Noise Enhanced Signaling in STDP Driven Spiking-Neuron Network

2017 ◽  
Vol 12 (4) ◽  
pp. 109-124 ◽  
Author(s):  
S.A. Lobov ◽  
M.O. Zhuravlev ◽  
V.A. Makarov ◽  
V.B. Kazantsev
Keyword(s):  
Spiking Neuron ◽  
Neuron Network ◽  
Spiking Neuron Network

Learning and data clustering with an RBF-based spiking neuron network

2006 ◽  
Vol 18 (1) ◽  
pp. 73-86 ◽  
Author(s):  
Natacha Gueorguieva ◽  
Iren Valova ◽  
Georgi Georgiev
Keyword(s):  
Data Clustering ◽  
Spiking Neuron ◽  
Neuron Network ◽  
Spiking Neuron Network

scholarly journals On the applicability of STDP-based learning mechanisms to spiking neuron network models

AIP Advances ◽  
2016 ◽  
Vol 6 (11) ◽  
pp. 111305 ◽  
Author(s):  
A. Sboev ◽  
D. Vlasov ◽  
A. Serenko ◽  
R. Rybka ◽  
I. Moloshnikov
Keyword(s):  
Network Models ◽  
Spiking Neuron ◽  
Learning Mechanisms ◽  
Neuron Network ◽  
Spiking Neuron Network

scholarly journals Cascade Spiking Neuron Network For Event-based Image Classification In Noisy Environment

2021 ◽  
Author(s):  
Yuntao Han ◽  
Tao Yu ◽  
Silu Cheng ◽  
Jiangtao Xu
Keyword(s):  
Image Classification ◽  
Liquid State ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Spiking Neuron ◽  
Noisy Environment ◽  
Neuron Network ◽  
Processing Event ◽  
Spiking Neuron Network ◽  
Event Based

<div> <div> <div> <div> <p>Spiking Neuron Network (SNN) has shown advantages in processing event-based data for image classification. However, the classification accuracy of SNNs decreases in noisy environment. The cascade spiking neuron network (cascade-SNN) was proposed to solve this problem in this letter. We used spiking convolutional spiking neuron network (SCNN) for features extraction and liquid state machine (LSM) for read out. Compared with early works on ANNs, this network achieved the state-of-the-art classification accuracy in DVS-CIFAR10 dataset and DVS-Gesture dataset, which are both challenging dataset because of noisy environment. We conducted ablation experiments to verify the proposed structure is effective and analyzed the influence of different hyper-parameters. </p> </div> </div> </div> </div>

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