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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Journal Article
Structural Learning in Artificial Neural Networks: A Neural Operator Perspective
TL;DR: This review provides a survey on structural learning methods in deep ANNs, including a new neural operator framework from a cellular neuroscience context and perspective aimed at motivating research on this challenging topic.
Proceedings ArticleDOI
Dynamically Reconfigurable Cryogenic Spiking Neuron based on Superconducting Memristor
TL;DR: In this article , an artificial neuron topology that can be electronically reconfigured and dynamically tuned to alter its spiking rate was proposed, and the spike rates can be further tuned by invoking gradual changes in the external bias current.
Journal ArticleDOI
A spiking neural network with probability information transmission
TL;DR: A Probabilistic Spike Response Model (PSRM), of which ignition mode is determined neither by the difference between the threshold and membrane voltage nor in the form of pulses, is proposed from a probabilistic perspective.
Journal ArticleDOI
Protein Structured Reservoir computing for Spike-based Pattern Recognition
TL;DR: In this article, a reservoir computing on a single protein molecule and neuromorphic connectivity with a small-world networking property has been proposed, where Izhikevich spiking neurons are used as elementary processors, corresponding to the atoms of verotoxin protein, and its molecule as a 'hardware' architecture of the communication networks connecting the processors.
Journal ArticleDOI
Energy-efficient event pattern recognition in wireless sensor networks using multilayer spiking neural networks
TL;DR: This work devise the multilayer spiking neuron training rules for event pattern classification in distributed wireless sensor networks and shows that the proposed architecture improves classification accuracy by a considerable amount as compared to a single Tempotron model’s performance.
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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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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