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
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks
TL;DR: In this article, a deep convolutional spiking neural network (DCSNN) and a latency-coding scheme were used to address the limitations of deep artificial neural networks, which have revolutionized the computer vision domain.
Journal ArticleDOI
Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron.
TL;DR: S4NN as discussed by the authors proposes a rank-order-coding-based learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rankorder coding, where neurons fire exactly one spike per stimulus, but the firing order carries information.
Journal ArticleDOI
The building blocks of a brain-inspired computer
Jack D. Kendall,Suhas Kumar +1 more
TL;DR: This review points to the important primitives of a brain-inspired computer that could drive another decade-long wave of computer engineering.
Journal ArticleDOI
Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System.
Byung Chul Jang,Sungkyu Kim,Sang Yoon Yang,Jihun Park,Jun-Hwe Cha,Jungyeop Oh,Junhwan Choi,Sung Gap Im,Vinayak P. Dravid,Sung-Yool Choi +9 more
TL;DR: It is demonstrated that the transition of the operation mode in poly(1, 3,5-trivinyl-1,3,5 -trimethyl cyclotrisiloxane) (pV3D3)-based flexible memristor from conventional binary to synaptic analog switching can be achieved simply by reducing the size of the formed filament.
Journal ArticleDOI
Neuromorphic Engineering: From Biological to Spike‐Based Hardware Nervous Systems
TL;DR: Fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed and an overview is provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike‐based neuromorphic computing platforms.
References
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Proceedings ArticleDOI
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.
Proceedings Article
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.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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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