Nonlinearization: naturalistic stimulation and nonlinear dynamic behavior in a spider mechanoreceptor
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In a previous study, we used linear frequency response analysis to show that naturalistic stimulation of spider primary mechanosensory neurons produced different response dynamics than the commonly used Gaussian random noise. We isolated this difference to the production of action potentials from receptor potential and suggested that the different distribution of frequency components in the naturalistic signal increased the nonlinearity of action potential encoding. Here, we tested the relative contributions of first- and second-order processes to the action potential signal by measuring linear and quadratic coherence functions. Naturalistic stimulation shifted the linear coherence toward lower frequencies, while quadratic coherence was always higher than linear coherence and increased with naturalistic stimulation. In an initial attempt to separate the order of time-dependent and nonlinear processes, we fitted quadratic frequency response functions by two block-structured models consisting of a power-law filter and a static second-order nonlinearity in alternate cascade orders. The same cascade models were then fitted to the original time domain data by conventional numerical analysis algorithms, using a polynomial function as the static nonlinearity. Quadratic models with a linear filter followed by a static nonlinearity were favored over the reverse order, but with weak significance. Polynomial nonlinear functions indicated that rectification is a major nonlinearity. A complete quantitative description of sensory encoding in these primary mechanoreceptors remains elusive but clearly requires quadratic and higher nonlinear operations on the input signal to explain the sensitivity of dynamic behavior to different input signal patterns.
KeywordsMechanotransduction Nonlinear Sensory coding Naturalistic Spider Action potential
This study was supported by the Canadian Institutes for Health Research and the Natural Sciences and Engineering Council of Canada.
- Bendat JS, Piersol AG (1980) Engineering applications of correlation and spectral analysis. Wiley, New YorkGoogle Scholar
- Gettrup E (1963) Phasic stimulation of a thoracic stretch receptor. J Exp Biol 40:323–333Google Scholar
- Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1990) Numerical recipes in C. The art of scientific computing. Cambridge University Press, CambridgeGoogle Scholar
- Pringle JW, Wilson VJ (1952) The response of a sense organ to a harmonic stimulus. J Exp Biol 29:220–234Google Scholar
- Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, UrbanaGoogle Scholar