New methods for quantifying rapidity of action potential onset differentiate neuron types

Two new methods for quantifying the rapidity of action potential onset have lower relative standard deviations and better distinguish neuron cell types than current methods. Action potentials (APs) in most central mammalian neurons exhibit sharp onset dynamics. The main views explaining such an abrupt onset differ. Some studies suggest sharp onsets reflect cooperative sodium channels activation, while others suggest they reflect AP backpropagation from the axon initial segment. However, AP onset rapidity is defined subjectively in these studies, often using the slope at an arbitrary value on the phase plot. Thus, we proposed more systematic methods using the membrane potential's second-time derivative (<(V)double over dot>V-m) peak width. Here, the AP rapidity was measured for four different cortical and hippocampal neuron types using four quantification methods: the inverse of full-width at the half maximum of the <(V)double over dot>V-m peak (IFWd(2)), the inverse of half-width at the half maximum of the <(V)double over dot>V-m peak (IHWd(2)), the phase plot slope, and the error ratio method. The IFWd(2) and IHWd(2) methods show the smallest variation among neurons of the same type. Furthermore, the AP rapidity, using the <(V)double over dot>V-m peak width methods, significantly differentiates between different types of neurons, indicating that AP rapidity can be used to classify neuron types. The AP rapidity measured using the IFWd(2) method was able to differentiate between all four neuron types analyzed. Therefore, the <(V)double over dot>V-m peak width methods provide another sensitive tool to investigate the mechanisms impacting the AP onset dynamics.

Automated summary

The study introduces two new methods for quantifying the rapidity of action potential onset, which have lower variation and better distinguish between different neuron types compared to current methods. By measuring the rapidity of action potentials, it is possible to effectively classify neuron types.