TABLE 1.

Summary table of the main features of selected models.

Model	Gerling et al., 2013	Saal et al., 2017	Ouyang et al., 2021	Hay and Pruszynski, 2020
Data	Monkeys	Monkeys	Monkeys	Humans
Afferent population type	SA1	SA1, RA1, RA2	SA1, RA1, RA2	RA1, second-order neurons
Receptive field	Simple, no multiple hotspots	Simple, no multiple hotspots	Simple, no multiple hotspots	Complex with multiple hotspots
Response properties	Firing rate Spike timing Response adaptation	Firing rate Spike timing Frequency tuning Response adaptation Edge enhancement	Firing rate Spike timing Frequency tuning Response adaptation Edge enhancement	Firing rate Spike timing Response adaptation Edge enhancement
Models of skin mechanics	3D Finite Element Model resembling different layers and viscoelastic properties	Skin treated as a flat surface – continuum mechanics to derive deformation	Skin treated as a resistance network	No, skin is only represented by a grid as reference for receptors location
Neural dynamics	Leaky integrate and fire	Leaky integrate and fire	Leaky integrate and fire	No
Stimuli	Static indentation of cylinders, bars, and spheres	Static spatiotemporal indentation of single pins that can be combined to form complex shapes	Static spatiotemporal indentation of single pins that can be combined to form complex shapes	Static indentation of edges and dots
Applications	Predicting behavioural response from simulated neural response. Assessing the effects of realistic skin properties (e.g., viscoelasticity) on the skin response with static indentation of cylinders, bars, spheres. Evaluating potential mechanisms of peripheral sensory processing at the level of first-order neurons.	Predicting behavioural response from simulated neural response. Assessing the effects of finger properties (e.g., skin elasticity and afferent density) on the neural population response to static and vibratory stimuli having a wide range of shapes. Real-time generation of spike trains for robotics and neuroprosthetics. Evaluating potential mechanisms of peripheral sensory processing at the level of first-order neurons.	Predicting behavioural response from simulated neural response. Simulating the neural population response to static and vibratory stimuli having a wide range of shapes. Real-time generation of spike trains for robotics and neuroprosthetics. Evaluating potential mechanisms of peripheral sensory processing at the level of first-order neurons.	Predicting behavioural response from simulated neural response. Assessing the role of complex receptive fields on the neural population response to statically indented edges and dots. Evaluating potential mechanisms of peripheral sensory processing at the level of second-order neurons.
Code	n/a	bensmaialab.github.io/software/	github.com/ouyangqq	senselab.med.yale.edu/modeldb
Documentation	n/a	Yes	Limited	Limited