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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Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks
TL;DR: For sequential and streaming tasks, this work demonstrates how a novel type of adaptive spiking recurrent neural network (SRNN) is able to achieve state-of-the-art performance compared to other spiking neural networks and almost reach or exceed the performance of classical recurrent neural networks (RNNs) while exhibiting sparse activity.
Journal ArticleDOI
Numerical Spiking Neural P Systems
TL;DR: It is proved that NSN P is Turing universal as number generating devices, where the production functions in each neuron are linear functions, each involving at most one variable, and as number accepting devices,NSN P systems are proved to be universal as well, even if each neuron contains only one production function.
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Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance
TL;DR: It is shown that a deep temporal-coded SNN can be trained easily and directly over the benchmark datasets CIFAR10 and ImageNet, with testing accuracy within 1% of the DNN of equivalent size and architecture.
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A multi-layer spiking neural network-based approach to bearing fault diagnosis
TL;DR: In this article , a probabilistic spiking response model (PSRM) with a multi-layer structure is put forth to enhance the performance of the SNN in terms of bearing fault diagnosis.
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Event-Based Angular Velocity Regression with Spiking Networks
TL;DR: This work proposes, for the first time, a temporal regression problem of numerical values given events from an event-camera and investigates the prediction of the 3- DOF angular velocity of a rotating event- camera with an SNN.
References
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Proceedings Article
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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