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
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A multi-agent evolutionary robotics framework to train spiking neural networks.
TL;DR: A novel multi-agent evolutionary robotics (ER) based framework, inspired by competitive evolutionary environments in nature, is demonstrated for training Spiking Neural Networks (SNN), and two evolutionary inheritance algorithms on the phenotypes, Mutation and Crossover with Mutation are demonstrated.
Journal ArticleDOI
Fine-tuning with local learning rules helps to compress and accelerate spiking neural networks without accuracy loss
Journal ArticleDOI
Towards New Generation, Biologically Plausible Deep Neural Network Learning
TL;DR: In this paper , a biologically plausible learning method is proposed to take advantage of various biological processes, such as Hebbian synaptic plasticity, and includes both supervised and unsupervised elements, and a series of experiments aimed at elucidating the advantages and disadvantages of the described biologically plausible Learning as compared with end-to-end backpropagation, and discuss the findings which should serve as a means of illuminating the algorithmic fundamentals of interest and directing future research.
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Spiking GATs: Learning Graph Attentions via Spiking Neural Network
Beibei Wang,Bo Jiang +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a graph spiking attention network (GSAT) for graph data representation and learning, which adopts a SNN module architecture which is obvious energy-efficient.
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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