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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2019

10.05.2018

Supervised Training of Spiking Neural Network by Adapting the E-MWO Algorithm for Pattern Classification

verfasst von: Ahmed A. Abusnaina, Rosni Abdullah, Ali Kattan

Erschienen in: Neural Processing Letters | Ausgabe 2/2019

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Abstract

Spiking neural networks (SNN) are more realistic and powerful than the preceding generations of the neural networks (e.g. multi-layer perceptron networks). The SNN can be applied for simulating the brain and its functions, as well as it is able to be employed for different applications such as pattern classification. Different methods have been proposed for supervised training of SNN, however, most of them were validated based on using the classical XOR problem, and they consume long training time if other problems are considered. This paper proposes a new supervised training method for SNN by adapting the Enhanced-Mussels Wandering Optimization algorithm. In addition, a SNN model for pattern classification is proposed. The proposed work is used for pattern classification of real-world problems. The obtained results indicate that the proposed method is competitive alternative in terms of classification accuracy and training time.
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Fußnoten
1
ANN are considered the second generation of neural networks.
 
2
Booij and Nguyen [24] did not indicate an exact name for their proposed method. In this paper the method that named GD-MultipleSpikes is referred to that method in [24].
 
Literatur
1.
Maass W (1997) Networks of spiking neurons: the third generation of neural network models. Neural Netw 10(9):1659–1671CrossRef
2.
Ghosh-Dastidar S, Adeli H (2009) Spiking neural networks. Int J Neural Syst 19(4):295–308CrossRef
3.
Chandhok C, Chaturvedi S (2012) Adaptation of spiking neural networks for image clustering. Int J Video Image Proces Netw Secur IJVIPNS-IJENS 12(03):8–13
4.
Mohemmed A, Schliebs S, Matsuda S, Kasabov N (2013) Training spiking neural networks to associate spatio-temporal inputoutput spike patterns. Neurocomputing 107:3–10CrossRef
5.
Belatreche A, Maguire L, McGinnity M (2007) Advances in design and application of spiking neural networks. Soft Comput 11(3):239–248CrossRef
6.
Ghosh-Dastidar S, Adeli H (2009) Third Generation Neural networks: spiking neural networks. Advances in computational intell., AISC 61. Springer, Berlin Heidelberg, pp 167–178
7.
Ponulak F, Kasiski A (2011) Introduction to spiking neural networks: information processing, learning and applications. Acta Neurobiol Exp 71(4):409–433
8.
Bohte S, Kok J (2005) Applications of spiking neural networks. J Inf Proces Lett 95(6):519–520CrossRefMATH
9.
Gtig R (2014) To spike, or when to spike? Curr Opin Neurobiol 25:134–139CrossRef
10.
Ponulak F (2006) Supervised Learning in Spiking Neural Networks with ReSuMe Method. Ph.D. Dissertation. Faculty of Electrical Engineering, Pozna University of Technology
11.
Bohte SM, Kok JN, Poutre HL (2002) Error-backprogation in temporally encoded networks of spiking neurons. Neurocomputing 48:17–37CrossRefMATH
12.
Xu Y, Zeng X, Han L, Yang J (2013) A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks. Neural Netw 43:99–113CrossRefMATH
13.
Shambour M, Khader AT, Abusnaina AA, Shambour Q (2014) Modified tournament harmony search for unconstrained optimisation problems. In: recent advances on soft computing and data mining. Springer International Publishing, pp 283–292
14.
Shambour M, Abusnaina AA, Alsalibi AI (2018) Modified global flower pollination algorithm and its application for optimization problems. J Interdiscip Sci Comput Life Sci 12:1
15.
An J, Kang Qi, Wang L, Wu Q (2013) Mussels wandering optimization: an ecologically inspired algorithm for global optimization. Cognit Comput 5(2):188–199CrossRef
16.
Abusnaina AA, Abdullah R, Kattan A (2014) Enhanced MWO training algorithm to improve classification accuracy of artificial neural networks. In: Herawan T, Ghazali R, Deris M (eds) Recent advances on soft computing and data mining. Springer International Publishing, pp 183–194
17.
18.
