Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Competitive Hebbian learning through spike-timing-dependent synaptic plasticity

Abstract

Hebbian models of development and learning require both activity-dependent synaptic plasticity and a mechanism that induces competition between different synapses. One form of experimentally observed long-term synaptic plasticity, which we call spike-timing-dependent plasticity (STDP), depends on the relative timing of pre- and postsynaptic action potentials. In modeling studies, we find that this form of synaptic modification can automatically balance synaptic strengths to make postsynaptic firing irregular but more sensitive to presynaptic spike timing. It has been argued that neurons in vivo operate in such a balanced regime. Synapses modifiable by STDP compete for control of the timing of postsynaptic action potentials. Inputs that fire the postsynaptic neuron with short latency or that act in correlated groups are able to compete most successfully and develop strong synapses, while synapses of longer-latency or less-effective inputs are weakened.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The STDP modification function.
Figure 2: Balanced excitation and irregular firing produced by STDP.
Figure 3: Correlation between pre- and postsynaptic action potentials before and after STDP.
Figure 4: Reduction of latency by STDP.
Figure 5: Effects of input correlation, firing rate or variability on steady-state peak synaptic conductances.

References

  1. Hebb, D. O. The Organization of Behavior: A Neuropsychological Theory (Wiley, New York, 1949).

    Google Scholar 

  2. Guillery, R. W. Binocular competition in the control of geniculate cell growth. J. Comp. Neurol. 144, 117–130 (1972).

    Article  CAS  Google Scholar 

  3. Miller, K. D. Synaptic economics: competition and cooperation in synaptic plasticity. Neuron 17, 371–374 (1996).

    Article  CAS  Google Scholar 

  4. Miller, K. D. in Models of Neural Networks, III (eds. Domany, E., van Hemmen, J. L. & Schulten, K.) 55–78 (Springer, New York, 1996).

    Book  Google Scholar 

  5. Bear, M. F. & Malenka, R. C. Synaptic plasticity: LTP and LTD. Curr. Opin. Neurobiol. 4, 389– 399 (1994).

    Article  CAS  Google Scholar 

  6. Miller, K. D. & MacKay, D. J. C. The role of constraints in Hebbian learning. Neural Comput. 6, 100– 126 (1994).

    Article  Google Scholar 

  7. Turrigiano, G. G., Leslie K. R., Desai, N. S., Rutherford L. C. & Nelson, S. B. Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391 , 892–896 (1998).

    Article  CAS  Google Scholar 

  8. Davis, G. W. & Goodman, C. S. Synapse-specific control of synaptic efficacy at the terminals of a single neuron. Nature 392, 82–86 (1998).

    Article  CAS  Google Scholar 

  9. O'Brien, R. J. et. al. Activity-dependent modulation of synaptic AMPA receptor accumulations . Neuron 21, 1067–1078 (1998).

    Article  CAS  Google Scholar 

  10. Bienenstock, E. L., Cooper, L. N. & Munro, P. W. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J. Neurosci. 2, 32–48 (1982).

    Article  CAS  Google Scholar 

  11. Levy, W. B. & Steward, D. Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience 8, 791–797 (1983).

    Article  CAS  Google Scholar 

  12. Gustafsson, B., Wigstrom, H., Abraham, W. C. & Huang, Y.-Y. Long-term potentiation in the hippocampus using depolarizing current pulses as the conditioning stimulus to single volley synaptic potentials. J. Neurosci. 7, 774–780 (1987) .

    Article  CAS  Google Scholar 

  13. Debanne, D., Gahwiler, B. H. & Thompson, S. M. Asynchronous pre- and postsynaptic activity induces associative long-term depression in area CA1 of the rat hippocampus in vitro. Proc. Natl. Acad. Sci. USA 91, 1148–1152 (1994).

    Article  CAS  Google Scholar 

  14. Markram, H., Lubke, J., Frotscher, M. & Sakmann, B. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213–215 (1997).

    Article  CAS  Google Scholar 

  15. Magee, J. C. & Johnston, D. A synaptically controlled, associative signal for Hebbian plasticity in hippocampal neurons. Science 275, 209–213 (1997).

