Analysis of motoneuron responses to composite synaptic volleys (computer simulation study)

This paper deals with the analysis of changes in motoneuron (MN) firing evoked by repetitively applied stimuli aimed toward extracting information about the underlying synaptic volleys. Spike trains were obtained from computer simulations based on a threshold-crossing model of tonically firing MN, subjected to stimulation producing postsynaptic potentials (PSPs) of various parameters. These trains were analyzed as experimental results, using the output measures that were previously shown to be most effective for this purpose: peristimulus time histogram, raster plot and peristimulus time intervalgram. The analysis started from the effects of single excitatory and inhibitory PSPs (EPSPs and IPSPs). The conclusions drawn from this analysis allowed the explanation of the results of more complex synaptic volleys, i.e., combinations of EPSPs and IPSPs, and the formulation of directions for decoding the results of human neurophysiological experiments in which the responses of tonically firing MNs to nerve stimulation are analyzed

A Threshold Equation for Action Potential Initiation

In central neurons, the threshold for spike initiation can depend on the stimulus and varies between cells and between recording sites in a given cell, but it is unclear what mechanisms underlie this variability. Properties of ionic channels are likely to play a role in threshold modulation. We examined in models the influence of Na channel activation, inactivation, slow voltage-gated channels and synaptic conductances on spike threshold. We propose a threshold equation which quantifies the contribution of all these mechanisms. It provides an instantaneous time-varying value of the threshold, which applies to neurons with fluctuating inputs. We deduce a differential equation for the threshold, similar to the equations of gating variables in the Hodgkin-Huxley formalism, which describes how the spike threshold varies with the membrane potential, depending on channel properties. We find that spike threshold depends logarithmically on Na channel density, and that Na channel inactivation and K channels can dynamically modulate it in an adaptive way: the threshold increases with membrane potential and after every action potential. Our equation was validated with simulations of a previously published multicompartemental model of spike initiation. Finally, we observed that threshold variability in models depends crucially on the shape of the Na activation function near spike initiation (about −55 mV), while its parameters are adjusted near half-activation voltage (about −30 mV), which might explain why many models exhibit little threshold variability, contrary to experimental observations. We conclude that ionic channels can account for large variations in spike threshold