| Summary: | When modeling a neuron, modelers often focus on the values of parameters that produce a desired output. However, if these parameters are not unique, there could be a number of parameter sets that produce the same output. Thus, even though the values of the various maximum conductances, half activation voltages and so on differ, as a set they can produce the same spike height, firing rates, and so forth.
To examine whether or not parameter sets are unique, a 3-compartment motoneuron model was created that has 15 target outputs and 59 parameters. Using parameter searches, over one hundred parameter sets were created for this model that produced the same output (within tolerances). Parameter values vary between parameter sets and indicate that the parameter values are not unique. In addition, some parameters are more tightly constrained than others. Principal component analysis is used to examine the dimensionality of the input and output spaces.
However, neurons are more than static output generators. For example, a variety of neuromodulatory influences are known to shift parameter values to alter neuronal output. Thus the question arises as to whether this non-uniqueness extends from model outputs to the models sensitivities to its parameters. In this work, the non-unique parameter sets are further analyzed using sensitivity analyses and output correlations to show that these values vary significantly between these parameter sets. Therefore, each of these models will react to parameter variation differently.
This work concludes that parameter sets are non-unique but have varying sensitivity analyses and output correlations. The ramifications of this are discussed for both modelers and neuroscientists.
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