Biophysical models of slowly and rapidly adapting mechanosensitive tactile afferents in human tongue.

Upon contact, the spike firing patterns of touch afferents encode object attributes such as force, vibration, and spatial geometry. Computational models in cutaneous skin have sought to emulate firing patterns of slowly and rapidly adapting afferents. Herein, for the tongue, we develop biophysical versions of such models, and which rely upon functions and parameters with physiological relevance, as opposed to stimulus features, and are extendable to a broad range of object interactions. The models are evaluated with mechanical inputs relevant to the oral processing of food, in particular, across stress ranges spanning material compliances and periodic vibrations emulating surface sliding. The results indicate the models recapitulate spike firing patterns of human afferents innervating the tongue. Moreover, predicted patterns of spike firing, e.g., the mean and peak firing frequency, first spike latency, and number of spikes, compare favorably with neural recordings across force magnitudes, as do the number of spikes per cycle across a range of periodic amplitudes and frequencies. For extension into a population of afferents in oral mucosa, these single-unit models are a starting point for the further efforts to capture the encoding of higher-level perceptible attributes, e.g., compliance, geometry, surface roughness, and movement velocity.