    Article  CAS  Google Scholar 

  16. 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).

    Article  CAS  Google Scholar 

  17. Debanne, D., Gahwiler, B. H. & Thompson, S. M. Long-term synaptic plasticity between pairs of individual CA3 pyramidal cells in rat hippocampal slice cultures. J. Physiol. (Lond.) 507, 237–247 (1998).

    Article  CAS  Google Scholar 

  18. Bi, G.-q. & Poo, M.-m. Activity-induced synaptic modifications in hippocampal culture: dependence on spike timing, synaptic strength and cell type. J. Neurosci. 18, 10464– 10472 (1998).

    Article  CAS  Google Scholar 

  19. Zhang, L. I., Tao, H. W., Holt C. E., Harris W. A. & Poo M.-m. A critical window for cooperation and competition among developing retinotectal synapses. Nature 395, 37–44 (1998).

    Article  CAS  Google Scholar 

  20. Feldman, D. E. Timing-based LTP and LTD at vertical inputs to layer II/III pyramidal cells in rat barrel cortex. Neuron 27, 45– 56 (2000).

    Article  CAS  Google Scholar 

  21. Gerstner, W., Ritz, R. & van Hemmen, J. L. Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biol. Cybern. 69, 503 –515 (1993).

    Article  CAS  Google Scholar 

  22. Minai, A. A. & Levy, W. B. Sequence learning in a single trial . INNS World Congress of Neural Networks II 505– 508 (1993).

  23. Abbott, L. F. & Blum, K. I. Functional significance of long-term potentiation for sequence learning and prediction. Cereb. Cortex 6, 406–416 (1996).

    Article  CAS  Google Scholar 

  24. Roberts, P. D. Computational consequences of temporally asymmetric learning rules: I. Differential Hebbian learning. J. Comput. Neurosci. 7, 235–246 (1999) .

    Article  CAS  Google Scholar 

  25. Gerstner, W., Kempter, R., van Hemmen, J. L. & Wagner, H. A neuronal learning rule for sub-millisecond temporal coding. Nature 383, 76–78 (1996).

    Article  CAS  Google Scholar 

  26. Gerstner, W., Kempter, R., van Hemmen, J. L. & Wagner, H. in Computational Neuroscience (ed. Bower, J.) 665– 669 (Plenum, New York, 1997).

    Book  Google Scholar 

  27. Blum, K. I. & Abbott, L. F. A model of spatial map formation in the hippocampus of the rat. Neural Comput. 8, 85–93 (1996).

    Article  CAS  Google Scholar 

  28. Gerstner, W. & Abbott, L. F. Learning navigational maps through potentiation and modulation of hippocampal place cells. J. Comput. Neurosci. 4, 79–94 (1997).

    Article  CAS  Google Scholar 

  29. Mehta, M. R., Quirk, M. C. & Wilson, M. Experience dependent asymmetric shape of hippocampal receptive fields. Neuron 25, 707– 715 (2000).

    Article  CAS  Google Scholar 

  30. Rao, R. & Sejnowski, T. J. in Advances in Neural Information Processing Systems 12 (eds. Solla, S. A., Leen, T. K. & Muller K.-b.) 164–171 (MIT Press, Cambridge, Massachusetts, 2000).

    Google Scholar 

  31. Mehta, M. R. & Wilson, M. From hippocampus to V1: Effect of LTP on spatiotemporal dynamics of receptive fields. Neurocomputing 32, 905–911 (2000).

    Article  Google Scholar 

  32. Kempter, R., Gerstner, W. & van Hemmen, J. L. Hebbian learning and spiking neurons. Phys. Rev. E59, 4498–4514 (1999).

    Google Scholar 

  33. Softky, W. R. & Koch, C. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334–350 (1994).

    Article  Google Scholar 

  34. Stevens, C. F. & Zador, A. M. Input synchrony and the irregular firing of cortical neurons. Nat. Neurosci. 1, 210–217 (1998).

    Article  CAS  Google Scholar 

  35. Shadlen, M. N. & Newsome, W. T. Noise, neural codes and cortical organization. Curr. Opin Neurobiol. 4, 569–579 (1994).

