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An Evolutionary Neuron Model with Dendritic Computation for Classification and Prediction

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12836)

Abstract

Advances in the understanding of dendrites promote the development of dendritic computation. For decades, the researchers are committed to proposing an appropriate neural model, which may feedback the research on neurons. This paper aims to employ an effective metaheuristic optimization algorithm as the learning algorithms to train the dendritic neuron model (DNM). The powerful ability of the backpropagation (BP) algorithm to train artificial neural networks led us to employ it as a learning algorithm for a conventional DNM, but this also inevitably causes the DNM to suffer from the drawbacks of the algorithm. Therefore, a metaheuristic optimization algorithm, named the firefly algorithm (FA) is adopted to train the DNM (FADNM). Experiments on twelve datasets involving classification and prediction are performed to evaluate the performance. The experimental results and corresponding statistical analysis show that the learning algorithm plays a decisive role in the performance of the DNM. It is worth emphasizing that the FADNM incorporates an invaluable neural pruning scheme to eliminate superfluous synapses and dendrites, simplifying its structure and forming a unique morphology. This simplified morphology can be implemented in hardware through logic circuits, which approximately has no effect on the accuracy of the original model. The hardwareization enables the FADNM to efficiently process high-speed data streams for large-scale data, which leads us to believe that it might be a promising technology to deal with big data.

Keywords

  • Dendritic neuron model
  • Firefly algorithm
  • Classification
  • Prediction
  • Hardwareization

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References

  1. Asuncion, A., Newman, D.: Uci machine learning repository (2007)

    Google Scholar 

  2. Boccardi, F., Heath, R.W., Lozano, A., Marzetta, T.L., Popovski, P.: Five disruptive technology directions for 5g. IEEE Commun. Mag. 52(2), 74–80 (2014)

    CrossRef  Google Scholar 

  3. y Cajal, S.R.: Histologie du système nerveux de l’homme & des vertébrés: Cervelet, cerveau moyen, rétine, couche optique, corps strié, écorce cérébrale générale & régionale, grand sympathique, vol. 2. A. Maloine (1911)

    Google Scholar 

  4. Chen, D.P.: High speed logic circuit simulator. US Patent 5,734,869 (1998)

    Google Scholar 

  5. Chen, W., Sun, J., Gao, S., Cheng, J.J., Wang, J., Todo, Y.: Using a single dendritic neuron to forecast tourist arrivals to Japan. IEICE Trans. Inf. Syst. 100(1), 190–202 (2017)

    CrossRef  Google Scholar 

  6. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)

    CrossRef  Google Scholar 

  7. Dutta, S., Singh, D.: High-speed computation in arithmetic logic circuit. US Patent App. 10/005,551 (2003)

    Google Scholar 

  8. Flach, P.A., Hernández-Orallo, J., Ramirez, C.F.: A coherent interpretation of AUC as a measure of aggregated classification performance. In: ICML (2011)

    Google Scholar 

  9. Fortier, P.A., Bray, C.: Influence of asymmetric attenuation of single and paired dendritic inputs on summation of synaptic potentials and initiation of action potentials. Neuroscience 236, 195–209 (2013)

    CrossRef  Google Scholar 

  10. Gabbiani, F., Krapp, H.G., Koch, C., Laurent, G.: Multiplicative computation in a visual neuron sensitive to looming. Nature 420(6913), 320–324 (2002)

    CrossRef  Google Scholar 

  11. García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the CEC2005 special session on real parameter optimization. J. Heuristics 15(6), 617 (2009)

    CrossRef  Google Scholar 

  12. Gerstner, W., Kistler, W.M., Naud, R., Paninski, L.: Neuronal dynamics: from single neurons to networks and models of cognition. Cambridge University Press (2014)

    Google Scholar 

  13. Gidon, A., et al.: Dendritic action potentials and computation in human layer 2/3 cortical neurons. Science 367(6473), 83–87 (2020)

    CrossRef  Google Scholar 

  14. Haykin, S.: Neural Networks and Learning Machines, 3/E. Pearson Education India (2010)

    Google Scholar 

  15. He, J., Wu, J., Yuan, G., Todo, Y.: Dendritic branches of dnm help to improve approximation accuracy. In: 2019 6th International Conference on Systems and Informatics (ICSAI), pp. 533–541. IEEE (2019)

    Google Scholar 

  16. Ji, J., Gao, S., Cheng, J., Tang, Z., Todo, Y.: An approximate logic neuron model with a dendritic structure. Neurocomputing 173, 1775–1783 (2016)

