The tempotron: a neuron that learns spike timing–based decisions | Nature Neuroscience
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.

  • Article
  • Published:

The tempotron: a neuron that learns spike timing–based decisions

Nature Neuroscience volume 9, pages 420–428 (2006)Cite this article

This article has been updated

Abstract

The timing of action potentials in sensory neurons contains substantial information about the eliciting stimuli. Although the computational advantages of spike timing–based neuronal codes have long been recognized, it is unclear whether, and if so how, neurons can learn to read out such representations. We propose a new, biologically plausible supervised synaptic learning rule that enables neurons to efficiently learn a broad range of decision rules, even when information is embedded in the spatiotemporal structure of spike patterns rather than in mean firing rates. The number of categorizations of random spatiotemporal patterns that a neuron can implement is several times larger than the number of its synapses. The underlying nonlinear temporal computation allows neurons to access information beyond single-neuron statistics and to discriminate between inputs on the basis of multineuronal spike statistics. Our work demonstrates the high capacity of neural systems to learn to decode information embedded in distributed patterns of spike synchrony.

Note: The PDF version of this article was corrected on the 14th of February, and the HTML version on the 16th of February. Please see the PDF for details.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Tempotron classification.
Figure 2: Tempotron learning rule.
Figure 3: Input patterns (see Methods for details).
Figure 4: Tempotron performance.
Figure 5: Tempotron decision times.
Figure 6: Tempotron robustness.
Figure 7: Learning multineural spike statistics.
Figure 8: Spike timing dependence of different learning rules.

Similar content being viewed by others

Change history

  • 14 February 2006

    The PDF version of this article was corrected on the 14th of February, and the HTML version on the 16th of February. Please see the PDF for details.

References

  1. Meister, M., Lagnado, L. & Baylor, D.A. Concerted signaling by retinal ganglion cells. Science 270, 1207–1210 (1995).

    Article  CAS  Google Scholar 

  2. deCharms, R.C. & Merzenich, M.M. Primary cortical representation of sounds by the coordination of action-potential timing. Nature 381, 610–613 (1996).

    Article  CAS  Google Scholar 

  3. Neuenschwander, S. & Singer, W. Long-range synchronization of oscillatory light responses in the cat retina and lateral geniculate nucleus. Nature 379, 728–732 (1996).

    Article  CAS  Google Scholar 

  4. Wehr, M. & Laurent, G. Odour encoding by temporal sequences of firing in oscillating neural assemblies. Nature 384, 162–166 (1996).

    Article  CAS  Google Scholar 

  5. Johansson, R.S. & Birznieks, I. First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nat. Neurosci. 7, 170–177 (2004).

    Article  CAS  Google Scholar 

  6. Hopfield, J.J. Pattern recognition computation using action potential timing for stimulus representation. Nature 376, 33–36 (1995).

    Article  CAS  Google Scholar 

  7. Thorpe, S., Delorme, A. & Van Rullen, R. Spike-based strategies for rapid processing. Neural Netw. 14, 715–725 (2001).

    Article  CAS  Google Scholar 

  8. Castelo-Branco, M., Goebel, R., Neuenschwander, S. & Singer, W. Neural synchrony correlates with surface segregation rules. Nature 405, 685–689 (2000).

    Article  CAS  Google Scholar 

  9. Nirenberg, S., Carcieri, S., Jacobs, A. & Latham, P. Retinal ganglion cells act largely as independent encoders. Nature 411, 698–701 (2001).

    Article  CAS  Google Scholar 

  10. Perkel, D.H. & Bullock, T.H. Neural coding. Neurosci. Res. Program Bull. 6, 221–348 (1968).

    Google Scholar 

  11. Singer, W. Time as coding space? Curr. Opin. Neurobiol. 9, 189–194 (1999).

    Article  CAS  Google Scholar 

  12. Meister, M. & Berry, M. II. The neural code of the retina. Neuron 22, 435–450 (1999).

    Article  CAS  Google Scholar 

  13. Shadlen, M. & Movshon, J. Synchrony unbound: a critical evaluation of the temporal binding hypothesis. Neuron 24, 67–77 (1999).

    Article  CAS  Google Scholar 

  14. Minsky, M.L. & Papert, S.A. Perceptrons (MIT Press, Cambridge, Massachusetts, 1969).

    Google Scholar 

  15. Hertz, J., Krogh, A. & Palmer, R.G. Introduction to the Theory of Neural Computation (Westview Press, Boulder, Colorado, 1991).

