Training Spiking Neurons by Means of Particle Swarm Optimization
Meta-heuristic algorithms inspired by nature have been used in a wide range of optimization problems. These types of algorithms have gained popularity in the field of artificial neural networks (ANN). On the other hand, spiking neural networks are a new type of ANN that simulates the behaviour of a biological neural network in a more realistic manner. Furthermore, these neural models have been applied to solve some pattern recognition problems. In this paper, it is proposed the use of the particle swarm optimization (PSO) algorithm to adjust the synaptic weights of a spiking neuron when it is applied to solve a pattern classification task. Given a set of input patterns belonging to K classes, each input pattern is transformed into an input signal. Then, the spiking neuron is stimulated during T ms and the firing rate is computed. After adjusting the synaptic weights of the neural model using the PSO algorithm, input patterns belonging to the same class will generate similar firing rates. On the contrary, input patterns belonging to other classes will generate firing rates different enough to discriminate among the classes. At last, a comparison between the PSO algorithm and a differential evolution algorithm is presented when the spiking neural model is applied to solve non-linear and real object recognition problems.
KeywordsParticle Swarm Optimization Particle Swarm Optimiza Algorithm Neuron Model Input Pattern Synaptic Weight
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- 1.Garro, B.A., Sossa, H., Vazquez, R.A.: Design of Artificial Neural Networks using a Modified Particle Swarm Optimization Algorithm. IJCNN, 938–945 (2009)Google Scholar
- 3.Loiselle, S., Rouat, J., Pressnitzer, D., Thorpe, S.: Exploration of rank order coding with spiking neural networks for speech recognition. IJCNN 4, 2076–2080 (2005)Google Scholar
- 7.Izhikevich, E.M.: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT press, Cambridge (2007)Google Scholar
- 8.Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases. Dept. Inf. Comput. Sci., Univ. California, Irvine (1994)Google Scholar
- 10.Vazquez, R.A., Cachon, A.: Integrate and fire neurons and their application in pattern recognition. In: Proceedings of the 7th CCE, pp. 424–428 (2010)Google Scholar
- 11.Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)Google Scholar
- 14.Gudise, V.G., Venayagamoorthy, G.K.: Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 110–117 (2003)Google Scholar
- 16.Hamed, H.N., Kasabov, N., Michlovský, Z., Shamsuddin, S.M.: String Pattern Recognition Using Evolving Spiking Neural Networks and Quantum Inspired Particle Swarm Optimization. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009. LNCS, vol. 5864, pp. 611–619. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 17.Kamoi, S., et al.: Pulse Pattern Training of Spiking Neural Networks Using Improved Genetic Algorithm. In: Proceedings of the IEEE CIRA, pp. 977 – 981 (2003)Google Scholar
- 18.Hong, S., et al.: A Cooperative Method for Supervised Learning in Spiking Neural Networks. In: 14th CSCWD, pp. 22–26 (2010)Google Scholar
- 19.Vazquez, R.A.: Izhikevich Neuron Model and its Application in Pattern Recognition. Australian Journal of Intelligent Information Processing Systems 11, 35–40 (2010)Google Scholar