A diverse population of mechanosensitive afferents in the skin informs our sense of touch. While empirical measurements can be made from single afferent units using microneurography, the derivation of a population response and connections between disparate measurement modalities require computational approaches. Prior models have tended to be stimulus-dependent and, at least in part data driven, which can hinder their ability to predict neural responses to untrained stimuli. Moreover, most prior models have required precise knowledge of contact relative to receptive field center. This is unrealistic, in that a neuron does not know the relative location of the stimulus but only responds to a spatial pattern of stress and/or strain near its receptive field. This talk will describe a biophysical model designed to predict the firing responses of both single-unit mechanoreceptive afferents, and populations of afferents, in response to thin, low-force monofilaments indented into the human finger. As an input, this effort takes a high-resolution imaging approach using 3D digital image correlation to measure the skin surface. Then, by making small adjustments in the parameters of the modeled biophysical neuron, it can mediate peak and steady-state firing properties and, notably, the size of receptive fields, highlighting how these factors are interrelated. Moreover, in varying these factors in concert with the density of the population of afferents, the model can differentiate the monofilament stimuli based on temporal patterns in the recruitment of receptive fields.
Gregory J. Gerling (Sun,) studied this question.
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