Spike Response Function Weight and Delay Updating Strategy Using Delay Rules

  • Authors

    • Abdullah H. Almasri
    • Shahnorbanun Sahran
    • Eiad Yafi
    2018-11-26
    https://doi.org/10.14419/ijet.v7i4.29.21965
  • Spiking Neural Network, Spike Response Function, Weight, Delay, Classification, Pattern Recognition.

  • Abstract

    Spike Response Function (SRF) plays an important role in the temporal coding Spiking Neural Network (SNN) as it has a significant role to determine when the neuron should fire. This paper studies the important role of the SRF in the SNN learning stability. It proposes a novel method to find out the rules to update delay for each class to make SRF stable, and then using these rules to update delay and weight simultaneously at the SNN learning rule. This method updates the delay depending on the local result to make SRF stable. The main issue of this paper is to put forward the idea that weight and delay parameters could and need to be updated simultaneously to make both SRF and SNN stable during the learning process. The delay rules strategy which have been found could be used for pattern recognition application which use SNN. The limitation of this work is that; getting the updating delay rules depends on a sample data from each class and the way of selecting the rules.

  • References

    1. [1] Maass, W., Networks of spiking neurons: the third generation of neural network models, Neural Networks, Vol 10, No. 9, pp. 1659-1671, 1997

      [2] Hopfield, J., Pattern recognition computation using action potential timing for stimulus representation, Nature, Vol 376, No. 6535, pp. 33-36, 1995.

      [3] Natschläger, T. & B. Ruf 1998. Spatial and temporal pattern analysis via spiking neurons. Network: Computation in Neural Systems 9(3): 319-332.

      [4] Bohte, S., H. La Poutré & J. Kok 2002. Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks. IEEE Transactions on Neural Networks 13(2): 426-435.

      [5] Bohte, S. M., J. N. Kok & H. La Poutré 2002. Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1–4): 17-37.

      [6] Bohte, S. M., J. N. Kok & H. La Poutre 2000. Unsupervised classification of complex clusters in networks of spiking neurons. International Joint Conference on Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS. 3 pp. 279-284 vol.3.

      [7] Charles, E. Y. A. 2006. Supervised and Unsupervised Weight and Delay Adaptation earning in temporal Coding Spiking Neural Networks. Thesis Doctor of Philosophy Cardiff, Cardiff.

      [8] Ghosh-Dastidar, S. & H. Adeli 2007. Improved spiking neural networks for EEG classification and epilepsy and seizure detection. Integrated Computer-Aided Engineering 14(3): 187-212.

      [9] Ghosh-Dastidar, S. & H. Adeli 2009. A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Networks 22(10): 1419-1431.

      [10] Grüning, A. & I. Sporea 2012. Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding. Neural Processing Letters 36(2): 117-134.

      [11] Jianguo, X. & M. J. Embrechts 2001. Supervised learning with spiking neural networks. International Joint Conference on Neural Networks, 2001. Proceedings. IJCNN '01. pp. 1772-1777 vol.3.

      [12] Ruf, B. & M. Schmitt 1998. Self-organization of spiking neurons using action potential timing. IEEE Transactions on Neural Networks 9(3): 575-578.

      [13] Sahran, S. 2007. Application of Spiking Neural Networks and the Bees Algorithm to Control Chart Pattern Recognition.Thesis Doctor of Philosophy Cardiff, Cardiff.

      [14] Sporea, I. & A. Grüning 2012. Supervised Learning in Multilayer Spiking Neural Networks. Neural Computation 25(2): 473-509.

      [15] Zhang, C.-w. & H.-j. Liu 2009. A New Supervised Spiking Neural Network. Second International Conference on Intelligent Computation Technology and Automation, 2009. ICICTA '09. 1 pp. 23-26.

      [16] Kasabov ,N.K.(2014).NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Networks , 52,62–76.

      [17] Tao, X. & H. Michel 2004. Data clustering via spiking neural networks through spike timing-dependent plasticity. 1 pp. 168–173.

      [18] Awadalla, M. H. A. & M. Abdellatif Sadek 2012. Spiking neural network-based control chart pattern recognition. Alexandria Engineering Journal 51(1): 27-35.

      [19] QingXiang, W., D. Bell, Q. Guilin & C. Jianyong 2006. Knowledge Representation and Learning Mechanism Based on Networks of Spiking Neurons. Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on. 4 pp. 2796-2801.

      [20] Abdullah H. Almasri and Shahnorbanun Sahran, 2014. Time Window, Spike Time and Threshold Boundary for Spiking Neural Network Applications. Journal of Applied Sciences, 14: 317-324.

      [21] Wolfgang Maass & C. M. Bishop. 2001. Pulsed Neural Networks Ed. 1st Edition. Cambridge, Massachusetts: The MIT Press.

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  • How to Cite

    Almasri, A. H., Sahran, S., & Yafi, E. (2018). Spike Response Function Weight and Delay Updating Strategy Using Delay Rules. International Journal of Engineering & Technology, 7(4.29), 173-177. https://doi.org/10.14419/ijet.v7i4.29.21965

    Received date: 2018-11-28

    Accepted date: 2018-11-28

    Published date: 2018-11-26