    CrossRef  Google Scholar 

  17. Jia, D., Zheng, S., Yang, L., Todo, Y., Gao, S.: A dendritic neuron model with nonlinearity validation on Istanbul stock and Taiwan futures exchange indexes prediction. In: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 242–246. IEEE (2018)

    Google Scholar 

  18. Jiang, T., Gao, S., Wang, D., Ji, J., Todo, Y., Tang, Z.: A neuron model with synaptic nonlinearities in a dendritic tree for liver disorders. IEEJ Trans. Electr. Electron. Eng. 12(1), 105–115 (2017)

    CrossRef  Google Scholar 

  19. Jiang, T., Wang, D., Ji, J., Todo, Y., Gao, S.: Single dendritic neuron with nonlinear computation capacity: a case study on XOR problem. In: 2015 IEEE International Conference on Progress in Informatics and Computing (PIC), pp. 20–24. IEEE (2015)

    Google Scholar 

  20. Koch, C.: Biophysics of computation: information processing in single neurons. Oxford University Press (2004)

    Google Scholar 

  21. Koch, C., Poggio, T., Torre, V.: Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing. Proc. Natl. Acad. Sci. 80(9), 2799–2802 (1983)

    CrossRef  Google Scholar 

  22. Lei, L., Zhong, Z., Zheng, K., Chen, J., Meng, H.: Challenges on wireless heterogeneous networks for mobile cloud computing. IEEE Wirel. Commun. 20(3), 34–44 (2013)

    CrossRef  Google Scholar 

  23. Lomotey, R.K., Deters, R.: Towards knowledge discovery in big data. In: 2014 IEEE 8th International Symposium on Service Oriented System Engineering, pp. 181–191. IEEE (2014)

    Google Scholar 

  24. London, M., Häusser, M.: Dendritic computation. Annu. Rev. Neurosci. 28, 503–532 (2005)

    CrossRef  Google Scholar 

  25. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)

    MathSciNet  CrossRef  Google Scholar 

  26. McHugh, M.L.: Interrater reliability: the kappa statistic. Biochemia Med. 22(3), 276–282 (2012)

    MathSciNet  CrossRef  Google Scholar 

  27. Musolesi, M.: Big mobile data mining: good or evil? IEEE Internet Comput. 18(1), 78–81 (2014)

    MathSciNet  CrossRef  Google Scholar 

  28. Polsky, A., Mel, B.W., Schiller, J.: Computational subunits in thin dendrites of pyramidal cells. Nat. Neurosci. 7(6), 621–627 (2004)

    CrossRef  Google Scholar 

  29. Qian, X., Wang, Y., Cao, S., Todo, Y., Gao, S.: MrDNM: a novel mutual information-based dendritic neuron model. Comput. Intell. Neurosci. 2019 (2019)

    Google Scholar 

  30. Reiff, D.F., Plett, J., Mank, M., Griesbeck, O., Borst, A.: Visualizing retinotopic half-wave rectified input to the motion detection circuitry of drosophila. Nat. Neurosci. 13(8), 973–978 (2010)

    CrossRef  Google Scholar 

  31. Sasaki, Y., et al.: The truth of the f-measure. 2007 (2007)

    Google Scholar 

  32. Segev, I.: Sound grounds for computing dendrites. Nature 393(6682), 207–208 (1998)

    CrossRef  Google Scholar 

  33. Sha, Z., Hu, L., Todo, Y., Ji, J., Gao, S., Tang, Z.: A breast cancer classifier using a neuron model with dendritic nonlinearity. IEICE Trans. Inf. Syst. 98(7), 1365–1376 (2015)

    CrossRef  Google Scholar 

  34. Sietsma, J.: Neural net pruning-why and how. In: Proceedings of International Conference on Neural Networks, San Diego, CA, vol. 1, pp. 325–333 (1988)

    Google Scholar 

  35. Song, Z., Gao, S., Yu, Y., Sun, J., Todo, Y.: Multiple chaos embedded gravitational search algorithm. IEICE Trans. Inf. Syst. 100(4), 888–900 (2017)

    CrossRef  Google Scholar 

  36. Stehman, S.V.: Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 62(1), 77–89 (1997)

    CrossRef  Google Scholar 

  37. Tang, C., Ji, J., Tang, Y., Gao, S., Tang, Z., Todo, Y.: A novel machine learning technique for computer-aided diagnosis. Eng. Appl. Artif. Intell. 92, 103,627 (2020)

