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
Resilience Analysis of Distributed Wireless Spiking Neural Networks
TL;DR: In this paper , a Distributed Wireless SNN (DW-SNN) system is considered and its performance in terms of inference accuracy and total neural activity when radio losses are applied to spikes transferred during the inference phase.
Journal ArticleDOI
MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks
TL;DR: This study presents a realistic approach for bio-inspired spike activity with MAP, presenting a novel neuromorphic perspective for incorporating biological properties into spiking neural networks.
Journal ArticleDOI
Spiking neural networks for nonlinear regression
TL;DR: A framework for regression using spiking neural net- works is proposed, and it is shown that SNNs can accurately model materials that are stressed beyond reversibility, which is a challenging type of non-linearity.
Journal ArticleDOI
Self-supervised representation learning for detection of ACL tear injury in knee MR videos
TL;DR: In this paper , a self-supervised learning approach was proposed to learn transferable features from MR video clips by enforcing the model to learn anatomical features and achieved an accuracy of 76.62% and an AUC score of 0.848 on the Sagittal plane.
Journal ArticleDOI
A dual-memory architecture for reinforcement learning on neuromorphic platforms
TL;DR: In this article, the authors describe a flexible architecture to carry out reinforcement learning on neuromorphic platforms, which is implemented using an Intel neuromorphic processor and demonstrated solving a variety of tasks using spiking dynamics.
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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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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