Biological Cybernetics

, 99:417 | Cite as

The quantitative single-neuron modeling competition

  • Renaud Jolivet
  • Felix Schürmann
  • Thomas K. Berger
  • Richard Naud
  • Wulfram Gerstner
  • Arnd Roth
Original Paper


As large-scale, detailed network modeling projects are flourishing in the field of computational neuroscience, it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively accurate in silico representations of those. Recent years have seen substantial effort being put in the development of algorithms for the systematic evaluation and optimization of neuron models with respect to electrophysiological data. It is however difficult to compare these methods because of the lack of appropriate benchmark tests. Here, we describe one such effort of providing the community with a standardized set of tests to quantify the performances of single neuron models. Our effort takes the form of a yearly challenge similar to the ones which have been present in the machine learning community for some time. This paper gives an account of the first two challenges which took place in 2007 and 2008 and discusses future directions. The results of the competition suggest that best performance on data obtained from single or double electrode current or conductance injection is achieved by models that combine features of standard leaky integrate-and-fire models with a second variable reflecting adaptation, refractoriness, or a dynamic threshold.


Integrate-and-fire model Quantitative predictions Benchmark testing Scientific competition 


  1. Achard P, De Schutter E (2006) Complex parameter landscape for a complex neuron model. PLoS Comp Biol 2(7): e94CrossRefGoogle Scholar
  2. Arcas B, Fairhall A (2003) What causes a neuron to spike. Neural Comp 15: 1789–1807CrossRefGoogle Scholar
  3. Aronov D, Victor JD (2004) Non-euclidean properties of spike train metric spaces. Phys Rev E 69: 061,905CrossRefGoogle Scholar
  4. Badel L, Lefort S, Brette R, Petersen CCH, Gerstner W, Richardson MJE (2008) Dynamic IV curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. J Neurophysiol 99(2): 656–666CrossRefPubMedGoogle Scholar
  5. Bower JM, Beeman D (1995) The book of Genesis. Springer, New YorkGoogle Scholar
  6. Brette R, Gerstner W (2005) Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal ActivityGoogle Scholar
  7. Brillinger DR (1988a) Maximum likelihood analysis of spike trains of interacting nerve cells. Biol Cybern 59: 189–200CrossRefPubMedGoogle Scholar
  8. Brillinger DR (1988b) The maximum likelihood approach to the identification of neuronal firing systems. Ann Biomed Eng 16: 3–16CrossRefPubMedGoogle Scholar
  9. Brillinger DR, Segundo JP (1979) Empirical examination of the threshold model of neuronal firing. Biol Cybern 35: 213–220CrossRefPubMedGoogle Scholar
  10. Brunel N, Hakim V, Richardson MJE (2003) Firing-rate resonance in a generalized integrate-and-fire neuron with subthreshold resonance. Phys Rev E 67: 051,916CrossRefGoogle Scholar
  11. Bush K, Knight J, Anderson C (2005) Optimizing conductance parameters of cortical neural models via electrotonic partitions. Neural Net 18(5–): 488–496CrossRefGoogle Scholar
  12. Carandini M, Horton JC, Sincich LC (2007) Thalamic filtering of retinal spike trains by postsynaptic summation. J Vision 7(14): 20CrossRefGoogle Scholar
  13. Clopath C, Jolivet R, Rauch A, Lüscher HR, Gerstner W (2007) Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two compartments. Neurocomputing 70(10–2): 1668–1673CrossRefGoogle Scholar
  14. Druckmann S, Banitt Y, Gidon A, Schürmann F, Markram H, Segev I (2007) A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci 1: 7–18CrossRefPubMedGoogle Scholar
