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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Proceedings ArticleDOI
Spiking Neurons with Differential Evolution Algorithm for Pattern Classification
TL;DR: A state-of-the-art manner, differential evolving spiking neural network (DESNN), is proposed for pattern classification, and the experimental results show that the algorithm used in this work applies the fewer neurons and it is effective forpattern classification tasks.
Proceedings ArticleDOI
Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks
TL;DR: In this paper, the authors proposed an energy-efficient SNN model with sparse spatio-temporal coding, which is based on re-parameterization of weights in an SNN and the application of sparsity regularization during optimization.
Journal ArticleDOI
Lessons from natural flight for aviation: then, now and tomorrow
TL;DR: This article reviewed the literature to identify key contributions that began in biology and have since been translated into aeronautical devices or capabilities, highlighting the importance of maintaining an open line of two-way communication between biologists and engineers.
Posted Content
Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1
David Berga,Xavier Otazu +1 more
TL;DR: In this article, a neurodynamic network of firing-rate neurons in order to predict visual attention was used to mimic the lateral interactions of V1 cells, which are responsible for bottom-up visual attention.
Journal ArticleDOI
Deep Spike Learning With Local Classifiers
TL;DR: This work proposes a spike-based efficient local learning rule by only considering the direct dependencies in the current time, and proposes two variants that additionally incorporate temporal dependencies through a backward and forward process, respectively.
References
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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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