    Article  CAS  Google Scholar 

  36. Tsodyks, M. & Sejnowski, T. J. Rapid switching in balanced cortical network models. Network 6, 1– 14 (1995).

    Article  Google Scholar 

  37. Troyer, T. W. & Miller, K. D. Physiological gain leads to high ISI variability in a simple model of a cortical regular spiking cell. Neural Comp. 9, 971–983 (1997).

    Article  CAS  Google Scholar 

  38. Troyer, T. W. & Miller, K. D. in Computational Neuroscience, Trends in Research (ed. Bower, J.) 197–201 (Plenum, New York, 1997).

    Book  Google Scholar 

  39. Bugmann, G., Christodoulou, C. & Taylor, J. G. Role of temporal integration and fluctuation detection in the highly irregular firing of a leaky integrator neuron model with partial reset. Neural Comput. 9, 985– 1000 (1997).

    Article  Google Scholar 

  40. Amit, D. J. & Brunel, N. Global spontaneous activity and local structured (learned) delay activity in cortex. Cereb. Cortex 7, 237–252 (1997).

    Article  CAS  Google Scholar 

  41. van Vreeswijk, C. & Sompolinsky, H. Chaotic balanced state in a model of cortical circuits. Neural Comput. 10, 1321–1327 (1998).

    Article  CAS  Google Scholar 

  42. Abbott, L. F. & Song, S. in Advances in Neural Information Processing Systems 11 (eds. Kearns, M. S., Solla, S. A. & Cohn, D. A.) 69–75 (MIT Press, Cambridge, Massachusetts, 1999).

    Google Scholar 

  43. Bekkers, J. M. & Stevens, C. F. J. Cable properties of cultured hippocampal neurons determined from sucrose-evoked miniature EPSCs . Neurophysiology 75, 1250– 1255 (1996).

    Article  CAS  Google Scholar 

  44. Sejnowski, T. J. Storing covariance with nonlinearly interacting neurons. J. Math. Biol. 4, 303–321 (1977).

    Article  CAS  Google Scholar 

  45. Mehta, M. R., Barnes, C. A. & McNaughton, B. L. Experience-dependent, asymmetric expansion of hippocampal place fields. Proc. Natl. Acad. Sci. USA 94, 8918–8921 (1997).

    Article  CAS  Google Scholar 

  46. Markram, H. & Tsodyks, M. V. Redistribution of synaptic efficacy between neocortical pyramidal neurones. Nature 382, 807–809 (1996).

    Article  CAS  Google Scholar 

  47. Stryker, M. P. in The Biology of Change in Otolaryngology (eds. Ruben, R. J., Van De Water, T. R. & Rubel, E. W.) 211–224 (Elsevier, Amsterdam, 1986).

    Google Scholar 

  48. Scanziani, M., Malenka, R. C. & Nicoll, R. A. Role of intercellular interactions in heterosynaptic long-term depression. Nature 380, 446– 450 (1996).

    Article  CAS  Google Scholar 

  49. Tang, Y.-P. et al. Genetic enhancement of learning and memory in mice. Nature 401, 63–69 (1999).

    Article  CAS  Google Scholar 

  50. Yuste, R., Majewska, A., Cash, S. S. & Denk, W. Mechanisms of calcium influx into hippocampal spines: heterogeneity among spines, coincidence detection by NMDA receptors, and optical quantal analysis. J. Neurosci. 19, 1976–1987 (1999).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Research supported by the Sloan Center for Theoretical Neurobiology at Brandeis University, the National Science Foundation (IBN-9817194), the National Institute of Mental Health (MH58754) and the W.M. Keck Foundation (L.F.A.); a Howard Hughes Predoctoral Fellowship (S.S.); and by R01-EY11001 from the National Eye Institute and an Alfred P. Sloan Research Fellowship (K.D.M.). We thank Todd Troyer for discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. F. Abbott.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Song, S., Miller, K. & Abbott, L. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3, 919–926 (2000). https://doi.org/10.1038/78829

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/78829

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing