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2014 | OriginalPaper | Buchkapitel

How to Generate the Input Current for Exciting a Spiking Neural Model Using the Cuckoo Search Algorithm

verfasst von : Roberto A. Vazquez, Guillermo Sandoval, Jose Ambrosio

Erschienen in: Cuckoo Search and Firefly Algorithm

Verlag: Springer International Publishing

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Abstract

Spiking neurons are neural models that try to simulate the behavior of biological neurons. This model generates a response (spikes or spike train) only when the model reaches a specific threshold. This response could be coded into a firing rate and perform a pattern classification task according to the firing rate generated with the input current. However, the input current must be carefully computed to obtain the desired behavior. In this paper, we describe how the Cuckoo Search algorithm can be used to train a spiking neuron and determine the best way to compute the input current for solving a pattern classification task. The accuracy of the methodology is tested using several pattern recognition problems.

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Vorheriges Kapitel A Binary Cuckoo Search and Its Application for Feature Selection

Nächstes Kapitel Multi-Objective Optimization of a Real-World Manufacturing Process Using Cuckoo Search

A chaos synchronization-based dynamic vision model for image segmentation. 5, (2005)

Exploration of rank order coding with spiking neural networks for speech recognition. 4, (2005)

Abdull Hamed, H.N., Kasabov, N., Michlovský, Z., and Shamsuddin, S.M.: String pattern recognition using evolving spiking neural networks and quantum inspired particle swarm optimization. In: Proceedings of the 16th International Conference on Neural Information Processing. Part II, ICONIP ’09, LNCS, pp. 611–619. Springer-Verlag, Heidelberg (2009)

Balasubramanian, S., Panigrahi, S., Logue, C., Gu, H., Marchello, M.: Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification. J. Food Eng. 91(1), 91–98 (2009)

Baldwin, E.A., Bai, J., Plotto, A., Dea, S.: Electronic noses and tongues. Applications for the food and pharmaceutical industries. Sensors 11(5), 4744–4766 (2011)

Barthelemy, P., Bertolotti, J., Wiersma, D.S.: A levy flight for light. Nature 453, 495–498 (2008)

Belatreche, A., Maguire, L.P., and McGinnity, T.M.: Pattern recognition with spiking neural networks and dynamic synapse. In: International FLINS Conference on Applied Computational Intelligence, pp. 205–210 (2004)

Bermak, A., Martinez, D.: A compact 3d vlsi classifier using bagging threshold network ensembles. IEEE Trans. Neural Netw., 14(5), 1097–1109 (2003)

Bohte, S.M., Kok, J.N., Poutre, H.L.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1–4), 17–37 (2002)CrossRefMATH

10.

Chen, H.T., Ng, K.T., Bermak, A., Law, M., Martinez, D.: Spike latency coding in biologically inspired microelectronic nose. IEEE Trans. Biomed. Circ. Syst., 5(2), 160–168 (2011)

11.

Devroye, L., Györfi, L., Lugosi, G.: A probabilistic theory of pattern recognition. Springer, Heidelberg (1996)

12.

Di Paolo, E.: Spike-timing dependent plasticity for evolved robots. Adapt. Behav., 10, 243–263 (2002)

13.

Floreano, D., Zufferey, J.-C., Nicoud, J.-D.: From wheels to wings with evolutionary spiking neurons. Artif. Life, 11(1–2), 121–138 (2005)

14.

Fu, J., Li, G., Qin, Y., Freeman, W.J.: A pattern recognition method for electronic noses based on an olfactory neural network. Sens. Actuators B: Chemical, 125(2), 489–497 (2007)

15.

Garro, B., Sossa, H., and Vazquez, R.: Design of artificial neural networks using a modified particle swarm optimization algorithm. In: International Joint Conference on Neural Networks. IJCNN, pp. 938–945 (2009)

16.

Garro, B.A., Sossa, H., and Vázquez, R.A.: Design of artificial neural networks using differential evolution algorithm. In: Proceedings of the 17th International Conference on Neural Information Processing: models and applications, Vol. Part II, pp. 201–208, ICONIP’10, LNCS, Springer-Verlag, Heidelberg (2010)

17.

Gerstner, W., Kistler, W.M.: Spiking Neuron Models. Single Neurons, Populations. Cambridge University Press, Plasticity (2002)

18.

Gomez-Chova, L., Calpe, J., Camps-Valls, G., Martin, J., Soria, E., Vila, J., Alonso-Chorda, L., Moreno, J.: Feature selection of hyperspectral data through local correlation and sffs for crop classification. In: Geoscience and Remote Sensing Symposium. IGARSS ’03. Proceedings. IEEE, International, vol. 1, pp. 555–557 (2003)

19.

Haralick, R., Shanmugam, K., and Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern., SMC, 3(6): 610–621 (1973)

20.

Hasselmo, M.E., Bodelón, C., Wyble, B.P.: A proposed function for hippocampal theta rhythm: separate phases of encoding and retrieval enhance reversal of prior learning. Neural Comput. 14, 793–817, April 2002

21.

Hopfield, J.J., Brody, C.D.: What is a moment Cortical sensory integration over a brief interval. Proc. Natl. Acad. Sci., vol. 97 (25), 13919–13924, Dec 2000

22.

Hu, M.-K.: Visual pattern recognition by moment invariants. IEEE Trans. Inform. Theory, 8(2), 179–187, Feb 1962

23.

Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw., 14(6), 1569–1572, Nov 2003

24.

Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw., 15(5), 1063–1070, Sept 2004

25.

Izhikevich, E.M.: Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT Press, Computational Neurosci. (2007)

26.

Jain, R., and Schunck, R.K.B.G.: Machine Vision. McGraw-Hill, New York (1995)

27.

Karaboga, D., Akay, B., and Ozturk, C.: Artificial bee colony (abc) optimization algorithm for training feed-forward neural networks. In: Proceedings of the 4th International Conference on Modeling Decisions for Artificial Intelligence, MDAI ’07, LNCS, pp. 318–329, Springer-Verlag, Heidelberg (2007)

28.

Karaboga, D., and Basturk, B.: Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. In: IFSA (1)’07, LNCS, pp. 789–798 (2007)

29.

Kennedy, J., and Eberhart, R.: Particle swarm optimization. In: Proceedings. IEEE Int. Conf. Neural Netw. (1995) vol. 4, pp. 1942–1948, Aug 2002

30.

Maass, W., Graz, T.U.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10, 1659–1671 (1997)

31.

Murphy, P., Aha, D.: UCI Repository of machine learning databases. Technical report, University of California, Department of Information and Computer Science, Irvine, CA, USA (1994)

32.

Nagy, G., Tolaba, J.: Nonsupervised crop classification through airborne multispectral observations. IBM J. Res. Dev. 16(2), 138–153 (1972)

33.

Oh, E.H., Song, H.S., Park, T.H.: Recent advances in electronic and bioelectronic noses and their biomedical applications. Enzym. Microb. Tech. 48(67), 427–437 (2011)

34.

Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66, Jan 1979

35.

Price, K.V., Storn, R.M., Lampinen, J.A.: Differential evolution a practical approach to global optimization. Nat. Comput. Ser. Springer-Verlag, Berlin (2005)

36.

Schwenker, F., Kestler, H.A., Palm, G.: Three learning phases for radial-basis-function networks. Neural Netw. 14(45), 439–458 (2001)

37.

Senthilnath, J., Omkar, S.N., Mani, V., and Karnwal, N.: Hierarchical artificial immune system for crop stage classification. In: Annuals IEEE India Conference (INDICON), pp. 1–4 (2011)

38.

Thorpe, S.J., Guyonneau, R., Guilbaud, N., Allegraud, J.-M., and VanRullen R.: Spikenet: real-time visual processing with one spike per neuron. Neurocomputing, 58–60:857–864 (2004). (Comput. Neurosci.: Trends Res. 2004)

39.

Vazquez, R.: Izhikevich neuron model and its application in pattern recognition. Aust. J. Intell. Inf. Process. Syst. 11(1), 35–40 (2010)

40.

Vazquez, R.: A computational approach for modeling the biological olfactory system during an odor discrimination task using spiking neuron. BMC Neurosci. 12(Suppl 1), p. 360 (2011)

41.

Vazquez, R.: Training spiking neural models using cuckoo search algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 679–686 (2011)

42.

Vazquez, R., and Cacho andn, A.: Integrate and fire neurons and their application in pattern recognition. In: 7th International Conference on Electrical Engineering Computing Science Automatic Control (CCE), pp. 424–428 (2010)

43.

Vazquez, R., Sossa, H., Garro, B.: 3d object recognition based on some aspects of the infant vision system and associative memory. Cognitive Comput. 2, 86–96 (2010)

44.

Vázquez R.A.: Pattern recognition using spiking neurons and firing rates. In: Proceedings of the 12th Ibero-American Conference on Advances Artificial Intelligence, IBERAMIA’10, LNAI, pp. 423–432, Springer-Verlag, Heidelberg (2010)

45.

Vázquez, R.A., and Garro, B.A.: Training spiking neurons by means of particle swarm optimization. In: Proceedings of the Second International Conference on Advances in Swarm Intelligence, vol. Part I, ICSI’11, pp. 242–249. Springer-Verlag, Heidelberg (2011)

46.

Vazquez Espinoza De Los Monteros, R.A., and Sossa Azuela, J. H.: A new associative model with dynamical synapses. Neural Process. Lett., 28:189–207, Dec 2008

47.

Viswanathan, G.M., Buldyrev, S.V., Havlin, S., da Luz, M.G.E., Raposo, E.P., Stanley, H.E.: Optimizing the success of random searches. Nature 401, 911–914 (1999)CrossRef

48.

Yang, X.-S.: 1-optimization and metaheuristic algorithms in engineering. Metaheuristics in water, geotechnical and transport engineering, pp. 1–23. Elsevier, Oxford (2013)

49.

Yang, X.-S., and Deb, S.: Cuckoo search via levy flights. In: World Congress on Nature Biologically Inspired Computing, NaBIC, pp. 210–214 (2009)

50.

Yin, Y., Yu, H., Zhang, H.: A feature extraction method based on wavelet packet analysis for discrimination of chinese vinegars using a gas sensors array. Sens. Actuators B: Chemical 134(2), 1005–1009 (2008)

Titel: How to Generate the Input Current for Exciting a Spiking Neural Model Using the Cuckoo Search Algorithm
verfasst von: Roberto A. Vazquez
Guillermo Sandoval
Jose Ambrosio
Verlag: Springer International Publishing
Buch: Cuckoo Search and Firefly Algorithm
Print ISBN: 978-3-319-02140-9

Electronic ISBN: 978-3-319-02141-6

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-319-02141-6_8

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