Natural scenes in tactile texture

Manfredi, Louise R.; Saal, Hannes P.; Brown, Kyler J.; Zielinski, Mark C.; Dammann, J. Francis; Polashock, Vicky S.; Bensmaı̈a, Sliman J.

doi:10.1152/jn.00680.2013

Cited by 181 publications

(158 citation statements)

References 77 publications

(93 reference statements)

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“…Three different texture samples were selected from a previously used dataset (7,20): one fine texture (wool gabardine), one coarse texture (upholstery), and a dot pattern (2-mm interdot spacing). Texture profiles (obtained by profilometry) were used as inputs to the model (with surfaces approximated as rigid).…”

Section: Simulated Experimentsmentioning

confidence: 99%

“…Coarse object features, such as edges and corners, are reflected in spatial patterns of activation in slowly adapting type I (SA1) and rapidly adapting (RA) fibers, which are densely packed in the fingertip (3,4). At the same time, interactions with objects and surfaces elicit high-frequency, low-amplitude surface waves that propagate across the skin of the finger and palm and excite vibration-sensitive Pacinian (PC) afferents all over the hand (5)(6)(7)(8).…”

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confidence: 99%

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Simulating tactile signals from the whole hand with millisecond precision

Saal

Delhaye

Rayhaun

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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When we grasp and manipulate an object, populations of tactile nerve fibers become activated and convey information about the shape, size, and texture of the object and its motion across the skin. The response properties of tactile fibers have been extensively characterized in single-unit recordings, yielding important insights into how individual fibers encode tactile information. A recurring finding in this extensive body of work is that stimulus information is distributed over many fibers. However, our understanding of population-level representations remains primitive. To fill this gap, we have developed a model to simulate the responses of all tactile fibers innervating the glabrous skin of the hand to any spatiotemporal stimulus applied to the skin. The model first reconstructs the stresses experienced by mechanoreceptors when the skin is deformed and then simulates the spiking response that would be produced in the nerve fiber innervating that receptor. By simulating skin deformations across the palmar surface of the hand and tiling it with receptors at their known densities, we reconstruct the responses of entire populations of nerve fibers. We show that the simulated responses closely match their measured counterparts, down to the precise timing of the evoked spikes, across a wide variety of experimental conditions sampled from the literature. We then conduct three virtual experiments to illustrate how the simulation can provide powerful insights into population coding in touch. Finally, we discuss how the model provides a means to establish naturalistic artificial touch in bionic hands.mechanoreceptor | tactile afferent | somatosensory periphery | skin mechanics | computational model T he human hand is endowed with thousands of mechanoreceptors of different types distributed across the skin, each innervated by one or more large myelinated nerve fibers (1). These fibers convey detailed information about contact events and provide us with an exquisite sensitivity to the form and surface properties of grasped objects (2, 3). During object manipulation and tactile exploration, the glabrous skin of the hand undergoes complex spatiotemporal mechanical deformations, which in turn, drive very precise spiking responses in individual afferents. Coarse object features, such as edges and corners, are reflected in spatial patterns of activation in slowly adapting type I (SA1) and rapidly adapting (RA) fibers, which are densely packed in the fingertip (3, 4). At the same time, interactions with objects and surfaces elicit high-frequency, low-amplitude surface waves that propagate across the skin of the finger and palm and excite vibration-sensitive Pacinian (PC) afferents all over the hand (5-8).Recording the activity of tactile nerve fibers in monkeys or humans is technically difficult, is slow, and generally yields responses from a single unit at a time (9, 10). Although such recordings have yielded powerful insights into the neural basis of touch, they provide a limited window into the information that the hand c...