    Google Scholar 

  38. Tang, Y., Ji, J., Gao, S., Dai, H., Yu, Y., Todo, Y.: A pruning neural network model in credit classification analysis. Comput. Intell. Neurosci. 2018 (2018)

    Google Scholar 

  39. Tang, Z., Kuratu, M., Tamura, H., Ishizuka, O., Tanno, K.: A neuron model based on dendritic mechanism. IEICE 83, 486–498 (2000)

    Google Scholar 

  40. Tang, Z., Tamura, H., Ishizuka, O., Tanno, K.: A neuron model with interaction among synapses. IEEJ Trans. Electron., Inf. Syst. 120(7), 1012–1019 (2000)

    Google Scholar 

  41. Taylor, W.R., He, S., Levick, W.R., Vaney, D.I.: Dendritic computation of direction selectivity by retinal ganglion cells. Science 289(5488), 2347–2350 (2000)

    CrossRef  Google Scholar 

  42. Teng, F., Todo, Y.: Dendritic neuron model and its capability of approximation. In: 2019 6th International Conference on Systems and Informatics (ICSAI), pp. 542–546. IEEE (2019)

    Google Scholar 

  43. Todo, Y., Tamura, H., Yamashita, K., Tang, Z.: Unsupervised learnable neuron model with nonlinear interaction on dendrites. Neural Netw. 60, 96–103 (2014)

    CrossRef  Google Scholar 

  44. Todo, Y., Tang, Z., Todo, H., Ji, J., Yamashita, K.: Neurons with multiplicative interactions of nonlinear synapses. Int. J. Neural Syst. 29(08), 1950,012 (2019)

    Google Scholar 

  45. Wang, H., et al.: Firefly algorithm with neighborhood attraction. Inf. Sci. 382, 374–387 (2017)

    CrossRef  Google Scholar 

  46. Wang, X., Tang, Z., Tamura, H., Ishii, M., Sun, W.: An improved backpropagation algorithm to avoid the local minima problem. Neurocomputing 56, 455–460 (2004)

    CrossRef  Google Scholar 

  47. Wang, Y., Liu, S.C.: Multilayer processing of spatiotemporal spike patterns in a neuron with active dendrites. Neural Comput. 22(8), 2086–2112 (2010)

    MathSciNet  CrossRef  Google Scholar 

  48. Yang, X.S.: Firefly algorithm, levy flights and global optimization. In: Bramer, M., Ellis, R., Petridis, M. (eds.) Research and Development in Intelligent Systems XXVI, pp. 209–218. Springer, London (2010). https://doi.org/10.1007/978-1-84882-983-1_15

  49. Yu, Y., Song, S., Zhou, T., Yachi, H., Gao, S.: Forecasting house price index of China using dendritic neuron model. In: 2016 International Conference on Progress in Informatics and Computing (PIC), pp. 37–41. IEEE (2016)

    Google Scholar 

  50. Yu, Y., Wang, Y., Gao, S., Tang, Z.: Statistical modeling and prediction for tourism economy using dendritic neural network. Comput. Intell. Neurosci. 2017 (2017)

    Google Scholar 

  51. Zhao, K., Zhang, T., Lai, X., Dou, C., Yue, D.: A dendritic neuron based very short-term prediction model for photovoltaic power. In: 2018 Chinese Control and Decision Conference (CCDC), pp. 1106–1110. IEEE (2018)

    Google Scholar 

  52. Zhou, T., Chu, C., Song, S., Wang, Y., Gao, S.: A dendritic neuron model for exchange rate prediction. In: 2015 IEEE International Conference on Progress in Informatics and Computing (PIC), pp. 10–14. IEEE (2015)

    Google Scholar 

  53. Zhou, T., Gao, S., Wang, J., Chu, C., Todo, Y., Tang, Z.: Financial time series prediction using a dendritic neuron model. Knowl.-Based Syst. 105, 214–224 (2016)

    CrossRef  Google Scholar 

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Acknowledgment

This work was supported by a Project of the Guangdong Basic and Applied Basic Research Fund (No. 2019A1515111139), the Nature Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 19KJB520015), the Talent Development Project of Taizhou University (No. TZXY2018QDJJ006), and the general item of Hunan philosophy and Social Science Foundation (20YBA260), namely Research on Financial Risk Management of Supply Chain in Hunan Free Trade Zone with Artificial Intelligence. The authors would like to thank the Otsuka Toshimi Scholarship Foundation for its support.

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Tang, C., Song, Z., Tang, Y., Tang, H., Wang, Y., Ji, J. (2021). An Evolutionary Neuron Model with Dendritic Computation for Classification and Prediction. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-84522-3_2

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