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EDITORIAL FOCUS
), its cause and nature remain uncertain.
In the tactile system, usage of the term adaptation has been somewhat loose and potentially confusing. One common use is in the classification of neural responses. For example, rapidly adapting afferents (FAI or RA, and FAII or PC) respond only to the dynamic components of a stimulus applied to the skin, whereas slowly adapting afferents (SAI and SAII) respond to both the dynamic and static components of the stimulus (Talbot et al. 1968). This type of adaptation, or filtering, has a time course of the order of a second or less and is most likely attributable to the mechanics of the receptor (Loewenstein and Skalak 1966). The second—and markedly different—way in which adaptation has been used is most evident in the large number of psychophysical studies that show that a prolonged vibratory stimulus results in a decrease in the subject’s sensitivity to a subsequent vibratory stimulus (Berglund and Berglund 1970; Hollins et al. 1990). Typically, in such experiments the frequencies of the adapting and test stimuli have been varied allowing a number of hypotheses on the different roles of the primary afferent types, sometimes cast in the form of distinct information processing channels (Bolanowski et al. 1988). Commonly, in explanations of the psychophysical results, vibratory adaptation has been loosely coupled with the frequency response characteristics of receptors or primary afferents. Experiments by Ken Johnson and colleagues now elucidate the basic mechanisms for both of these types of adaptation (Bensmaia et al. 2005; Leung et al. 2005).
A major impediment in characterizing adaptation at the primary afferent level has been the lack of a method of quantifying it dynamically during the process of adaptation and during the process of recovery. Part of the difficulty is that primary afferent responses to vibration exhibit a ramp–plateau pattern with changes in amplitude having no effect on impulse rate over the extensive plateaus; thus impulse rate is not a good index of adaptation (Freeman and Johnson 1982b). Johnson and colleagues (Bensmaia et al. 2005; Leung et al. 2005, both in this issue of the Journal of Neurophysiology, p. 3023–3045) have developed a new method that allows continual measurement of the degree of adaptation and recovery by tracking estimated changes in the absolute and entrainment thresholds, I0 and I1 (Johnson 1974), over time. They systematically varied the frequency and amplitude of the adapting stimulus, as well as the frequency of the test stimulus, and quantified the shifts in the absolute and entrainment thresholds of the fibers. For all three afferents (SAI, FAI, and FAII) adaptation and recovery followed an exponential time course. These results were explained by a model in which the adaptation is mediated by changes in an afferent’s spiking threshold caused by events at the transduction site (Freeman and Johnson 1982a). Their data elucidate a number of adaptation paradoxes.
SAI afferents adapt more rapidly to a vibratory stimulus than FAI or FAII afferents do, even though an SAI afferent’s response to a step stimulus exhibits a static or slowly adapting component. This paradox is explained by the hypothesis of the experimenters, backed by their data, that adaptation to a vibratory stimulus is mediated by a change in the threshold at the transducer site. This process is exponential with a shorter time constant for SAI afferents than for FAI or FAII afferents. In contrast, the time course of response to a step stimulus depends on the mechanical filtering properties of the receptors; Merkel complexes are low-pass filters, whereas both Meissner and Pacinian corpuscles are band-pass filters.
The presence of two distinct mechanisms, a low- or band-pass filter plus desensitization at the transducer site, explains another apparent contradiction. In the frequency range where the sensitivity of SAI and FAI afferents decreases with frequency, perhaps erroneously suggesting that adaptation would decrease with increasing frequency, thresholds for SAI and FAI afferents actually increased substantially with increasing frequency.
Accurate profiles of the time course of adaptation and recovery allowed the authors to develop a model of the spike generation and transduction process. To explain adaptation, they hypothesized a specific ionic mechanism in which calcium ion concentration affects calcium-gated ion channels. This hypothesis can be tested in the future with detailed biophysical models and ultimately by direct measurement, thus putting the phenomenon on a sound biophysical footing.
These papers have practical implications for a new generation of tactile devices. Technological advances have led to a resurgence of interest in tactile display systems for use in prosthetic devices, haptic displays, and teleoperator systems (Iwamoto and Shinoda 2005; Kyung et al. 2005). Many of these devices rely on vibrotactile stimuli; an understanding of adaptation will be essential for optimizing these displays.
Ken Johnson died on May 12, 2005. For over 30 yr Ken has been a leader in the field of somatosensory research making innovative contributions to our understanding of neural coding in tactile sensation, particularly at the primary afferent level. The hallmarks of his research are elegant experimental paradigms, coupled with incisive quantitative analysis, used to relate neural population responses to psychophysical data. His insight has resulted in a significant increment in our comprehension of aspects of tactile sensation that are of fundamental importance.
Anatomy and Cell Biology, University of Melbourne, Parkville, Victoria, Australia
Address for reprint requests and other correspondence: A. W. Goodwin, Anatomy and Cell Biology, University of Melbourne, Parkville, Victoria 3010, Australia (E-mail: a.goodwin{at}unimelb.edu.au)
REFERENCES
Bensmaia SJ, Leung YY, Hsiao SS, and Johnson KO. Vibratory adaptation of cutaneous mechanoreceptive afferents. J Neurophysiol 94: 3023–3036, 2005.
Berglund U and Berglund B. Adaptation and recovery in vibrotactile perception. Percept Mot Skills 30: 843–853, 1970.[Web of Science][Medline]
Bolanowski SJ Jr, Gescheider GA, Verrillo RT, and Checkosky CM. Four channels mediate the mechanical aspects of touch. J Acoust Soc Am 84: 1680–1694, 1988.[CrossRef][Web of Science][Medline]
Freeman AW and Johnson KO. Cutaneous mechanoreceptors in macaque monkey: temporal discharge patterns evoked by vibration, and a receptor model. J Physiol 323: 21–41, 1982a.
Freeman AW and Johnson KO. A model accounting for effects of vibratory amplitude on responses of cutaneous mechanoreceptors in macaque monkey. J Physiol 323: 43–64, 1982b.
Hollins M, Goble AK, Whitsel BL, and Tommerdahl M. Time course and action spectrum of vibrotactile adaptation. Somatosens Mot Res 7: 205–221, 1990.[Web of Science][Medline]
Iwamoto T and Shinoda H. Ultrasound tactile display for stress field reproduction—examination of non-vibratory tactile apparent movement. In: World Haptics Conference (WHC 2005), Pisa, Italy, March 18–20, 2005. New York: IEEE Computer Society, 2005, p. 220–228.
Johnson KO. Reconstruction of population response to a vibratory stimulus in quickly adapting mechanoreceptive afferent fiber population innervating glabrous skin of the monkey. J Neurophysiol 37: 48–72, 1974.
Kyung K-U, Ahn M, Kwon D-S, and Srinisvasan MA. A compact broadband tactile display and its effectiveness in the display of tactile form. In: World Haptics Conference (WHC 2005), Pisa, Italy, March 18–20, 2005. New York: IEEE Computer Society, 2005, p. 600–601.
Leung YY, Bensmaia SJ, Hsiao SS, and Johnson KO. Time course of vibratory adaptation and recovery. J Neurophysiol 94: 3037–3045, 2005.
Loewenstein WR and Skalak R. Mechanical transmission in a Pacinian corpuscle. An analysis and a theory. J Physiol 182: 346–378, 1966.
Talbot WH, Darian-Smith I, Kornhuber HH, and Mountcastle VB. The sense of flutter-vibration: comparison of the human capacity with response patterns of mechanoreceptive afferents from the monkey hand. J Neurophysiol 31: 301–334, 1968.
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