Phase Precession and Recession with STDP and Anti-STDP

  • Răzvan V. Florian
  • Raul C. Mureşan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)


We show that standard, Hebbian spike-timing dependent plasticity (STDP) induces the precession of the firing phase of neurons in oscillatory networks, while anti-Hebbian STDP induces phase recession. In networks that are subject to oscillatory inhibition, the intensity of excitatory input relative to the inhibitory one determines whether the phase can precess due to STDP or whether the phase is fixed. This phenomenon can give a very simple explanation to the experimentally-observed hippocampal phase precession. Modulation of STDP can lead, through precession and recession, to the synchronization of the firing of a trained neuron to a target phase.


Excitatory Input Inhibitory Input Postsynaptic Neuron Neural Computation Hebbian Learning 
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.


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  1. 1.
    Markram, H., Lübke, J., Frotscher, M., Sakmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213–215 (1997)CrossRefGoogle Scholar
  2. 2.
    Bi, G.Q., Poo, M.M.: Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience 18, 10464–10472 (1998)Google Scholar
  3. 3.
    Dan, Y., Poo, M.M.: Spike timing-dependent plasticity of neural circuits. Neuron 44, 23–30 (2004)CrossRefGoogle Scholar
  4. 4.
    Dan, Y., Poo, M.M.: Hebbian depression of isolated neuromuscular synapses in vitro. Science 256, 1570–1573 (1992)CrossRefGoogle Scholar
  5. 5.
    Bell, C.C., Han, V.Z., Sugawara, Y., Grant, K.: Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature 387, 278–281 (1997)CrossRefGoogle Scholar
  6. 6.
    Egger, V., Feldmeyer, D., Sakmann, B.: Coincidence detection and changes of synaptic efficacy in spiny stellate neurons in rat barrel cortex. Nature Neuroscience 2, 1098–1105 (1999)CrossRefGoogle Scholar
  7. 7.
    Roberts, P.D., Bell, C.C.: Spike timing dependent synaptic plasticity in biological systems. Biological Cybernetics 87, 392–403 (2002)MATHCrossRefGoogle Scholar
  8. 8.
    Kempter, R., Gerstner, W., van Hemmen, J.L.: Hebbian learning and spiking neurons. Physical Review E 59, 4498–4514 (1999)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Kempter, R., Gerstner, W., van Hemmen, J.L.: Intrinsic stabilization of output rates by spike-based Hebbian learning. Neural Computation 13, 2709–2742 (2001)MATHCrossRefGoogle Scholar
  10. 10.
    Song, S., Miller, K.D., Abbott, L.F.: Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience 3, 919–926 (2000)CrossRefGoogle Scholar
  11. 11.
    Roberts, P.: Computational consequences of temporally asymmetric learning rules: I. Differential Hebbian learning. Journal of Computational Neuroscience 7, 235–246 (1999)CrossRefGoogle Scholar
  12. 12.
    Rao, R.P.N., Sejnowski, T.J.: Spike-timing-dependent Hebbian plasticity as temporal difference learning. Neural Computation 13, 2221–2237 (2001)MATHCrossRefGoogle Scholar
  13. 13.
    Toyoizumi, T., Pfister, J.P., Aihara, K., Gerstner, W.: Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model. In: Saul, L., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17, pp. 1409–1416. MIT Press, Cambridge (2005)Google Scholar
  14. 14.
    Bell, A.J., Parrara, L.C.: Maximising sensitivity in a spiking network. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17. MIT Press, Cambridge (2004)Google Scholar
  15. 15.
    Chechik, G.: Spike time dependent plasticity and information maximization. Neural Computation 15, 1481–1510 (2003)MATHCrossRefGoogle Scholar
  16. 16.
