STOCHASTIC RESONANCE IN MECHANORECEPTORS
James B. Fallon, Department of Electrical and Computer Systems Engineering, Monash University, Vic.
Noise can be defined as unwanted or meaningless data that is intermixed with relevant information. It is usual to attempt to minimise the level of ambient noise in non-linear systems designed to detect small signals, as it is believed that an optimal output signal-to-noise ratio is achieved when the input signal-to-noise ratio is largest. In some cases, however, the addition of noise to the input of a non-linear system can actually result in an increase in the output signal-to-noise ratio. This phenomenon can be explained by the theory of stochastic resonance.
Stochastic resonance, first proposed in 1981 as an explanation for a possible periodicity in the earth's ice-volume record 1,2, involves matching the time-scale of a noise-induced response of a system, to that of an otherwise sub-threshold periodic input. Many biological systems have been proposed to exhibit stochastic resonance, with one of the first being the mechanoreceptors in the tailfin of the crayfish Procambarus clarkii 3. Stochastic resonance has also been proposed to occur in inner hair cells 4,5, both slowly and rapidly adapting mechanoreceptors in the rat 6-8, and in psychophysical experiments involving tactile sensation 9.
Dithering is another mechanism whereby the addition of noise to the input of a system can result in an increase in the output signal-to-noise ratio. Therefore, the key features of stochastic resonance and dithering will be examined and a subsequent review of some of the proposed examples of stochastic resonance in mechanoreceptors from the literature will be made. Finally, recent experimental data illustrating stochastic resonance in slowly adapting mechanoreceptors in the toad (Bufo marinus) and psychophysical experiments involving tactile sensation will be presented.
(1) Benzi R, Sutera A, Vulpiani A. Journal of Physics A Mathematical & General. 1981;14:L453-L457.
(2) Benzi R, Parisi G, Sutera A, Vulpiani A. Tellus. 1982;34:10-16.
(3) Douglass JK, Wilkens L, Pantazelou E, Moss F. Nature. 1993;365:337-340.
(4) Jaramillo F, Wiesenfeld K. Nature Neuroscience. 1998;1:384-388.
(5) Jaramillo F, Wiesenfeld K. Chaos Solitons & Fractals. 2000;11:1869-1874.
(6) Collins JJ, Imhoff TT, Grigg P. Journal of Neurophysiology. 1996;76:642-645.
(7) Ivey C, Apkarian AV, Chialvo DR. Journal of Neurophysiology. 1998;79:1879-1890.
(8) Nozaki D, Mar DJ, Grigg P, Collins JJ. Physical Review Letters. 1999;82:2402-2405.
(9) Collins JJ, Imhoff TT, Grigg P. Nature. 1996;383:770.
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