Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
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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
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Journal ArticleDOI
Training Full Spike Neural Networks via Auxiliary Accumulation Pathway
TL;DR: Peng et al. as mentioned in this paper proposed a DualStream Training (DST) method which adds a detachable auxiliary accumulation path (AAP) to the full spiking residual networks to compensate for the information loss during the forward and backward propagation, and facilitate the training of the FSNN.
Book ChapterDOI
Coupled Oscillator Networks for von Neumann and Non-von Neumann Computing
TL;DR: In this paper, a general framework based on the phase equation derived from the description of nonlinear weakly coupled oscillators especially useful for computing applications is presented, which can be potentially employed as building blocks for both von Neumann and non-von Neumann architectures.
Journal ArticleDOI
Hyperspectral Image Band Selection Based on CNN Embedded GA (CNNeGA)
TL;DR: In this article , a deep network with 3D-convolutional layers embedded in a GA was used to select the required number of spectral bands in hyperspectral images (HSIs).
Journal ArticleDOI
Overview of Memristor-Based Neural Network Design and Applications
Longcheng Ye,Zhi Kui Gao,Jinke Fu,Wang Ren,Ci-Hui Yang,Jing Wen,Xiang Wan,Qingying Ren,Shipu Gu,Xiaoyan Liu,Xiaojuan Lian,Lei Wang +11 more
TL;DR: The potential approaches for overcoming the physical limitations of memristor-based neural networks and the outlook of Memristor applications on emerging neural networks are envisioned.
Journal ArticleDOI
A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model
Daniel Casanueva-Morato,Alvaro Ayuso-Martinez,Juan Pedro Dominguez-Morales,Angel Jimenez-Fernandez,Gabriel Jimenez-Moreno +4 more
TL;DR: This work presents the first hardware implementation of a fully functional bio-inspired spike-based hippocampus memory model, paving the road for the development of future more complex neuromorphic systems.
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.
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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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