Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
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
Spiking Neural Networks and online learning: An overview and perspectives
TL;DR: In this article, the authors present a comprehensive overview of the use of Spiking Neural Networks for online learning in non-stationary data streams and propose a new algorithm to adapt to these changes as fast as possible, while maintaining good performance scores.
Journal ArticleDOI
Artificial Neural Networks for Neuroscientists: A Primer.
TL;DR: This pedagogical Primer introduces artificial neural networks and demonstrates how they have been fruitfully deployed to study neuroscientific questions, and details how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research.
Journal ArticleDOI
Supervised learning in spiking neural networks: A review of algorithms and evaluations
TL;DR: This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively, and provides five qualitative performance evaluation criteria and presents a new taxonomy for supervisedLearning algorithms depending on these five performance evaluated criteria.
Journal ArticleDOI
Compound Fault Diagnosis of Gearboxes via Multi-label Convolutional Neural Network and Wavelet Transform
TL;DR: A novel compound fault diagnosis method of the gearbox is proposed by integrating convolutional neural network with wavelet transform (WT) and multi-label (ML) classification, namely WT-MLCNN, which can achieve higher accuracy than other existing methods in literatures.
Proceedings ArticleDOI
Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function
Iulia M. Comsa,Thomas Fischbacher,Krzysztof Potempa,Andrea Gesmundo,Luca Versari,Jyrki Alakuijala +5 more
TL;DR: In this paper, a spiking neural network model is proposed to encode information in the relative timing of individual neuron spikes and performs classification using the first output neuron to spike, which achieves similar accuracy to a fully connected conventional network.
References
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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.
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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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