article, the authors proposed a new unsupervised multi-spike learning rule and the recurrent spiking neural machine (RSNM) is trained by this rule, the complex spatiotemporal pattern of spike trains are learned.
Journal ArticleDOI
A Shallow SNN Model for Embedding Neuromorphic Devices in a Camera for Scalable Video Surveillance Systems
TL;DR: In this article , a shallow spiking neural network (SNN) model was proposed for person monitoring and worker support with a video surveillance system. But, the model was only implemented in a few neuromorphic devices.
References
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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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