### Using the MultipleRunFitter

Here is how to use a "bare" MultipleRunFitter to show that the 3-state boltzmann distribution is a good fit to the steady state HH potassium conductance data.

1) If you don't have the conductance data plotted in NEURON start it up now from the hh508.hoc file. Along with the NEURONMainMenu, you will see a graph with a family of conductance time courses and a graph with the steady state conductances (single line).

2) Copy the steady state conductance line to the clipboard. ie. in the steady state graph select the "PickVector" tool from the Graph menu (right mouse button) and then click slowly on the steady state graph line (left mouse button). The line will turn red while the button is pressed.

3) Select the NEURONMainMenu/Tools/Fitting/MultipleRunFitter item to start a MultipleRunFitter.

4) In the MulRunFitter, select Generators/AddFitnessGenerator/AddFunctionFitness. An "Unnamed Function Fitness" item preceded by a '-' will appear in the generator list of the MulRunFitter panel.

5) Make sure the list is in "Display" mode (The mode is selected via the "Generators" menu item) and double click on the "Unnamed Function Fitness" item you created to pop up a panel for that fitness generator. At any time that panel may be closed and another popped up from the main MulRunFitter panel.

6) In the generator panel, select the Fitness/CommonFunctionalForms/ThreeStateBoltzmann item and "Accept" the expression that pops up in the dialog. The expression

         A/(1 + exp(k1*(d1-$1)) + exp(k2*(d2-$1)))

will appear in the generator panel. The names, A, k1, d1, k2, d2 are the parameters and $1 is the independent variable (in this case, voltage clamp level)

7) Copy the steady state data from the Clipboard to the generator panel by placing the mouse in the Graph part of the generator and selecting the DataFromClipboard menu item (right mouse button shows the menu). The data will appear in the Graph as a red line and there will be a default region (indicated by two vertical blue lines) spanning the data. Press the "ErrorValue" button in the generator panel, The expression is evaluated at each data point and drawn on the Graph as a black line. The error value is the square norm beteen data and expression treated as continuous functions. If the parameters still have their default value of "1" the error value should be 119.4 .

8) Now is a good time to save what has been done so far. Setting up complicated optimization strategies with the fitter is a lengthy process and you don't want to lose the effort expended so far if a mistake is made later on (e.g. accidentally closing the main MulRunFitter instance panel; or crashing the fitter with parameters which were allowed to get out of range). The rule is to save if you have done enough work since the last save that you would mind if that work increment was lost. The NEURONMainMenu/File/SaveSession saves all windows on the screen but that is unsuitable since we only want to save the one MulRunFitter instance. Saving subsets of windows can be done either with the NEURONMainMenu/Window/GroupManager tool or the NEURONMainMenu/Window/Print&FileWindowManager tool. Here we use the latter. First pop up the Print&FileWindowManager tool by selecting NEURONMainMenu/Window/Print&FileWindowManager. To save, select the window item in the Print&FileWindowManager (PWM) corresponding to the main MulRunFitter instance (do not select any other windows such as the generators which are created by the main instance) by pressing the left mouse button (we assume you are in the "select" mode) so that the window icon appears on the paper side of the PWM and select the Session/SaveSelected menu item. At this time "temp1.ses" is an appropriate file name. Test the "temp1.ses" file by retrieving it with the NEURONMainMenu/File/LoadSession menu item (or, equivalently, with the PWM Session/Retrieve menu item).

9) Add the A,k1,d1,k2,d2 parameters to the MulRunFitter with the Parameters/AddParameter menu item. Unfortunately they must be added one at a time (but see the note below about the *.ses.ft1 file) It is simplest in this case just to type the name of the parameter into the string field editor of the variable chooser that pops up each time. The names of the parameters appear in the list. If a mistake is made, you can use the Parameters menu to change and remove names from the parameter list. When the parameter names are entered, I always save the session again.

> Note: when a MultipleRunFitter is saved in a session file, say temp1.ses, two other files are created called temp1.ses.fd1 and temp1.ses.ft1. The fd1 file contains mostly the data to be fit. The ft1 file, however, is intended to be a readable and easily understandable specification of the fitting strategy -- an essential piece of documentation in a lengthy fitting project . In many cases, it is easier to change that file than to use the GUI tool to modify that information.

10) Recall that the generator item in the list of generators had a '-' in front of it. This means that that generator will NOT be executed when the total error value is calculated. Select the Generators/UseGenerator item and double click on the generator item. Items with a '+' contribute to the total error value. Press the "ErrorValue" button. The value should be the same as the error value of the individual generator.

11) Pop up an optimizer control panel by selecting the MulRunFitter/Parameters/SelectOptimizers/Praxis menu item. Press the "Optimize" button on the resulting panel. The fit may converge faster if you first enter reasonable values into the parameter field editors. It may happen that bad initial values cause the fit to fail. If it does not seem to be converging, stop the fit by pressing the Stop button on the optimizer control panel and restarting, perhaps with a different set of parameters. When starting with parameter values all equal to 1, praxis finds the optimum in approximately 6400 function evaluations. With those (all 1's) starting values and using the MulRunFitter/Parameters/DomainPanel and setting the "group attributes" to "positive definite limits" and "use log scale", the fit succeeds in approximately 300 function evaluations.

NEURON hands-on course
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