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Section: Simulated Experimentsmentioning

confidence: 99%

mentioning

confidence: 99%

Simulating tactile signals from the whole hand with millisecond precision

Saal

Delhaye

Rayhaun

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

223

251

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“…Bensmaïa et al attempted to measure these high frequency components as a finger was sliding over the surface of a material by using a Hall-effect transducer attached to the finger [6]. Similarly, the propagated vibratory information was acquired using a laser Doppler velocity meter [7,8], microphones [9], accelerometers [10], and PVDF films [11] attached to the finger close to the tip, as well as a microphone [12] on the forearm. Although these vibrations in the adjacent skin originate from the vibratory deformations of the finger pad, these methods were not to measure the deformations of the individual finger pad.…”

Section: Introductionmentioning

confidence: 99%

“…In terms of texture and vibrotactile perception, the phases of the skin vibrations were less influential, and the power or intensity of the spectral density in the frequency domain are considered important [6,8,[23][24][25]. Some researchers have demonstrated that virtual textures or surfaces can be presented even without a representation of the phase information of the vibratory signals [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Wearable finger pad deformation sensor for tactile textures in frequency domain by using accelerometer on finger side

et al. 2017

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In this study, we set out to develop a method for estimating the fine and fast shear deformation of a finger pad, that is, the palm side of a fingertip, as it scans the surface of a material. Using a miniature accelerometer, we measured the acceleration at the radial skin, the deformation of which is accompanied by the shear deformation of the finger pad. Using a transfer function, as specified in a separate experiment, between the pad and side of a finger, we estimated the shear deformation of the finger pad in the frequency domain. A comparison between an estimate based on the accelerometer and another based on a precise force sensor for the tangential component of the interaction force between the fingertip and material surfaces showed that the estimation accuracy was sufficient for frequencies in excess of approximately 20-50 Hz and for skin deformation above 10 −6 m. Our technique merely requires that an accelerometer be attached to the side of the fingertip, which allows active texture exploration. These estimates or measurements of the finger skin deformation caused by touching materials will help us to comprehend the relationships between material surfaces and the resulting texture sensations.

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“…Analysis of natural scene statistics has proven to be a powerful approach for understanding neural coding within audition and vision, but has yet to be generally applied to the somatosensory system, although some work has been done on texture (Ritt et al, 2008;Manfredi et al, 2014). In the context of somatosensation, the way in which the animal's tactile sensors sample the environment will obviously play an important role in determining the tactile sensory data it will acquire.…”

Section: Introductionmentioning

confidence: 99%

Probability distributions of whisker–surface contact: quantifying elements of the rat vibrissotactile natural scene

Hobbs

Towal

Hartmann

2015

Journal of Experimental Biology

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Analysis of natural scene statistics has been a powerful approach for understanding neural coding in the auditory and visual systems. In the field of somatosensation, it has been more challenging to quantify the natural tactile scene, in part because somatosensory signals are so tightly linked to the animal's movements. The present work takes a step towards quantifying the natural tactile scene for the rat vibrissal system by simulating rat whisking motions to systematically investigate the probabilities of whisker-object contact in naturalistic environments. The simulations permit an exhaustive search through the complete space of possible contact patterns, thereby allowing for the characterization of the patterns that would most likely occur during long sequences of natural exploratory behavior. We specifically quantified the probabilities of 'concomitant contact', that is, given that a particular whisker makes contact with a surface during a whisk, what is the probability that each of the other whiskers will also make contact with the surface during that whisk? Probabilities of concomitant contact were quantified in simulations that assumed increasingly naturalistic conditions: first, the space of all possible head poses; second, the space of behaviorally preferred head poses as measured experimentally; and third, common head poses in environments such as cages and burrows. As environments became more naturalistic, the probability distributions shifted from exhibiting a 'row-wise' structure to a more diagonal structure. Results also reveal that the rat appears to use motor strategies (e.g. head pitches) that generate contact patterns that are particularly well suited to extract information in the presence of uncertainty.

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Natural scenes in tactile texture

Cited by 181 publications

References 77 publications

Simulating tactile signals from the whole hand with millisecond precision

Simulating tactile signals from the whole hand with millisecond precision

Wearable finger pad deformation sensor for tactile textures in frequency domain by using accelerometer on finger side

Probability distributions of whisker–surface contact: quantifying elements of the rat vibrissotactile natural scene

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