Spatio-temporal filtering properties of a dendritic cable with active spines: a modeling study in the spike-diffuse-spike framework

J Comput Neurosci. 2006 Dec;21(3):293-306. doi: 10.1007/s10827-006-8776-4. Epub 2006 Jul 28.

Abstract

The spike-diffuse-spike (SDS) model describes a passive dendritic tree with active dendritic spines. Spine-head dynamics is modeled with a simple integrate-and-fire process, whilst communication between spines is mediated by the cable equation. In this paper we develop a computational framework that allows the study of multiple spiking events in a network of such spines embedded on a simple one-dimensional cable. In the first instance this system is shown to support saltatory waves with the same qualitative features as those observed in a model with Hodgkin-Huxley kinetics in the spine-head. Moreover, there is excellent agreement with the analytically calculated speed for a solitary saltatory pulse. Upon driving the system with time-varying external input we find that the distribution of spines can play a crucial role in determining spatio-temporal filtering properties. In particular, the SDS model in response to periodic pulse train shows a positive correlation between spine density and low-pass temporal filtering that is consistent with the experimental results of Rose and Fortune [1999, 'Mechanisms for generating temporal filters in the electrosensory system,' The Journal of Experimental Biology 202: 1281-1289]. Further, we demonstrate the robustness of observed wave properties to natural sources of noise that arise both in the cable and the spine-head, and highlight the possibility of purely noise induced waves and coherent oscillations.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials / physiology*
  • Animals
  • Dendrites / physiology*
  • Models, Biological*
  • Neural Conduction / physiology*
  • Neurons / cytology*
  • Neurons / physiology
  • Reproducibility of Results
  • Synaptic Transmission / physiology
  • Time Factors