  15. Fourcaud-Trocmé N, Hansel D, van Vreeswijk C, Brunel N (2003) How spike generation mechanisms determine the neuronal response to fluctuating inputs. J Neurosci 23: 11,628–11,640Google Scholar
  16. Geisler WS, Albrecht DG, Salvi RJ, Saunders SS (1991) Discrimination performance of single neurons: rate and temporal information. J Neurophysiol 66: 334–362PubMedGoogle Scholar
  17. Gerken WC, Purvis LK, Butera RJ (2006) Genetic algorithm for optimization and specification of a neuron model. Neurocomputing 69(10–2): 1039–1042CrossRefGoogle Scholar
  18. Gerstner W, Kistler WM (2002) Spiking neuron models. Cambridge University Press, CambridgeGoogle Scholar
  19. Gerstner W, Kempter R, van Hemmen JL, Wagner H (1996) A neuronal learning rule for sub-millisecond temporal coding. Nature 383: 76–78CrossRefPubMedGoogle Scholar
  20. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol (London) 117: 500–544Google Scholar
  21. Hopfield JJ (1995) Pattern recognition computation using action potential timing for stimulus representation. Nature 376: 33–36CrossRefPubMedGoogle Scholar
  22. Huys QJM, Ahrens MB, Paninski L (2006) Efficient estimation of detailed single-neuron models. J Neurophysiol 96: 872–890CrossRefPubMedGoogle Scholar
  23. Izhikevich EM (2004) Which model to use for cortical spiking neurons. IEEE Trans Neural Net 15: 1063–1070CrossRefGoogle Scholar
  24. Izhikevich EM, Edelman GM (2008) Large-scale model of mammalian thalamocortical systems. PNAS 105(9): 3593–3598CrossRefPubMedGoogle Scholar
  25. Jolivet R, Gerstner W (2004) Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival. J Physiol-Paris 98(4–): 442–451CrossRefPubMedGoogle Scholar
  26. Jolivet R, Lewis TJ, Gerstner W (2004) Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. J Neurophysiol 92(2): 959–976CrossRefPubMedGoogle Scholar
  27. Jolivet R, Rauch A, Lüscher HR, Gerstner W (2006a) Integrate-and-Fire models with adaptation are good enough: predicting spike times under random current injection. Adv Neural Inform Process Syst 18: 595–602Google Scholar
  28. Jolivet R, Rauch A, Lüscher HR, Gerstner W (2006b) Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J Comput Neurosci 21(1): 35–49CrossRefPubMedGoogle Scholar
  29. Jolivet R, Kobayashi R, Rauch A, Naud R, Shinomoto S, Gerstner W (2008) A benchmark test for a quantitative assessment of simple neuron models. J Neurosci Meth 169(2): 417–424CrossRefGoogle Scholar
  30. Keat J, Reinagel P, Reid RC, Meister M (2001) Predicting every spike: a model for the responses of visual neurons. Neuron 30: 803–817CrossRefPubMedGoogle Scholar
  31. Kempter R, Gerstner W, van Hemmen JL, Wagner H (1998) Extracting oscillations: neuronal coincidence detection with noisy periodic spike input. Neural Comp 10: 1987–2017CrossRefGoogle Scholar
  32. Keren N, Peled N, Korngreen A (2005) Constraining compartmental models using multiple voltage recordings and genetic algorithms. J Neurophysiol 94(6): 3730–3742CrossRefPubMedGoogle Scholar
  33. Kistler WM, Gerstner W, van Hemmen JL (1997) Reduction of Hodgkin-Huxley equations to a single-variable threshold model. Neural Comp 9: 1015–1045CrossRefGoogle Scholar
  34. Kobayashi R, Shinomoto S (2007) State space method for predicting the spike times of a neuron. Phys Rev E 75(1): 11,925CrossRefGoogle Scholar
  35. La Camera G, Rauch A, Lüscher HR, Senn W, Fusi S (2004) Minimal models of adapted neuronal response to in vivo-like input currents. Neural Comp 16: 2101–2124CrossRefGoogle Scholar
  36. Lansky P, Sanda P, He J (2006) The parameters of the stochastic leaky integrate-and-fire neuronal model. J Comput Neurosci 21(2): 211–223CrossRefPubMedGoogle Scholar
  37. Larkum ME, Zhu JJ, Sakmann B (1999) A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398(6725): 338–341CrossRefPubMedGoogle Scholar