    Bohte, S.M., Mozer, C.: Reducing spike train variability: A computational theory of spike-timing dependent plasticity. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17. MIT Press, Cambridge (2004)Google Scholar
  17. 17.
    Hopfield, J.J., Brody, C.D.: Learning rules and network repair in spike-timing-based computation networks. Proceedings of the National Academy of Sciences 101, 337–342 (2004)CrossRefGoogle Scholar
  18. 18.
    Legenstein, R., Naeger, C., Maass, W.: What can a neuron learn with spike-timing-dependent plasticity? Neural Computation 17, 2337–2382 (2005)MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Abbott, L.F., Gerstner, W.: Homeostasis and learning through spike-timing dependent plasticity. In: Gutkin, B., Hansel, D., Meunier, C., Dalibard, J., Chow, C. (eds.) Methods and Models in Neurophysics: Proceedings of the Les Houches Summer School 2003, Elsevier Science, Amsterdam (2005)Google Scholar
  20. 20.
    Florian, R.V.: A reinforcement learning algorithm for spiking neural networks. In: Zaharie, D., Petcu, D., Negru, V., Jebelean, T., Ciobanu, G., Cicortaş, A., Abraham, A., Paprzycki, M. (eds.) Proceedings of the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2005), pp. 299–306. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  21. 21.
    Florian, R.V.: Reinforcement learning through modulation of spike-timing dependent plasticity. Neural Computation (2006) (In press)Google Scholar
  22. 22.
    Buzsaki, G.: Theta oscillations in the hippocampus. Neuron 33, 325–340 (2002)CrossRefGoogle Scholar
  23. 23.
    Buzsaki, G., Draguhn, A.: Neuronal oscillations in cortical networks. Science 304, 1926–1929 (2004)CrossRefGoogle Scholar
  24. 24.
    Troyer, T.W., Miller, K.D.: Physiological gain leads to high ISI variability in a simple model of a cortical regular spiking cell. Neural Computation 9, 971–983 (1997)CrossRefGoogle Scholar
  25. 25.
    Abbott, L.F., Nelson, S.B.: Synaptic plasticity: taming the beast. Nature Neuroscience 3, 1178–1183 (2000)CrossRefGoogle Scholar
  26. 26.
    Tsodyks, M.V., Skaggs, W.E., Sejnowski, T.J., McNaughton, B.L.: Population dynamics and theta rhythm phase precession of hippocampal place cell firing: A spiking neuron model. Hippocampus 6, 271–280 (1996)CrossRefGoogle Scholar
  27. 27.
    Mehta, M.R., Lee, A.K., Wilson, M.A.: Role of experience and oscillations in transforming a rate code into a temporal code. Nature 417, 741–746 (2002)CrossRefGoogle Scholar
  28. 28.
    Turrigiano, G.G., Nelson, S.B.: Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience 5, 97–107 (2004)CrossRefGoogle Scholar
  29. 29.
    Gerstner, W., Kistler, W.M.: Spiking neuron models. Cambridge University Press, Cambridge (2002)MATHGoogle Scholar
  30. 30.
    O’Keefe, J., Recce, M.L.: Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317–330 (1993)CrossRefGoogle Scholar
  31. 31.
    Mehta, M.R., Quirk, M.C., Wilson, M.A.: Experience-dependent asymmetric shape of hippocampal receptive fields. Neuron 25, 707–715 (2000)CrossRefGoogle Scholar
  32. 32.
    Scarpetta, S., Marinaro, M.: A learning rule for place fields in a cortical model: Theta phase precession as a network effect. Hippocampus 15, 979–989 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Răzvan V. Florian
    • 1
    • 2
  • Raul C. Mureşan
    • 1
    • 3
  1. 1.Center for Cognitive and Neural Studies (Coneural)Cluj-NapocaRomania
  2. 2.Institute for Interdisciplinary Experimental ResearchBabeş-Bolyai UniversityCluj-NapocaRomania
  3. 3.Frankfurt Institute for Advanced StudiesJohann Wolfgang Goethe UniversityFrankfurt am MainGermany

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