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
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks
TL;DR: A general audio-to-spiking conversion procedure is introduced and two novel spike-based classification datasets are provided that show that leveraging spike timing information within these datasets is essential for good classification accuracy.
Journal ArticleDOI
Roadmap on emerging hardware and technology for machine learning.
Karl K. Berggren,Qiangfei Xia,Konstantin K. Likharev,Dmitri B. Strukov,Hao Jiang,Thomas Mikolajick,Damien Querlioz,Martin Salinga,John R. Erickson,Shuang Pi,Feng Xiong,Peng Lin,Can Li,Yu Chen,Shisheng Xiong,Brian D. Hoskins,Matthew W. Daniels,Advait Madhavan,Advait Madhavan,James Alexander Liddle,Jabez J. McClelland,Yuchao Yang,Jennifer L. M. Rupp,Jennifer L. M. Rupp,Stephen S. Nonnenmann,Kwang-Ting Cheng,Nanbo Gong,Miguel Angel Lastras-Montano,A. Alec Talin,Alberto Salleo,Bhavin J. Shastri,Thomas Ferreira de Lima,Paul R. Prucnal,Alexander N. Tait,Yichen Shen,Huaiyu Meng,Charles Roques-Carmes,Zengguang Cheng,Zengguang Cheng,Harish Bhaskaran,Deep Jariwala,Han Wang,Jeffrey M. Shainline,Kenneth Segall,Jianhua Yang,Kaushik Roy,Suman Datta,Arijit Raychowdhury +47 more
TL;DR: The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
Journal ArticleDOI
S4NN: temporal backpropagation for spiking neural networks with one spike per neuron
TL;DR: This work derives a new learning rule for multilayer spiking neural networks, named S4NN, akin to traditional error backpropagation, yet based on latencies, and shows how approximated error gradients can be computed backward in a feedforward network with any number of layers.
Journal ArticleDOI
Biologically plausible deep learning — But how far can we go with shallow networks?
TL;DR: In this article, the authors investigate how far they can go on digit (MNIST) and object (CIFAR10) classification with biologically plausible, local learning rules in a network with one hidden layer and a single readout layer.
Proceedings Article
Learning to Detect Objects with a 1 Megapixel Event Camera
TL;DR: This work publicly releases the first high-resolution large-scale dataset for object detection and introduces a novel recurrent architecture for event-based detection and a temporal consistency loss for better-behaved training.
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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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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