Abusnaina AA, Abdullah R, Kattan A (2014) The application of mussels wandering optimization algorithm for spiking neural networks training. In: 1st International engineering conference (IEC2014) On developments in civil & computer engineering applications, pp 197–204
19.
Bohte SM, Kok JN, Poutre HL (2000) Spike-prop: errorbackprogation in multi-layer networks of spiking neurons. In: Proceedings of the European symposium on artificial neural networks ESANN2000 , ed. M. Verleysen, D-facto, pp 419–425
20.
Jin Y, Wen R, Sendhoff B (2007) Evolutionary multi-objective optimization of spiking neural networks. In artificial neural networksICANN 2007. Springer Berlin Heidelberg, pp 370–379
21.
Xin J, Embrechts MJ (2001) Supervised learning with spiking neural networks. In neural networks, 2001. International joint conference on proceedings. IJCNN’01. IEEE, vol 3, pp 1772–1777
22.
Schrauwen B, Van Campenhout J (2004) Improving spikeprop: enhancements to an error-backpropagation rule for spiking neural networks. In: Proceedings of the 15th ProRISC workshop, vol 11
23.
Wu QX, McGinnity TM, Maguire LP, Glackin B, Belatreche A (2006) Learning under weight constraints in networks of temporal encoding spiking neurons. Neurocomputing 69(16–18):1912–1922CrossRef
24.
Booij O, Nguyen H (2005) A gradient descent rule for multiple spiking neurons emitting multiple spikes. Inf Proces Lett 95(6):552–557MathSciNetCrossRefMATH
25.
Ghosh-Dastidar S, Adeli H (2009) A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Netw 22:1419–1431CrossRef
26.
Ghosh-Dastidar S, Adeli H (2007) Improved spiking neural networks for EEG classification and epilepsy and seizure detection. Integr Comput Aided Eng 14(3):187–212CrossRef
27.
Silva S. M, Ruano A. E (2005) Application of Levenberg-Marquardt method to the training of spiking neural networks. In: ICNN&B’05 international conference on neural networks and brain. IEEE, vol 3, pp 1354–1358
28.
Pavlidis NG, Tasoulis DK, Plagianakos VP, Nikiforidis G, Vrahatis MN (2005) Spiking neural network training using evolutionary algorithms. In: Proceedings 2005 IEEE international joint conference on neural networks. IEEE, vol 4, pp 2190–2194
29.
Hong S, Ning L, Xiaoping L, Qian W (2010) A cooperative method for supervised learning in spiking neural networks. In: 2010 14th International conference on computer supported cooperative work in design (CSCWD). IEEE, pp 22–26
30.
Belatreche A, Maguire LP, McGinnity M, Wu QX (2003) An evolutionary strategy for supervised training of biologically plausible neural networks. In The sixth international conference on computational intelligence and natural computing (CINC), proceedings of the 7th joint conference on information sciences, pp 1524–1527
31.
Belatreche A, Maguire LP, McGinnity M, Wu Q (2003) A method for supervised training of spiking neural networks. In: Proceedings IEEE conference cybernetics intelligence challenges and advances, CICA, pp 39–44
32.
Abusnaina AA, Abdullah R (2013) Mussels wandering optimization algorithm based training of artificial neural networks for pattern classification. In: Proceedings of the 4th international conference on computing and informatics, ICOCI, pp 78–85
33.
Abusnaina AA, Abdullah R (2014) Spiking neuron models: a review. Int J Digit Content Technol Appl (JDCTA) 8(3):14–21
34.
Jolivet R, Timothy J, Gerstner W (2003) The spike response model: a framework to predict neuronal spike trains. In: Artificial neural networks and neural information ProcessingICANN/ICONIP 2003. Springer Berlin Heidelberg, pp 846–853
35.
Gerstner W, Kistler WM (2002) Spiking neuron models: single neurons, populations, plasticity. Cambridge University Press, CambridgeCrossRefMATH
36.
Shamir M (2014) Emerging principles of population coding: in search for the neural code. Curr Opin Neurobiol 25:140–148CrossRef
37.
Thrope S, Delorme A, Rullen RV (2001) spike-based strategies for rapid processing. Neural Netw 14:715–725CrossRef
38.
39.
Dehuri S, Roy R, Cho SB, Ghosh A (2012) An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. J Syst Softw 85(6):1333–1345CrossRef
Metadaten
Titel
Supervised Training of Spiking Neural Network by Adapting the E-MWO Algorithm for Pattern Classification
verfasst von
Ahmed A. Abusnaina
Rosni Abdullah
Ali Kattan
Publikationsdatum
10.05.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9846-0

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