The mammalian skin is a densely innervated soft tissue where neural mechanoreceptors embedded in the skin report to the brain the mechanical events arising at its surface. Up to now, models of the transformations of these events into neural signals relied on quasi-static or viscoelastic mechanical models of the skin tissue. Here we developed a model, which, in addition to elasticity and viscosity, accounted for mass to accurately reproduce the propagation of mechanical waves observed in vivo. Skin dynamics converted sensory inputs into rapidly evolving spatiotemporal patterns that magnified the information made available to a population of mechanoreceptors. Accounting for dynamics in the skin tissue thus greatly enhanced the separability of tactile inputs and was efficient for a large range of mechanical parameter values. This advantage vanished when these parameters were set to approximate the quasi-static or viscoelastic cases.
