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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Probabilistic Wind Power Forecasting Based on Spiking Neural Network
TL;DR: In this paper, the authors proposed a probabilistic forecasting method for wind power forecasting based on spiking neural network, which does not involve any distribution assumption of the prediction errors required by most existing forecasting methods.
Journal ArticleDOI
FSpiNN: An Optimization Framework for Memory-Efficient and Energy-Efficient Spiking Neural Networks
TL;DR: In this article, the authors proposed FSpiNN, an optimization framework for obtaining memory-efficient and energy-efficient SNNs for training and inference processing, with unsupervised learning capability while maintaining accuracy.
Journal ArticleDOI
Neuromorphic computing with antiferromagnetic spintronics
TL;DR: The prospects and challenges of antiferromagnetic spintronics for neuromorphic computing are discussed and overview and discussion are given on non-spiking artificial neural networks, spiking neural Networks, and reservoir computing.
Journal ArticleDOI
Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction
TL;DR: Gross morphological differences account for most of the univariate and multivariate sex differences in GMVOL and sex could be reliably predicted (> 80%) when using raw local GMVOL, but also when using scaling or proportions adjusted-data or TIV as a single predictor.
Journal ArticleDOI
Supervised Learning in Spiking Neural Networks with Synaptic Delay-Weight Plasticity
Malu Zhang,Malu Zhang,Jibin Wu,Ammar Belatreche,Zihan Pan,Xiurui Xie,Yansong Chua,Guoqi Li,Hong Qu,Haizhou Li,Haizhou Li +10 more
TL;DR: This paper investigates the viability of integrating synaptic delay plasticity into supervised learning and proposes a novel learning method that adjusts both the synaptic delays and weights of the learning neurons to make them fire precisely timed spikes, that is referred to as synaptic delay-weight plasticity.
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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