  38. Larkum ME, Senn W, Lüscher HR (2004) Top-down dendritic input increases the gain of layer 5 pyramidal neurons. Cereb Cortex 14: 1059–1070CrossRefPubMedGoogle Scholar
  39. Leibold C, Gundlfinger A, Schmidt R, Thurley K, Schmitz D, Kempter R (2008) Temporal compression mediated by short-term synaptic plasticity. PNAS 105(11): 4417CrossRefPubMedGoogle Scholar
  40. MacLeod K, Backer A, Laurent G (1998) Who reads temporal information contained across synchronized and oscillatory spike trains. Nature 395: 693–698CrossRefPubMedGoogle Scholar
  41. Markram H (2006) The blue brain project. Nat Rev Neurosci 7(2): 153–160CrossRefPubMedGoogle Scholar
  42. Marmarelis VZ, Berger TW (2005) General methodology for nonlinear modeling of neural systems with Poisson point-process inputs. Math Biosci 196(1): 1–13CrossRefPubMedGoogle Scholar
  43. Mullowney P, Iyengar S (2008) Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data. J Comput Neurosci 24: 179–194CrossRefPubMedGoogle Scholar
  44. Paninski L, Pillow JW, Simoncelli EP (2005) Comparing integrate-and-fire models estimated using intracellular and extracellular data. Neurocomputing 65(66): 379–385CrossRefGoogle Scholar
  45. Pillow JW, Paninski L, Uzzell VJ, Simoncelli EP, Chichilnisky EJ (2005) Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J Neurosci 25(47): 11,003–11,013CrossRefGoogle Scholar
  46. Prinz AA, Billimoria CP, Marder E (2003) Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol 90(6): 3998–4015CrossRefPubMedGoogle Scholar
  47. Prinz AA, Bucher D, Marder E (2004) Similar network activity from disparate circuit parameters. Nat Neurosci 7(12): 1345–1352CrossRefPubMedGoogle Scholar
  48. Rauch A, La Camera G, Lüscher HR, Senn W, Fusi S (2003) Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. J Neurophysiol 90: 1598–1612CrossRefPubMedGoogle Scholar
  49. van Rossum MCW (2001) A novel spike distance. Neural Comp 13: 751–763CrossRefGoogle Scholar
  50. Schaefer AT, Larkum ME, Sakmann B, Roth A (2003) Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. J Neurophysiol 89(6): 3143–3154CrossRefPubMedGoogle Scholar
  51. Song D, Chan RH, Marmarelis VZ, Hampson RE, Deadwyler SA, Berger TW (2007) Nonlinear dynamic modeling of spike train transformations for hippocampal-cortical prostheses. IEEE Trans Biomed Eng 54: 1053–1066CrossRefPubMedGoogle Scholar
  52. Vanier MC, Bower JM (1999) A comparative survey of automated parameter-search methods for compartmental neural models. J Comput Neurosci 7(2): 149–171CrossRefPubMedGoogle Scholar
  53. Victor JD, Purpura KP (1996) Nature and precision of temporal coding in visual cortex: a metric-space analysis. J Neurophysiol 76: 1310–1326PubMedGoogle Scholar
  54. Victor JD, Purpura KP (1997) Metric-space analysis of spike trains: theory, algorithms and application. Netw Comp Neural Syst 8: 127–164CrossRefGoogle Scholar
  55. Wang Y, Gupta A, Toledo-Rodriguez M, Wu CZ, Markram H (2002) Anatomical, physiological, molecular and circuit properties of nest basket cells in the developing somatosensory cortex. Cereb Cortex 12: 395–410CrossRefPubMedGoogle Scholar
  56. Weaver CM, Wearne SL (2006) The role of action potential shape and parameter constraints in optimization of compartment models. Neurocomputing 69(10–2): 1053–1057CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Renaud Jolivet
    • 1
  • Felix Schürmann
    • 2
  • Thomas K. Berger
    • 2
  • Richard Naud
    • 2
  • Wulfram Gerstner
    • 2
  • Arnd Roth
    • 3
  1. 1.Institute of Pharmacology and ToxicologyUniversity of ZurichZurichSwitzerland
  2. 2.Brain Mind InstituteEPFLLausanneSwitzerland
  3. 3.Wolfson Institute for Biomedical ResearchUniversity College LondonLondonUK

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