Abstract

Most biologically realistic simulations, especially highly realistic simulations of single neurons, have large numbers of parameters which are not strongly constrained by existing experimental data. In such cases, the modeler has to choose the parameters that cause the model to produce outputs which are as close as possible to the outputs of the real system. Doing this manually is a very tedious process: typically one parameter at a time is adjusted and the modeler sees if the simulation outputs are any closer to the desired behavior than before.

This tutorial discusses and compares both analytical and stochatic methods for performing automated parameter searches. It also provides advice on strategies for constraining the range of parameter space to be searched. It illustrates these methods with the GENESIS library of parameter search objects and functions that automate the search process. This allows searches that might take months of manual work to be done in a few days of automated searches with no user intervention whatsoever.

See the tutorial (parameter_searching.ppt) (Powerpoint version)

See the tutorial (parameter_searching.pdf) (PDF format)

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