Evolving Spiking Neural Network as a Classifier: An Experimental Review

被引：0

作者：

Saravanan, M. ^{[1
]}

Bablani, Annushree ^{[2
]}

Rangisetty, Navyasai ^{[2
]}

机构：

[1] Ericsson India Global Serv Pvt Ltd, Perungudi, India

[2] Indian Inst Informat Technol, Sri City, India

来源：

ADVANCES IN COMPUTING AND DATA SCIENCES (ICACDS 2022), PT II | 2022年 / 1614卷

关键词：

Evolving spiking neural network; Reservoir; Gaussian receptive field; Spatial-temporal data; NEURONS;

D O I：

10.1007/978-3-031-12641-3_25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The brain-inspired Spiking Neural Networks (SNNs) are considered as the third generation of neural networks for AI applications. The spiking neural network has been proved very efficient to predict and classify data with spatial and temporal information in the way of modeling the behavior and learning potential of the brain. The aim is to understand the working of the Evolving SNN (ESNN) as a classifier and how it is different from the existing neural models. Besides exploring the existing ESNN architecture, the results have been generated by tuning the various parameters of the ESNN model which may be contributing to provide better prediction accuracies. The tuned ESNN model is applied to various datasets and compared with the existing second-generation neural network model like LSTM. The results show comparable improvement in the classification accuracy using ESNN which concludes that the ESNN and its variants are the beginning of a new era of Neural Networks.

引用

页码：304 / 315

页数：12

共 22 条

[1]

Dhoble K., 2013, THESIS U TECHNOLOGY

[2] Integrated Feature Selection and Parameter Optimization for Evolving Spiking Neural Networks using Quantum Inspired Particle Swarm Optimization [J].

Hamed, Haza Nuzly Abdull ;

Kasabov, Nikola ;

Shamsuddin, Siti Mariyam .

2009 INTERNATIONAL CONFERENCE OF SOFT COMPUTING AND PATTERN RECOGNITION, 2009, :695-+

[3]

Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.8.1735, 10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]

[4]

Kasabov N, 1998, ICONIP'98: THE FIFTH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING JOINTLY WITH JNNS'98: THE 1998 ANNUAL CONFERENCE OF THE JAPANESE NEURAL NETWORK SOCIETY - PROCEEDINGS, VOLS 1-3, P1232

[5]

Kasabov N., 2011, NEURAL NETWORKS

[6]

Kasabov NK, 2018, Springer Series on Bioand Neurosystems

[7] DEAP: A Database for Emotion Analysis Using Physiological Signals [J].

Koelstra, Sander ;

Muhl, Christian ;

Soleymani, Mohammad ;

Lee, Jong-Seok ;

Yazdani, Ashkan ;

Ebrahimi, Touradj ;

Pun, Thierry ;

Nijholt, Anton ;

Patras, Ioannis .

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2012, 3 (01) :18-31

[8] Evolving Spiking Neural Networks for online learning over drifting data streams [J].

Lobo, Jesus L. ;

Lana, Ibai ;

Del Ser, Javier ;

Bilbao, Miren Nekane ;

Kasabov, Nikola .

NEURAL NETWORKS, 2018, 108 :1-19

[9] Networks of spiking neurons: The third generation of neural network models [J].

Maass, W .

NEURAL NETWORKS, 1997, 10 (09) :1659-1671

[10] CDDM: A method to detect and handle Concept Drift in Dynamic Mobility Model for seamless 5G Services [J].

Perepu, Satheesh K. ;

Dey, Kaushik .

2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2020,

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