A Gradient Rule for the Plasticity of a Neuron’s Intrinsic Excitability

  • Jochen Triesch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)


While synaptic learning mechanisms have always been a core topic of neural computation research, there has been relatively little work on intrinsic learning processes, which change a neuron’s excitability. Here, we study a single, continuous activation model neuron and derive a gradient rule for the intrinsic plasticity based on information theory that allows the neuron to bring its firing rate distribution into an approximately exponential regime, as observed in visual cortical neurons. In simulations, we show that the rule works efficiently.


Rate Distribution Input Distribution Intrinsic Excitability Intrinsic Plasticity Visual Cortical Neuron 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Desai, N.S., Rutherford, L.C., Turrigiano, G.G.: Plasticity in the intrinsic excitability of cortical pyramidal neurons. Nature Neuroscience 2, 515–520 (1999)CrossRefGoogle Scholar
  2. 2.
    Zhang, W., Linden, D.J.: The other side of the engram: Experience-driven changes in neuronal intrinsic excitability. Nature Reviews Neuroscience 4, 885–900 (2003)CrossRefGoogle Scholar
  3. 3.
    Daoudal, G., Debanne, D.: Long-term plasticity of intrinsic excitability: Learning rules and mechanisms. Learning and Memory 10, 456–465 (2003)CrossRefGoogle Scholar
  4. 4.
    Zhang, M., Hung, F., Zhu, Y., Xie, Z., Wang, J.H.: Calcium signal-dependent plasticity of neuronal excitability developed postnatally. J. Neurobiol. 61, 277–287 (2004)CrossRefGoogle Scholar
  5. 5.
    Cudmore, R., Turrigiano, G.: Long-term potentiation of intrinisic excitability in lv visual cortical neurons. J. Neurophysiol. 92, 341–348 (2004)CrossRefGoogle Scholar
  6. 6.
    Marder, E., Abbott, L.F., Turrigiano, G.G., Liu, Z., Golowasch, J.: Memory from the dynamics of intrinsic membrane currents. Proc. Natl. Acad. Sci. 93, 13481–13486 (1996)CrossRefGoogle Scholar
  7. 7.
    Baddeley, R., Abbott, L.F., Booth, M., Sengpiel, F., Freeman, T.: Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proc. R. Soc. London, Ser. B 264, 1775–1783 (1998)Google Scholar
  8. 8.
    Stemmler, M., Koch, C.: How voltage-dependent conductances can adapt to maximize the information encoded by neuronal firing rate. Nature Neuroscience 2, 521–527 (1999)CrossRefGoogle Scholar
  9. 9.
    Triesch, J.: Synergies between intrinsic and synaptic plasticity in individual model neurons. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 17. MIT Press, Cambridge (2005)Google Scholar
  10. 10.
    Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jochen Triesch
    • 1
    • 2
  1. 1.Department of Cognitive ScienceUC San DiegoLa JollaUSA
  2. 2.Frankfurt Institute for Advanced StudiesJohann Wolfgang Goethe UniversityFrankfurt am MainGermany

Personalised recommendations