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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A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design
TL;DR: A novel supervised learning algorithm for SNNs based on temporal coding is proposed, designed to facilitate analog VLSI implementations with analog resistive memory, by which ultrahigh energy efficiency can be achieved.
Proceedings ArticleDOI
A New Neuromorphic Computing Approach for Epileptic Seizure Prediction
TL;DR: A neuromorphic computing approach for seizure prediction is proposed in this work which uses a designed gaussian random discrete encoder to generate spike sequences from the EEG samples and make predictions in a spiking convolutional neural network (Spiking-CNN) which combines the advantages of CNNs and SNNs.
Journal ArticleDOI
An end-to-end exemplar association for unsupervised person Re-identification
TL;DR: Extensive experiments and ablation studies on seven RE-ID datasets demonstrate the superiority of the proposed EEA over most state-of-the-art unsupervised and domain adaptation RE- ID methods.
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Collective and synchronous dynamics of photonic spiking neurons
Takahiro Inagaki,Kensuke Inaba,Timothée Leleu,Toshimori Honjo,Takuya Ikuta,Koji Enbutsu,Takeshi Umeki,Ryoichi Kasahara,Kazuyuki Aihara,Hiroki Takesue +9 more
TL;DR: The experimental results show that the effective change causes spontaneous modification of the spiking modes and firing rates of clustered neurons, and such collective dynamics can be utilized to realize efficient heuristics for solving NP-hard combinatorial optimization problems.
Journal ArticleDOI
Predicting thermoelectric properties from chemical formula with explicitly identifying dopant effects
TL;DR: In this article, a unified architecture of neural networks, called DopNet, is proposed to accurately predict the materials properties of the doped materials by explicitly and independently embedding the host materials and the dopants.
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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