Changes in fractional slow tail amplitude with changes in fssalso support random mixing. (A) For each injection ratio, fss and tail current amplitudes normalized to BKGmax for a patch (AST) were measured at different times of removal of inactivation by trypsin. Resulting means and SD for both fss and AST values are plotted. (B) Based on the fitted parameters for the random mixing model from Fig. 6, fractional slow tail was calculated for each fss value and overlaid over the values from A. The maximal AST value for the idealized curves was stepped between 0.20 and 0.25 to yield the minimum SSQ (0.003508) between experimental and idealized. (C) Based on fitted values for the segregated model, idealized curves for AST as a function of fss were generated and overlaid over the experimental data. The minimal SSQ (0.00679) was obtained at AST = 0.21. SSQ, sum of squares.
Figure 7.

Changes in fractional slow tail amplitude with changes in f ss also support random mixing. (A) For each injection ratio, fss and tail current amplitudes normalized to BKGmax for a patch (AST) were measured at different times of removal of inactivation by trypsin. Resulting means and SD for both fss and AST values are plotted. (B) Based on the fitted parameters for the random mixing model from Fig. 6, fractional slow tail was calculated for each fss value and overlaid over the values from A. The maximal AST value for the idealized curves was stepped between 0.20 and 0.25 to yield the minimum SSQ (0.003508) between experimental and idealized. (C) Based on fitted values for the segregated model, idealized curves for AST as a function of fss were generated and overlaid over the experimental data. The minimal SSQ (0.00679) was obtained at AST = 0.21. SSQ, sum of squares.

This Feature Is Available To Subscribers Only

or Create an Account

Close Modal
Close Modal