    Google Scholar 

  16. Gardner, E. The space of interactions in neural network models. J. Phys. A 21, 257–270 (1988).

    Article  Google Scholar 

  17. Brunel, N., Hakim, V., Isope, P., Nadal, J.P. & Barbour, B. Optimal information storage and the distribution of synaptic weights: perceptron versus purkinje cell. Neuron 43, 745–757 (2004).

    CAS  PubMed  Google Scholar 

  18. Vaadia, E. et al. Dynamics of neuronal interactions in monkey cortex in relation to behavioural events. Nature 373, 515–518 (1995).

    Article  CAS  Google Scholar 

  19. MacLeod, K., Backer, A. & Laurent, G. Who reads temporal information contained across synchronized and oscillatory spike trains? Nature 395, 693–698 (1998).

    Article  CAS  Google Scholar 

  20. Brody, C. & Hopfield, J.J. Simple networks for spike-timing-based computation, with application to olfactory processing. Neuron 37, 843–852 (2003).

    Article  CAS  Google Scholar 

  21. Schnitzer, M.J. & Meister, M. Multineuronal firing patterns in the signal from eye to brain. Neuron 37, 499–511 (2003).

    Article  CAS  Google Scholar 

  22. Kuhn, A., Aertsen, A. & Rotter, S. Higher-order statistics of input ensembles and the response of simple model neurons. Neural Comput. 15, 67–101 (2003).

    Article  Google Scholar 

  23. Bressloff, P.B. & Taylor, J.G. Perceptron-like learning in time-summating neural networks. J. Phys. A 25, 4373–4388 (1992).

    Article  Google Scholar 

  24. Seung, H.S. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40, 1063–1073 (2003).

    Article  CAS  Google Scholar 

  25. Artola, A., Brocher, S. & Singer, W. Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex. Nature 347, 69–72 (1990).

    Article  CAS  Google Scholar 

  26. Ngezahayo, A., Schachner, M. & Artola, A. Synaptic activity modulates the induction of bidirectional synaptic changes in adult mouse hippocampus. J. Neurosci. 20, 2451–2458 (2000).

    Article  CAS  Google Scholar 

  27. Sjostrom, P., Turrigiano, G. & Nelson, S. Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32, 1149–1164 (2001).

    Article  CAS  Google Scholar 

  28. Lisman, J. & Spruston, N. Postsynaptic depolarization requirements for LTP and LTD: a critique of spike timing-dependent plasticity. Nat. Neurosci. 8, 839–841 (2005).

    Article  CAS  Google Scholar 

  29. Cummings, J.A., Mulkey, R.M., Nicoll, R.A. & Malenka, R.C. Ca2+ signaling requirements for long-term depression in the hippocampus. Neuron 16, 825–833 (1996).

    Article  CAS  Google Scholar 

  30. Malenka, R.C. & Nicoll, R.A. Long-term potentiation–a decade of progress? Science 285, 1870–1874 (1999).

    Article  CAS  Google Scholar 

  31. Kempter, R., Gerstner, W. & van Hemmen, J.L. Hebbian learning and spiking neurons. Phys. Rev. E 59, 4498–4515 (1999).

    Article  CAS  Google Scholar 

  32. Song, S., Miller, K.D. & Abbott, L.F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2000).

    Article  CAS  Google Scholar 

  33. Gütig, R., Aharonov, R., Rotter, S. & Sompolinsky, H. Learning input correlations through non-linear temporally asymmetric Hebbian plasticity. J. Neurosci. 23, 3697–3714 (2003).

    Article  Google Scholar 

  34. Guyonneau, R., VanRullen, R. & Thorpe, S.J. Neurons tune to the earliest spikes through STDP. Neural Comput. 17, 859–879 (2005).

    Article  Google Scholar 

  35. Legenstein, R., Naeger, C. & Maass, W. What can a neuron learn with spike-timing-dependent plasticity? Neural Comput. 17, 2337–2382 (2005).

    Article  Google Scholar 

  36. Worgotter, F. & Porr, B. Temporal sequence learning, prediction, and control: a review of different models and their relation to biological mechanisms. Neural Comput. 17, 245–319 (2005).

    Article  Google Scholar 

  37. Foehring, R.C. & Lorenzon, N.M. Neuromodulation, development and synaptic plasticity. Can. J. Exp. Psychol. 53, 45–61 (1999).

