Analysis of the interaction between the dendritic conductance density and activated area in modulating alpha-motoneuron EPSP: statistical computer model

Neural Comput. 2008 Jun;20(6):1385-410. doi: 10.1162/neco.2008.03-07-490.


Five reconstructed alpha-motoneurons (MNs) are simulated under physiological and morphological realistic parameters. We compare the resulting excitatory postsynaptic potential (EPSP) of models, containing voltage-dependent channels on the dendrites, with the EPSP of a passive MN and an active soma and axon model. In our simulations, we apply three different distribution functions of the voltage-dependent channels on the dendrites: a step function (ST) with uniform spatial dispersion; an exponential decay (ED) function, with proximal to the soma high-density location; and an exponential rise (ER) with distally located conductance density. In all cases, the synaptic inputs are located as a gaussian function on the dendrites. Our simulations lead to eight key observations. (1) The presence of the voltage-dependent channels conductance (g(Active)) in the dendrites is vital for obtaining EPSP peak boosting. (2) The mean EPSP peaks of the ST, ER, and ED distributions are similar when the ranges of G (total conductance) are equal. (3) EPSP peak increases monotonically when the magnitude of g(Na_step) (maximal g(Na) at a particular run) is increased. (4) EPSP kinetics parameters were differentially affected; time integral was decreased monotonically with increased g(Na_step), but the rate of rise (the decay time was not analyzed) does not show clear relations. (5) The total G can be elevated by increasing the number of active dendrites; however, only a small active area of the dendritic tree is sufficient to get the maximal boosting. (6) The sometimes large variations in the parameters values for identical G depend on the g(Na_step) and active dendritic area. (7) High g(Na_step) in a few dendrites is more efficient in amplifying the EPSP peak than low g(Na_step) in many dendrites. (8) The EPSP peak is approximately linear with respect to the MNs' R(N) (input resistance).

MeSH terms

  • Animals
  • Computer Simulation*
  • Dendrites / physiology*
  • Electric Conductivity
  • Excitatory Postsynaptic Potentials / physiology*
  • Models, Neurological*
  • Models, Statistical
  • Motor Neurons / cytology*
  • Motor Neurons / physiology*