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

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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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Notes

  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].

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Authors and Affiliations

  1. Department of Computer Sciences, Faculty of Engineering and Technology, Birzeit University, Ramallah, Palestine

    Ahmed A. Abusnaina

  2. School of Computer Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia

    Rosni Abdullah

  3. IT Department, Ishik University, Erbīl, Iraq

    Ali Kattan

Authors
  1. Ahmed A. Abusnaina
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  2. Rosni Abdullah
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  3. Ali Kattan
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Correspondence to Ahmed A. Abusnaina.

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Abusnaina, A.A., Abdullah, R. & Kattan, A. Supervised Training of Spiking Neural Network by Adapting the E-MWO Algorithm for Pattern Classification. Neural Process Lett 49, 661–682 (2019). https://doi.org/10.1007/s11063-018-9846-0

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