Extracting non-linear integrate-and-fire models from experimental data using dynamic I-V curves

Biol Cybern. 2008 Nov;99(4-5):361-70. doi: 10.1007/s00422-008-0259-4. Epub 2008 Nov 15.

Abstract

The dynamic I-V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current-voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models-of the refractory exponential integrate-and-fire type-provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons.

MeSH terms

  • Action Potentials / physiology
  • Animals
  • Brain / physiology
  • Models, Neurological*
  • Neurons / physiology*
  • Rats