Keywords: neural network, Hodgkin-Huxley model, stochastic resonance, complex systems, tactile sensing, finite element analysis, human skin.

Stochastic resonance (SR), one of the basic principles intrinsically possessed by any living thing for highly adapting to complicated environments including various disturbances, attracts much research attention because it is considered a source of such high adaptation capability, as described in some survey papers [1,2]. Deterministic dynamics may be enhanced when fluctuation and random disturbance occurs in a nonlinear dissipative system. Based on this SR effect, a signal can be detected by superimposing a proper noise on an undetectable weak signal. The SR phenomenon in a single neuron, which is also observed in simulations of a numerical model, is being examined in such realms as neurophysics and brain science.

Although in tactile sensing, noise is inevitably mixed by contact with an object and a sensor's movement on it, a human being can evaluate the several micrometers of unevenness on the object's surface. In addition to these experimental results, humans have been found to effectively utilize SR in a tactile sensation. Therefore, we expect to build a new tactile sensing system that possesses high robustness for adaptation over environmental changes and disturbance by mimicking biological information processing.

The present research applies SR to tactile sensing. Based on the results, we intend to develop a tactile sensing system capable of measuring an object surface with high precision not only in a controlled environment like a precision measurement room but also in a living environment. This system will be utilized in tactile sensor-mounted robots that have been researched in previous papers.

Mechanical noise occurs when we scan an object surface with our finger, and human tactile receptors emit spontaneous immanency noise. Therefore, for SR in tactile sensation, there are two possibilities: one is caused by the nonlinearity of the human skin's mechanical response; the other is caused by the chaotic characteristics of neurons. For the former possibility, we calculated human skin deformation using the finite element method considering viscoelasticity and geometrical non-linearity. For the latter possibility, we performed numerical simulations using a Hodgkin-Huxley (HH) model capable of emulating a squid's neuron activities. Since von Mises equivalent stress distributed in the skin does not show time dependent variation, the non-linearity of human skin mechanical response does not play an important role in SR. On the other hand, the HH model shows SR on tactile sensations. However, SNR is markedly varied even if a tiny deviation of input noise intensity is applied. This result implies that the HH model requires modification to remove the bumpy variation for robotic applications.

1

F. Moss, K. Wiesenfeld, "The Benefits of Background Noise", Nikkei Science, pp. 126-131, Oct. 1995, (in Japanese).

2

L. Gammaitoni, P. Hänggi, P. Jung, F. Marchesoni, "Stochastic Resonance", Reviews of Modern Physics, Vol. 70, No. 1, pp. 223-287, 1998. doi:10.1103/RevModPhys.70.223

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