Testing the fit of a quantal model of neurotransmission.
Many studies of synaptic transmission have assumed a parametric model to estimate the mean quantal content and size or the effect upon them of manipulations such as the induction of long-term potentiation. Classical tests of fit usually assume that model parameters have been selected independently of the data. Therefore, their use is problematic after parameters have been estimated. We hypothesized that Monte Carlo (MC) simulations of a quantal model could provide a table of parameter-independent critical values with which to test the fit after parameter estimation, emulating Lilliefors's tests. However, when we tested this hypothesis within a conventional quantal model, the empirical distributions of two conventional goodness-of-fit statistics were affected by the values of the quantal parameters, falsifying the hypothesis. Notably, the tests' critical values increased when the combined variances of the noise and quantal-size distributions were reduced, increasing the distinctness of quantal peaks. Our results support two conclusions. First, tests that use a predetermined critical value to assess the fit of a quantal model after parameter estimation may operate at a differing unknown level of significance for each experiment. Second, a MC test enables a valid assessment of the fit of a quantal model after parameter estimation.
Greenwood
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Department of Cellular and Molecular Physiology, Yale University, New Haven, Connecticut 06510, USA. agreenwo@unm.edu
Landaw
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Brown
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D000818:Animals; D055592:Biophysical Phenomena; D001703:Biophysics; D016013:Likelihood Functions; D017774:Long-Term Potentiation / Q000502:physiology; D008959:Models, Neurological; D009010:Monte Carlo Method; D016012:Poisson Distribution; D009435:Synaptic Transmission / Q000502:physiology*