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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Proceedings ArticleDOI
Q-SpiNN: A Framework for Quantizing Spiking Neural Networks
TL;DR: Q-SpiNN as mentioned in this paper employs quantization for different SNN parameters based on their significance to the accuracy, and develops an algorithm that quantifies the benefit of the memory-accuracy trade-off obtained by the candidates, and selects the Pareto-optimal one.
Journal ArticleDOI
An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation
TL;DR: In this article, a double exponential adaptive threshold (DEXPonential Adaptive Threshold) neuron model was proposed to improve the performance of neuromorphic RNNs by providing faster convergence, higher accuracy and a flexible long short-term memory.
Journal ArticleDOI
Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm
TL;DR: The experimental results show that TSA is the best in terms of mean classification rates and outperformed the opponents on 18 problems and the main disadvantage of BP is trapping into local minima.
Journal ArticleDOI
Constructing Accurate and Efficient Deep Spiking Neural Networks With Double-Threshold and Augmented Schemes.
TL;DR: This study provides new approaches for further integration of advanced techniques in ANNs to improve the performance of SNNs, and proposes two new conversion methods, namely TerMapping and AugMapping, which could be of great merit to applied developments with spike-based neuromorphic computing.
Posted Content
Fast and deep: energy-efficient neuromorphic learning with first-spike times
Julian Göltz,Laura Kriener,Andreas Baumbach,Sebastian Billaudelle,Oliver Breitwieser,Benjamin Cramer,Dominik Dold,Akos F. Kungl,Walter Senn,Johannes Schemmel,Karlheinz Meier,Mihai A. Petrovici +11 more
TL;DR: This work describes a rigorous derivation of learning first-spike times in networks of leaky integrate-and-fire neurons, relying solely on input and output spike times, and shows how it can implement error backpropagation in hierarchical spiking networks.
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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ImageNet Classification with Deep Convolutional Neural Networks
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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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