Skip to main content
  • Poster presentation
  • Open access
  • Published:

How do channel densities and various time constants affect the dynamic gain of a detailed model of a pyramidal neuron?

The axon initial segment (AIS) controls the transformation of dendrosomatic synaptic input into spike output and the backpropagation of action potentials into the dendrites due to its lower spike initiation threshold. Channel density and kinetics can both contribute to this low threshold. However, the nature of such threshold differences is unknown and topic of current debates [13].

Dynamical response properties give a constraint on the AIS function. Here we study the dynamical response properties of a detailed multi compartment NEURON [4] model that well reproduces the sodium concentration changes in the AIS and soma generated by action potential firing in a layer 5 pyramidal cell [2].

To study these properties, we inject different current stimuli into the soma. These are constant currents and Gaussian noise currents as studied in [5]. We vary the sodium and potassium channel densities at the axon initial segment as well as the sodium activation time constant taum. Furthermore, we study the influence of input current parameters as mean, variance and correlation time. We then calculate the dynamic rate response of a population of independent neurons. This is described at linear order by a filter function with frequency dependent gain as done by [5].

The f-I curves show that the neuron model under investigation is of type I. This holds true for all channel densities tested. The cut-off frequency appears insensitive to AIS channel density.


  1. Maarten Kole, Susanne Ilschner, Björn Kampa, Stephen Williams, Peter Ruben, Greg Stuart: Action potential generation requires a high sodium channel density in the axon initial segment. Nature Neuroscience. 2008, 11 (2): 178-86. 10.1038/nn2040.

    Article  Google Scholar 

  2. Ilya Fleidervish, Nechama Lasser-Ross, Michael Gutnick, William Ross: Na+ imaging reveals little difference in action potential-evoked Na+ influx between axon and soma. Nature Neuroscience. 2010, 13 (7): 852-860. 10.1038/nn.2574.

    Article  Google Scholar 

  3. Chris Dulla, John Huguenard: Who let the spikes out?. Nature Neuroscience. 2009, 12 (8): 959-60. 10.1038/nn0809-959.

    Article  Google Scholar 

  4. Carnevale NT, Hines ML: The NEURON book. 2006, Cambridge University Press

    Book  Google Scholar 

  5. Matthew Higgs, William Spain: Conditional bursting enhances resonant firing in neocortical layer 2-3 pyramidal neurons. The Journal of Neuroscience. 2009, 29 (5): 1285-99. 10.1523/JNEUROSCI.3728-08.2009.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to David Hofmann.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Hofmann, D., Neef, A., Fleidervish, I. et al. How do channel densities and various time constants affect the dynamic gain of a detailed model of a pyramidal neuron?. BMC Neurosci 14 (Suppl 1), P419 (2013).

Download citation

  • Published:

  • DOI: