Predicting SA-I mechanoreceptor spike times with a skin-neuron model

Slowly adapting type I (SA-I) mechanoreceptors encode the edges and curvature of touched objects by generating neural spikes in response to indentation of the skin. Beneath this general input-output relationship, models are of great utility for understanding the sub-processes, as SA-I transduction sites are inaccessible to whole-cell recording. This work develops and validates a SA-I skin-receptor model that combines a finite element model of skin mechanics, a sigmoidal function of transduction, and a leaky integrate-and-fire model of neural dynamics. The model produced a R(2)=0.80 goodness of fit between predicted and observed firing rates for 3 and 5mm grating stimuli. In addition, modulation indices of predicted firing rates for 3 and 5mm gratings are 0.46 and 0.59, respectively, compared to the 0.71 and 0.72 found in vivo. An analysis of predicted first spikes indicates their latency may also be enhanced by edges, as edge proximity shortened first spike latencies by 26.2 and 41.8 ms for the 3 and 5mm gratings, respectively. The model described here bridges the gap between those models that transform sustained indentation to firing rates and those that transform vibration to spike times.