Neural coding of passive lump detection in compliant artificial tissue

Abstract

Here, we investigate the neural mechanisms of detecting lumps embedded in artificial compliant tissues. We performed a combined psychophysical study of humans performing a passive lump detection task with a neurophysiological study in nonhuman primates (Macaca mulatta) where we recorded the responses of peripheral mechanoreceptive afferents to lumps embedded at various depths in intermediates (rubbers) of varying compliance. The psychophysical results reveal that human lump detection is greatly degraded by both lump depth and decreased compliance of the intermediate. The neurophysiology results reveal that only the slowly adapting type 1 (SA1) afferents provide a clear spatial representation of lumps at all depths and that the representation is affected by lump size, depth, and compliance of the intermediate. The rapidly adapting afferents are considerably less sensitive to the lump. We defined eight neural response measures that we hypothesized could explain the psychophysical behavior, including peak firing rate, spatial spread of neural activity, and additional parameters derived from these measures. We find that peak firing rate encodes the depth of the lump, and the neural spatial spread of the SA1 response encodes for lump size but not lump shape. We also find that the perception of lump size may be affected by the compliance of the intermediate. The results show that lump detection is based on a spatial population code of the SA1 afferents, which is distorted by the depth of the lump and compliance of the tissue.

Stretching the skin of the fingertip creates a perceptual and motor illusion of touching a harder spring

“…Using anesthesia and passive stimulation via a robot, Srinivasan and Lamotte (Srinivasan, LaMotte 1996) identified the unique role of tactile information in the discrimination of subtle differences in softness. However, passive or anesthetized touch is different from active touch (Gwilliam et al 2014). Recently, there has been a significant advancement in the development of devices for tactile stimulation of the finger pads (Prattichizzo, Pacchierotti & Rosati 2012, Quek et al 2014.…”

Computational Modeling Reinforces that Proprioceptive Cues May Augment Compliance Discrimination When Elasticity Is Decoupled from Radius of Curvature

“…Third, others employ stimuli that correctly characterize compliance by elastic modulus and account for elastic-to-elastic surface contact, but only use stimuli of flat surface geometry. Finally and relatedly, single-nerve, electrophysiological recordings have recently been conducted in response to compliant stimuli [5, 6]. To fill the gap between single-unit recordings and psychophysical studies, computational models are required to decipher how a population of afferents encodes compliance.…”

Grasp-squeeze adaptation to changes in object compliance leads to dynamic beta-band communication between primary somatosensory and motor cortices

“…It is known that in generating outputs for dexterous manipulation 1 or participating in motor skill learning 2 , M1 plays an essential role. The primary somatosensory cortex (S1) is known to be crucial in receiving tactile sensory input 3 . Among studies of how tactile stimuli are encoded in S1, Weber et al 4 studied how spatial and temporal codes mediate tactile perception of static natural textures while Chowdhury et at 5 showed how its Area 2 also encodes proprioception in the whole arm kinematics.…”