Statistical Computer Model Analysis of the Reciprocal and Recurrent Inhibitory Postsynaptic Potentials in α-Motoneurons

Abstract

We simulate reconstructed α-motoneurons (MNs) under physiological and morphological realistic parameters and compare the modeled reciprocal (REC) and recurrent (REN) inhibitory postsynaptic potentials (IPSPs) containing voltage-dependent channels on the dendrites with the IPSPs of a passive MN model. Three distribution functions of the voltage-dependent channels on the dendrites are applied: a step function (ST) with uniform spatial dispersion; an exponential decay (ED) function, with channels with high density located proximal to the soma; and an exponential rise (ER) with a higher density of channels located distally. The excitatory and REN inhibitory inputs are located as a gaussian function on the dendrites, while the REC inhibitory synapses are located proximal to the soma. Our simulations generate four key results. (1) The distribution pattern of the voltage-dependent channels does not affect the IPSP peak, its time integral (TI), or its rate of rise (RR). However, the IPSP peak decreased in the presence of the active dendrites, while the EPSP peak increased. (2) Proximally located IPSP conductance produces greater IPSP peak, RR, and TI. (3) Increased duration of the IPSP produces greater RR and moderately increased TI and has a small effect on the peak amplitude. (4) The IPSP of both REC and REN models is specific to each MN: its amplitude is proportional to the MNs' input resistance, RN; the increase of IPSP at the proximal location of the IPSP synapses is inversely related to RN; and the effect of the IPSP conductance duration is insensitive to RN.