Supervised Learning Algorithm for Multi-spike Liquid State Machines

  • Xianghong LinEmail author
  • Qian Li
  • Dan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)


The liquid state machines have been well applied for solving large-scale spatio-temporal pattern recognition problems. The current supervised learning algorithms for the liquid state machines of spiking neurons generally only adjust the synaptic weights in the output layer, the synaptic weights of input and hidden layers are generated in the process of network structure initialization and no longer change. That is to say, the hidden layer is a static network, which usually neglects the dynamic characteristics of the liquid state machines. Therefore, a new supervised learning algorithm for the liquid state machines of spiking neurons based on bidirectional modification is proposed, which not only adjusts the synaptic weights in the output layer, but also changes the synaptic weights in the input and hidden layers. The algorithm is successfully applied to the spike trains learning. The experimental results show that the proposed learning algorithm can effectively learn the spike trains pattern with different learning parameter.


Spiking neural network Liquid state machine Bidirectional modification Synaptic plasticity 



The work is supported by the National Natural Science Foundation of China under Grant No. 61762080, and the Medium and Small Scale Enterprises Technology Innovation Foundation of Gansu Province under Grant No. 17CX2JA038.


  1. 1.
    Bohte, S.M.: The evidence for neural information processing with precise spike-times: a survey. Nat. Comput. 3(2), 195–206 (2004)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Quiroga, R.Q., Panzeri, S.: Principles of Neural Coding. CRC Press, Boca Raton (2013)CrossRefGoogle Scholar
  3. 3.
    Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15(5), 1063–1070 (2004)CrossRefGoogle Scholar
  4. 4.
    Ostojic, S., Brunel, N.: From spiking neuron models to linear-nonlinear models. PLoS Comput. Biol. 7(1), e1001056 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Maass, W.: Lower bounds for the computational power of networks of spiking neurons. Neural Comput. 8(1), 1–40 (2014)CrossRefGoogle Scholar
  6. 6.
    Ghosh-Dastidar, S., Adeli, H.: Spiking neural networks. Int. J. Neural Syst. 19(4), 295–308 (2009)CrossRefGoogle Scholar
  7. 7.
    Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)CrossRefGoogle Scholar
  8. 8.
    Maass, W.: Liquid state machines: motivation, theory, and applications. In: Computability in Context: Computation and Logic in the Real World, pp. 275–296. Imperial College Press, London (2011)CrossRefGoogle Scholar
  9. 9.
    Rosselló, J.L., Alomar, M.L., Morro, A., et al.: High-density liquid-state machine circuitry for time-series forecasting. Int. J. Neural Syst. 26(5), 1550036 (2016)CrossRefGoogle Scholar
  10. 10.
    Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)CrossRefGoogle Scholar
  11. 11.
    Burgsteiner, H., Kröll, M., Leopold, A., et al.: Movement prediction from real-world images using a liquid state machine. Appl. Intell. 26(2), 99–109 (2007)CrossRefGoogle Scholar
  12. 12.
    Sala, D.A., Brusamarello, V.J., Azambuja, R.D., et al.: Positioning control on a collaborative robot by sensor fusion with liquid state machines. In: 2017 IEEE International Instrumentation and Measurement Technology Conference, pp. 1–6. IEEE, Turin, Italy (2017)Google Scholar
  13. 13.
    Zhang, Y., Li, P., Jin, Y., et al.: A digital liquid state machine with biologically inspired learning and its application to speech recognition. IEEE Trans. Neural Netw. Learn. Syst. 26(11), 2635–2649 (2015)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Jin, Y., Li, P.: Performance and robustness of bio-inspired digital liquid state machines: a case study of speech recognition. Neurocomputing 226, 145–160 (2017)CrossRefGoogle Scholar
  15. 15.
    Zoubi, O.A., Awad, M., Kasabov, N.K.: Anytime multipurpose emotion recognition from EEG data using a liquid state machine based framework. Artif. Intell. Med. 86, 1–8 (2018)CrossRefGoogle Scholar
  16. 16.
    Xue, F., Guan, H., Li, X.: Improving liquid state machine with hybrid plasticity. In: Advanced Information Management, Communicates, Electronic and Automation Control Conference, pp. 1955–1959. IEEE, Xi’an, China (2017)Google Scholar
  17. 17.
    Kroese, B., van der Smagt, P.: An Introduction to Neural Networks, 8th edn. The University of Amsterdam, Amsterdam (1996)Google Scholar
  18. 18.
    Ponulak, F., Kasinski, A.: Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting. Neural Comput. 22(2), 467–510 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Li, C.Y., Lu, J.T., Wu, C.P., et al.: Bidirectional modification of presynaptic neuronal excitability accompanying spike timing-dependent synaptic plasticity. Neuron 41(2), 257–268 (2004)CrossRefGoogle Scholar
  20. 20.
    Lin, X., Wang, X., Hao, Z.: Supervised learning in multilayer spiking neural networks with inner products of spike trains. Neurocomputing 237, 59–70 (2017)CrossRefGoogle Scholar
  21. 21.
    Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, New York (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and EngineeringNorthwest Normal UniversityLanzhouChina

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