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
Towards Energy-Preserving Natural Language Understanding With Spiking Neural Networks
TL;DR: In this article , a spiking encoder was proposed to alleviate the bottleneck of neural-based NLU models by transforming numeric values into discrete spiking signals and replacing massive multiplications with much cheaper additive operations.
Journal ArticleDOI
SaARSP: An Architecture for Systolic-Array Acceleration of Recurrent Spiking Neural Networks
TL;DR: SaARSP as mentioned in this paper decouples the processing of feed-forward synaptic connections from that of recurrent connections to allow for the exploitation of parallelism across multiple time points, and further explore the temporal granularity of the proposed decoupling in terms of optimal time window size and reconfiguration of the systolic array considering layer-dependent connectivity to boost performance.
S piking c onvolutional n eural n etworks for t ext c lassification
TL;DR: The authors proposed to encode pre-trained word embeddings as spike trains and showed empirically that converted SNNs achieve comparable results to their DNN counterparts with much less energy consumption across multiple datasets for both English and Chinese.
Proceedings ArticleDOI
Recognition of Bengali Handwritten Digits Using Spiking Neural Network Architecture
Journal ArticleDOI
A systematic comparison of deep learning methods for EEG time series analysis
TL;DR: In this paper , the authors compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline, and show that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement.
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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