Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns

  • Qiang YuEmail author
  • Huajin Tang
  • Jun Hu
  • Kay Chen Tan
Part of the Intelligent Systems Reference Library book series (ISRL, volume 126)


This chapter introduces a new temporal learning rule, namely the Precise-Spike-Driven (PSD) Synaptic Plasticity, for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff (WH) rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters.


Afferent Neuron Spike Train Neuron Model Learning Rule Training Epoch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity, 1st edn. Cambridge University Press, Cambridge (2002)CrossRefzbMATHGoogle Scholar
  2. 2.
    Ghosh-Dastidar, S., Adeli, H.: Spiking neural networks. Int. J. Neural Syst. 19(04), 295–308 (2009)CrossRefGoogle Scholar
  3. 3.
    Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10(9), 1659–1671 (1997)CrossRefGoogle Scholar
  4. 4.
    Shadlen, M.N., Movshon, J.A.: Synchrony unbound: review a critical evaluation of the temporal binding hypothesis. Neuron 24, 67–77 (1999)CrossRefGoogle Scholar
  5. 5.
    Gütig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420–428 (2006)CrossRefGoogle Scholar
  6. 6.
    Widrow, B., Lehr, M.: 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proc. IEEE 78(9), 1415–1442 (1990)CrossRefGoogle Scholar
  7. 7.
    Knudsen, E.I.: Supervised learning in the brain. J. Neurosci. 14(7), 3985–3997 (1994)Google Scholar
  8. 8.
    Thach, W.T.: On the specific role of the cerebellum in motor learning and cognition: clues from PET activation and lesion studies in man. Behav. Brain Sci. 19(3), 411–431 (1996)CrossRefGoogle Scholar
  9. 9.
    Ito, M.: Mechanisms of motor learning in the cerebellum. Brain Res. 886(1–2), 237–245 (2000)CrossRefGoogle Scholar
  10. 10.
    Carey, M.R., Medina, J.F., Lisberger, S.G.: Instructive signals for motor learning from visual cortical area MT. Nat. Neurosci. 8(6), 813–819 (2005)CrossRefGoogle Scholar
  11. 11.
    Brader, J.M., Senn, W., Fusi, S.: Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural Comput. 19(11), 2881–2912 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Bohte, S.M., Kok, J.N., Poutré, J.A.L.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1–4), 17–37 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Ponulak, F.: ReSuMe-new supervised learning method for spiking neural networks. Institute of Control and Information Engineering, Poznoń University of Technology, Technical report (2005)Google Scholar
  14. 14.
    Florian, R.V.: The chronotron: a neuron that learns to fire temporally precise spike patterns. PLoS One 7(8), e40,233 (2012)Google Scholar
  15. 15.
    Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: SPAN: spike pattern association neuron for learning spatio-temporal spike patterns. Int. J. Neural Syst. 22(04), 1250,012 (2012)Google Scholar
  16. 16.
    Yu, Q., Tang, H., Tan, K.C., Li, H.: Rapid feedforward computation by temporal encoding and learning with spiking neurons. IEEE Trans. Neural Netw. Learn. Syst. 24(10), 1539–1552 (2013)CrossRefGoogle Scholar
  17. 17.
    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)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569–1572 (2003)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Rossum, M.: A novel spike distance. Neural Comput. 13(4), 751–763 (2001)CrossRefzbMATHGoogle Scholar
  20. 20.
    Rieke, F., Warland, D., van Steveninck, R.D., Bialek, W.: Spikes: Exploring the Neural Code, 1st edn. MIT Press, Cambridge (1997)zbMATHGoogle Scholar
  21. 21.
    Hu, J., Tang, H., Tan, K.C., Li, H., Shi, L.: A spike-timing-based integrated model for pattern recognition. Neural Comput. 25(2), 450–472 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Gardner, E.: The space of interactions in neural networks models. J. Phys. A21, 257–270 (1988)MathSciNetzbMATHGoogle Scholar
  23. 23.
    Foehring, R.C., Lorenzon, N.M.: Neuromodulation, development and synaptic plasticity. Can. J. Exp. Psychol./Rev. Canadienne de Psychologie Expérimentale 53(1), 45–61 (1999)CrossRefGoogle Scholar
  24. 24.
    Seamans, J.K., Yang, C.R., et al.: The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Prog. Neurobiol. 74(1), 1–57 (2004)CrossRefGoogle Scholar
  25. 25.
    Artola, A., Bröcher, S., Singer, W.: Different voltage-dependent thresholds for inducing long-term depressiona and long-term potentiation in slices of rat visual cortex. Nature 347, 69–72 (1990)CrossRefGoogle Scholar
  26. 26.
    Ngezahayo, A., Schachner, M., Artola, A.: Synaptic activity modulates the induction of bidirectional synaptic changes in adult mouse hippocampus. J. Neurosci. 20(7), 2451–2458 (2000)Google Scholar
  27. 27.
    Lisman, J., Spruston, N.: Postsynaptic depolarization requirements for LTP and LTD: a critique of spike timing-dependent plasticity. Nat. Neurosci. 8(7), 839–841 (2005)CrossRefGoogle Scholar
  28. 28.
    Froemke, R.C., Poo, M.M., Dan, Y.: Spike-timing-dependent synaptic plasticity depends on dendritic location. Nature 434(7030), 221–225 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute for Infocomm ResearchSingaporeSingapore
  2. 2.College of Computer ScienceSichuan UniversityChengduChina
  3. 3.AGI TechnologiesSingaporeSingapore
  4. 4.Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong

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