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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A Co-Designed Neuromorphic Chip With Compact (17.9K F<sup>2</sup>) and Weak Neuron Number-Dependent Neuron/Synapse Modules
TL;DR: In this article , the authors proposed a co-designed neuromorphic core (SRCcore) based on quantized spiking neural network (SNN) technology and compact chip design methodology.
Journal ArticleDOI
ChatGPT-Like Large-Scale Foundation Models for Prognostics and Health Management: A Survey and Roadmaps
Yanfang Li,Huan Wang,Muxia Sun +2 more
TL;DR: The emergence of large-scale foundation models (LSF-Models) such as ChatGPT and DALLE-E marks the entry of AI into a new era of AI-2.0 from AI-1.0 as mentioned in this paper .
Journal ArticleDOI
Encoding integers and rationals on neuromorphic computers using virtual neuron
TL;DR: In this article , a virtual neuron abstraction was proposed for encoding and adding integers and rational numbers by using spiking neural network primitives, and the virtual neuron could perform an addition operation using 23 nJ of energy on average with a mixed-signal, memristor-based neuromorphic processor.
Journal ArticleDOI
Spiking SiamFC++: Deep Spiking Neural Network for Object Tracking
Shuiying Xiang,Tao Zhang,Shuqing Jiang,Ya Nan Han,Yahui Zhang,Chenyang Du,Xing Xing Guo,Licun Yu,Yuechun Shi,Yue Hao +9 more
TL;DR: The performance of the Spiking SiamFC++ outperforms the existing state-of-the-art approaches in SNN-based object tracking, which provides a novel path for SNN application in the field of target tracking.
Proceedings ArticleDOI
Hand Gesture Recognition Using IR-UWB Radar with Spiking Neural Networks
TL;DR: This paper proposes a high-accuracy and low-power algorithm for hand gesture recognition using spiking neural networks (SNNs), which have more biological interpretability and are inherently suitable for processing time-series signals.
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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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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