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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An adaptive threshold mechanism for accurate and efficient deep spiking convolutional neural networks
TL;DR: In this article , an adaptive threshold mechanism for improved balance between weight and threshold of SNNs is proposed, which makes it possible to obtain as small a threshold as possible while distinguishing inputs, so as to generate sufficient firing to drive higher layers.
Journal ArticleDOI
E-prop on SpiNNaker 2: Exploring online learning in spiking RNNs on neuromorphic hardware
TL;DR: In this article , a biologically-inspired E-prop approach for training Spiking Recurrent Neural Networks (SRNNs) was proposed to solve the back propagation through time (BPTT) problem.
Journal ArticleDOI
Robust trajectory generation for robotic control on the neuromorphic research chip Loihi
TL;DR: In this article, the authors exploit a biologically-inspired spiking neural network model, the so-called anisotropic network, to generate complex robotic movements as a building block for robotic control using state of the art neuromorphic hardware.
Journal ArticleDOI
Neuromorphic device based on silicon nanosheets
Chenhao Wang,Xinyi Xu,Xiaodong Li,Mark D. Butala,Wen Huang,Lei Yin,Wenbing Peng,Munir Ali,S. C. Bodepudi,Xvsheng Qiao,Yang Xu,Wei Sun,De-Liang Yang +12 more
TL;DR: In this paper , the authors presented neuromorphic devices based on silicon nanosheets that are chemically exfoliated and surface modified, enabling self-assembly into hierarchical stacking structures, which can be switched between a unipolar memristor and a feasibly reset-able synaptic device.
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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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