    Article  CAS  Google Scholar 

  38. Seamans, J. & Yang, C. The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Prog. Neurobiol. 74, 1–58 (2004).

    Article  CAS  Google Scholar 

  39. Wespatat, V., Tennigkeit, F. & Singer, W. Phase sensitivity of synaptic modifications in oscillating cells of rat visual cortex. J. Neurosci. 24, 9067–9075 (2004).

    Article  CAS  Google Scholar 

  40. Lisman, J. A mechanism for the Hebb and the anti-Hebb processes underlying learning and memory. Proc. Natl. Acad. Sci. USA 86, 9574–9578 (1989).

    Article  CAS  Google Scholar 

  41. Mansuy, I. Calcineurin in memory and bidirectional plasticity. Biochem. Biophys. Res. Commun. 311, 1195–1208 (2003).

    Article  CAS  Google Scholar 

  42. Liu, L. et al. Role of NMDA receptor subtypes in governing the direction of hippocampal synaptic plasticity. Science 304, 1021–1024 (2004).

    Article  CAS  Google Scholar 

  43. Wang, H.X., Gerkin, R.C., Nauen, D.W. & Bi, G.Q. Coactivation and timing-dependent integration of synaptic potentiation and depression. Nat. Neurosci. 8, 187–193 (2005).

    Article  CAS  Google Scholar 

  44. Malinow, R. & Malenka, R. AMPA receptor trafficking and synaptic plasticity. Annu. Rev. Neurosci. 25, 103–126 (2002).

    Article  CAS  Google Scholar 

  45. Zhu, J., Qin, Y., Zhao, M., Van Aelst, L. & Malinow, R. Ras and rap control AMPA receptor trafficking during synaptic plasticity. Cell 110, 443–455 (2002).

    Article  CAS  Google Scholar 

  46. Tomita, S., Stein, V., Stocker, T., Nicoll, R. & Bredt, D. Bidirectional synaptic plasticity regulated by phosphorylation of stargazin-like tarps. Neuron 45, 269–277 (2005).

    Article  CAS  Google Scholar 

  47. Lisman, J., Schulman, H. & Cline, H. The molecular basis of CaMKII function in synaptic and behavioural memory. Nat. Rev. Neurosci. 3, 175–190 (2002).

    Article  CAS  Google Scholar 

  48. Centonze, D., Gubellini, P., Pisani, A., Bernardi, G. & Calabresi, P. Dopamine, acetylcholine and nitric oxide systems interact to induce corticostriatal synaptic plasticity. Rev. Neurosci. 14, 207–216 (2003).

    Article  CAS  Google Scholar 

  49. Mauk, M. & Buonomano, D. The neural basis of temporal processing. Annu. Rev. Neurosci. 27, 307–340 (2004).

    Article  CAS  Google Scholar 

  50. Buonomano, D. Decoding temporal information: a model based on short-term synaptic plasticity. J. Neurosci. 20, 1129–1141 (2000).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank L. de Hoz, A. Globerson, M. Gutnick, D. Hansel, O. White and Y. Yarom for comments. We acknowledge computational resources provided by the Harvard University Bauer Center for Genomic Research. This work was supported in part by the German Research Foundation, the Minerva Foundation, the European Commission's Improving Human Potential Program, the Israel Science Foundation (Center of Excellence no. 8006/00) and the Defense Research & Development Directorate (MAFAT).

Author information

Authors and Affiliations

  1. Racah Institute of Physics, Hebrew University, Jerusalem, 91904, Israel

    Robert Gütig & Haim Sompolinsky

  2. Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, 91904, Israel

    Robert Gütig & Haim Sompolinsky

  3. Institute for Theoretical Biology, Humboldt University, Berlin, 10115, Germany

    Robert Gütig

  4. Neuroscience Research Center, Charitè Medical Faculty of Berlin, Berlin, 10117, Germany

    Robert Gütig

  5. Center for Brain Science, Harvard University, Cambridge, 02138, Massachusetts, USA

    Haim Sompolinsky

Authors
  1. Robert Gütig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haim Sompolinsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Robert Gütig or Haim Sompolinsky.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gütig, R., Sompolinsky, H. The tempotron: a neuron that learns spike timing–based decisions. Nat Neurosci 9, 420–428 (2006). https://doi.org/10.1038/nn1643

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn1643

This article is cited by

Search

Advanced 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