Abstract
For decades, the dominant theory of roughness coding in the somatosensory nerves posited that perceived roughness was determined by the spatial pattern of activation in one population of tactile nerve fibers, namely slowly adapting type 1 (SA1) afferents. Indeed, the perceived roughness of coarsely textured surfaces tracks the spatial variation in SA1 responses – the degree to which response strength varies across SA1 afferents. However, in a later study, the roughness of a different set of dot patterns was found to be a monotonic function of dot spacing, a result interpreted as evidence that roughness was determined by the strength of SA1 responses – the population firing rate – rather than their spatial layout. Then again, the spatial variation hypothesis was not tested directly as afferent responses to the conflicting patterns were not measured. To fill this gap, we simulated afferent responses to the dot patterns used in these roughness coding experiments using a model of skin mechanics. We then implemented the spatial variation and firing rate models of roughness based on these simulated responses to generate predictions of perceived roughness. We found that the spatial variation model accounts for perceived roughness under all tested conditions whereas the firing rate model does not.
Highlights
A major question in neuroscience is how patterns of activation in sensory neurons give rise to percepts
One of the key features identified by Johnson and colleagues was an inverse U-shaped curve describing roughness as a function of dot spacing[8]: Dot patterns were roughest when dots were separated by 3 mm and decreased when the dot spacing deviated from this value; the shape of this function could be readily explained from the proposed neural code
The mean strain evoked by each dot pattern is proportional to the mean slowly adapting type 1 (SA1) firing rate that is evoked by the same dot pattern (Fig. 2d)
Summary
A major question in neuroscience is how patterns of activation in sensory neurons give rise to percepts. The variation in this spatial pattern – the degree to which the strength of the evoked response varies over the spatial distribution of afferents – was shown to co-vary with judgments of perceived roughness for these same surfaces measured in psychophysical experiments with human observers[3,4,5] This neural code made intuitive sense because a perfectly flat surface excites SA1 afferents distributed across the fingertip in similar ways, resulting in low spatial variation, whereas a texture consisting of tall, skinny dots arranged in a moderately dense configuration excites some SA1 afferents (those whose receptive field falls over a dot) and not others (those whose receptive fields fall between dots). We conclude that roughness perception is driven, at least in part, by the spatial variation in SA1 responses
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