Abstract
The Artificial Neural Networks (ANNs) have been used for solving problems in many theoretical and practical areas. Advances on the field of ANNs have derived in Spiking Neural Networks (SNNs); which are considered as the third generation of ANNs. SNNs receive/send the information by timing of events (spikes) instead by the spike rate; as their predecessors do. Although SNNs are capable to solve some functions with fewer neurons than networks of previous generations, there aren’t rules to set the architecture of any kind of ANN for solving a specific task; usually the architecture is set empirically based on the designer’s experience and the neural network’s performance over the problem. Recently, metaheuristic algorithms are being implemented to optimize some aspect on ANNs such as weight, connections and even the architecture. This work proposes a generic framework for automatic construction of Fully-Connected Feed-Forward Spiking Neural Networks through an indirect representation by means of Grammatical Evolution (GE) based on Evolutionary Strategy (ES) algorithm. Two well-known benchmarks datasets of pattern recognition were used for testing the proposal of this paper.
Chapter PDF
References
Belatreche, A.: Biologically Inspired Neural Networks: Models, Learning, and Applications. VDM Verlag, Saarbrücken (2010)
Bohte, S.M., Kok, J.N., LaPoutre, H.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48, 17–37 (2002)
De Mingo Lopez, L.F., Gomez Blas, N., Arteta, A.: The optimal combination: Grammatical swarm, particle swarm optimization and neural networks. Journal of Computational Science 3(1-2), 46–55 (2012)
Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments. SCI, vol. 194. Springer, Heidelberg (2009)
Ding, S., Li, H., Su, C., Yu, J., Jin, F.: Evolutionary artificial neural networks: A review. Artif. Intell. Rev. 39(3), 251–260 (2013)
Fang, H.-L., Ross, P., Corne, D.: A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 375–382. Morgan Kaufmann (1993)
Garro, B.A., Sossa, H., Vazquez, R.A.: Design of artificial neural networks using a modified particle swarm optimization algorithm. In: Proceedings of the 2009 International Joint Conference on Neural Networks, IJCNN 2009, pp. 2363–2370. IEEE Press, Piscataway (2009)
Gerstner, W.: Time structure of the activity in neural network models. Physical Review E 51(1), 738–758 (1995)
Gerstner, W., Kistler, W.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press (2002)
Holland, J.: Adaptation in natural and artificial systems. University of Michigan Press (1975)
Hong, S., Ning, L., Xiaoping, L., Qian, W.: A cooperative method for supervised learning in spiking neural networks. In: CSCWD, pp. 22–26. IEEE (2010)
Johnson, C., Roychowdhury, S., Venayagamoorthy, G.K.: A reversibility analysis of encoding methods for spiking neural networks. In: IJCNN, pp. 1802–1809 (2011)
Judd, J.S.: Neural Network Design and the Complexity of Learning. Neural Network Modeling and Connectionism Series. Massachusetts Institute Technol. (1990)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, pp. 1137–1145 (1995)
Koza, J.R., Poli, R.: Genetic programming. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 127–164. Kluwer, Boston (2005)
Maass, W.: Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons, pp. 211–217. MIT Press (1996)
Maass, W.: Networks of spiking neurons: The third generation of neural network models. Neural Networks 10(9), 1659–1671 (1997)
O’Neill, M., Brabazon, A.: Grammatical differential evolution. In: International Conference on Artificial Intelligence (ICAI 2006), Las Vegas, Nevada. CSEA Press (2006)
Rechenberg, I.: Evolutions Strategie: optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog (1973)
Ryan, C., Collins, J.J., Neill, M.O.: Grammatical evolution: Evolving programs for an arbitrary language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83–95. Springer, Heidelberg (1998)
Shen, H., Liu, N., Li, X., Wang, Q.: A cooperative method for supervised learning in spiking neural networks. In: CSCWD, pp. 22–26. IEEE (2010)
Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Espinal, A., Carpio, M., Ornelas, M., Puga, H., Melín, P., Sotelo-Figueroa, M. (2014). Developing Architectures of Spiking Neural Networks by Using Grammatical Evolution Based on Evolutionary Strategy. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-Lopez, J.A., Salas-Rodríguez, J., Suen, C.Y. (eds) Pattern Recognition. MCPR 2014. Lecture Notes in Computer Science, vol 8495. Springer, Cham. https://doi.org/10.1007/978-3-319-07491-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-07491-7_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07490-0
Online ISBN: 978-3-319-07491-7
eBook Packages: Computer ScienceComputer Science (R0)