Optimization -Estimate optimal coeffiecients

You are interested in modeling the shape of an individual action potential using the function,

f(x)= b(1)/(1+exp.^(-b(2)*(x-b(3))))- b(1)/(1+exp.^(-b(4)*(x-b(5))));

ActionPotentialRecords.mat, contain the voltage traces of several observed action potentials in the APs variable, as well as the time of each observation in times, whose values are [1260x1 double]. Estimate the optimal coefficients of your model based on the data available in the file and use the method of undersampling to determine the coefficient of variation (confidence measure) of each of your fit parameters (use 30 data subsets). An often used confidence threshold is 0.05 for the coefficient of variation.

if true
  % code
end
% 1) Create an anonymous function, fx, of your model
fx = @(x,p) ???;
for k=1:30
% 2) Create an array of indices, ix, that represent 1/30th of your data
% and that change with each loop iteration, k
ix = ???;
% 3) create your objective function (sum of squared error)
SSEf = @(x) sum( ( ??? ).^2 );
% 4) perform the optimization
???
end
% 5) compute parameter means and standard deviations