Structural Characterization of the Whisker System of the Rat

Voges, Danja; Carl, K.; Klauer, Gertrud; Uhlig, Robert P.; Schilling, Cornelius; Behn, Carsten; Witte, Hartmut

doi:10.1109/jsen.2011.2161464

Cited by 49 publications

(32 citation statements)

References 15 publications

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“…Notice that the Young's modulus E , the density ρ, and the cone angle α vary in a small range across the whisker pad (Hartmann et al, 2003; Carl et al, 2012; Voges et al, 2012) so that c wave should weakly depend on the whisker identity. Similarly, the values reported for the damping factor appear to lie within a rather small range (Hartmann et al, 2003; Neimark et al, 2003).…”

Section: Discussionmentioning

confidence: 99%

Whisker encoding of mechanical events during active tactile exploration

Boubenec¹,

Shulz²,

Debrégeas³

2012

Front. Behav. Neurosci.

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Rats use their whiskers to extract a wealth of information about their immediate environment, such as the shape, position or texture of an object. The information is conveyed to mechanoreceptors located within the whisker follicle in the form of a sequence of whisker deflections induced by the whisker/object contact interaction. How the whiskers filter and shape the mechanical information and effectively participate in the coding of tactile features remains an open question to date. In the present article, a biomechanical model was developed that provides predictions of the whisker dynamics during active tactile exploration, amenable to quantitative experimental comparison. This model is based on a decomposition of the whisker profile into a slow, quasi-static sequence and rapid resonant small-scale vibrations. It was applied to the typical situation of a rat actively whisking across a solid object. Having derived the quasi-static sequence of whisker deformation, the resonant properties of the whisker were analyzed, taking into account the boundary conditions imposed by the whisker/surface contact. We then focused on two elementary mechanical events that are expected to trigger significant neural responses, namely (1) the whisker/object first contact and (2) the whisker detachment from the object. Both events were found to trigger a deflection wave propagating upward to the mystacial pad at constant velocity of ≈3–5 m/s. This yielded a characteristic mechanical signature at the whisker base, in the form of a large peak of negative curvature occurring ≈4 ms after the event has been triggered. The dependence in amplitude and lag of this mechanical signal with the main contextual parameters (such as radial or angular distance) was investigated. The model was validated experimentally by comparing its predictions to high-speed video recordings of shock-induced whisker deflections performed on anesthetized rats. The consequences of these results on possible tactile encoding schemes are briefly discussed.

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Section: Discussionmentioning

confidence: 99%

Whisker encoding of mechanical events during active tactile exploration

Boubenec¹,

Shulz²,

Debrégeas³

2012

Front. Behav. Neurosci.

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“…1(a). From outside to in, the whisker consists of the cuticle, the cortex, and the medulla, which can be approximated as hollow [15][16][17][18]. Whiskers have an intrinsic curvature that is approximately quadratic and a Young's modulus that ranges between 2.90 GPa to 4.92 GPa and also increases slightly from base to tip [17,19,20].…”

Section: Methods a Artificial Whiskermentioning

confidence: 99%

A Novel Whisker Sensor Used for 3D Contact Point Determination and Contour Extraction

Emnett¹,

Graff²,

Hartmann³

2018

Robotics: Science and Systems XIV

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Abstract-We developed a novel whisker-follicle sensor that measures three mechanical signals at the whisker base. The first two signals are closely related to the two bending moments, and the third is an approximation to the axial force. Previous simulation studies have shown that these three signals are sufficient to determine the three-dimensional (3D) location at which the whisker makes contact with an object. Here we demonstrate hardware implementation of 3D contact point determination and then use continuous sweeps of the whisker to show proof-of principle 3D contour extraction. We begin by using simulations to confirm the uniqueness of the mapping between the mechanical signals at the whisker base and the 3D contact point location for the specific dimensions of the hardware whisker. Multi-output random forest regression is then used to predict the contact point locations of objects based on observed mechanical signals. When calibrated to the simulated data, signals from the hardware whisker can correctly predict contact point locations to within 1.5 cm about 74% of the time. However, if normalized output voltages from the hardware whiskers are used to train the algorithm (without calibrating to simulation), predictions improve to within 1.5 cm for about 96% of contact points and to within 0.6 cm for about 78% of contact points. This improvement suggests that as long as three appropriate predictor signals are chosen, calibrating to simulations may not be required. The sensor was next used to perform contour extraction on a cylinder and a cone. We show that basic contour extraction can be obtained with just two sweeps of the sensor. With further sweeps, it is expected that full 3D shape reconstruction could be achieved.

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“…The scales are set in an irregular pattern, which is the same in all of the whisker parts. However, the size of the scales does vary in different parts (Voges et al, 2012). The whisker has an inhomogeneous structure that produces different levels of elasticity in different sections, as reflected in Young's modulus.…”

Section: The Input Of the Vibrissal Systemmentioning

confidence: 99%