Estimating Synaptic Parameters from Mean, Variance, and Covariance in Trains of Synaptic Responses

摘要: Fluctuation analysis of synaptic transmission using the variance-mean approach has been restricted in past to steady-state responses. Here we extend this method short repetitive trains responses, during which response amplitudes are not stationary. We consider intervals between trains, long enough so that system is same average state at beginning each train. This allows ensemble means and variances for a train separately. Thus, modifications efficacy short-term plasticity can be attributed changes parameters. In addition, provide practical guidelines covariance successive responses trains. Explicit algorithms estimate parameters derived tested by Monte Carlo simulations on basis binomial model transmission, allowing quantal variability, heterogeneity release probability, postsynaptic receptor saturation desensitization. find combined variance advantageous yielding an number sites, independent probability under certain conditions. Furthermore, it one calculate apparent size sequence stimuli.