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
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Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting
TL;DR: In this paper , a bioinspired machine vision system based on a 2D phototransistor array fabricated from large-area monolayer molybdenum disulfide (MoS2) and integrated with an analog, nonvolatile, and programmable memory gate-stack is demonstrated.
Journal ArticleDOI
Memristor‐Based Intelligent Human‐Like Neural Computing
TL;DR: In this paper , the authors reviewed the biological nervous system and memristor-based nervous system thoroughly, including the structures and also the functions, and the difficulties that need to be overcome, and future development prospects are also discussed.
Posted Content
ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories.
TL;DR: In this paper, the authors propose ReSpawn, a framework for mitigating the negative impacts of faults in both the off-chip and on-chip memories for resilient and energy-efficient SNNs.
Neuron-Inspired Time-of-Flight Sensing via Spike-Timing-Dependent Plasticity of Artificial Synapses
Minseong Park,Yuan Yuan,Yongmin Baek,Andrew H. Jones,Nicholas Lin,Doeon Lee,Hee Sung Lee,Sihwan Kim,Joe C. Campbell,Kyusang Lee +9 more
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Artificial Intelligence and Advanced Materials
TL;DR: A review of the origins, procedures, and applications of artificial intelligence can be found in this paper , where ML and its methods are reviewed to provide basic knowledge of its implementation and its potential.
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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