Performance Evaluation of a Temporal Sequence Learning Spiking Neural Network

2007 ◽  
Author(s):  
T. Ichishita ◽  
R. H. Fujii
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Sequence Learning ◽  
Temporal Sequence ◽  
Spiking Neural Network

scholarly journals A neural network model for temporal sequence learning and motor programming

1994 ◽  
Vol 7 (9) ◽  
pp. 1461-1476 ◽  
Author(s):  
Bernard Ans ◽  
Yves Coiton ◽  
Jean-Claude Gilhodes ◽  
Jean-Luc Velay
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Sequence Learning ◽  
Temporal Sequence ◽  
Motor Programming

scholarly journals Spiking Neural Network Learning Models for Spike Sequence Learning and Data Classification

2020 ◽  
pp. 1-17
Author(s):  
Moses Apambila Agebure ◽  
Edward Yellakuor Baagyere ◽  
Elkanah Olaosebikan Oyetunji
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Sequence Learning ◽  
Data Classification ◽  
Machine Learning Algorithms ◽  
Spiking Neural Network ◽  
Learning Models ◽  
Class Imbalance Problem ◽  
Extensive Evaluation ◽  
Spike Sequence

Supervised learning in Spiking Neural Network (SNN) is a hotbed for researchers due to the advantages temporal coded networks provide over that of rate-coded networks with respect to efficiency in information processing and transfer rates. Supervised learning in rate-coded networks though well established, it is difficult to directly apply such models to SNN due to difference in information coding schemes. In this paper, we seek to exploit the advantages of spiking neural networks for spike sequence learning in order to establish two (2) models; batch and sequential learning models for solving data classification tasks. The models are built using the least squares approach leveraging on its approximation abilities. The first set of experiments are on spikesequence learning in which an extensive evaluation of the model is performed using different inputoutput firing rates and learning periods. Results from these experiments show that the proposed model for spike sequence learning produced better performance than some existing models derived for spike sequence learning, particularly, at higher learning periods. The proposed models for data classification are also tested on some selected benchmark datasets most of which had imbalance class distributions and also on real-world road condition datasets for anomaly classification collected by the authors as part of a larger study. While the proposed models generalised very well to all datasets including those with the class imbalance problem where F1and Recall values above 0.90 were recorded, some well-know machine learning algorithms applied to the datasets yielded lower F1 and Recall values and in some cases recorded 0.0 Recall.

scholarly journals Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm

2018 ◽  
Vol 145 ◽  
pp. 488-494 ◽  
Author(s):  
Aleksandr Sboev ◽  
Alexey Serenko ◽  
Roman Rybka ◽  
Danila Vlasov ◽  
Andrey Filchenkov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Spiking Neural Network ◽  
Network Parameters

scholarly journals Radar Emitter Identification Based on Fully Connected Spiking Neural Network

2021 ◽  
Vol 1914 (1) ◽  
pp. 012036
Author(s):  
LI Wei ◽  
Zhu Wei-gang ◽  
Pang Hong-feng ◽  
Zhao Hong-yu
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
Fully Connected
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