Characterization of neuron models
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Modern neuron models are large, complex entities. The ability to better simulate these complex models has been iven by the development of ever more powerful and cheap computers. The capacity to manage and understand the models has lagged behind improvements in simulation ability almost from the inception of neuron modeling. Despite the computing power currently available, more powerful simulation platforms and strategies are needed to cope with current and next generation neuron models. This thesis develops methodologies aimed at better characterizing motoneuron models. The hypothesis presented is that relationships between model outputs in addition to the relationships between model inputs (parameters) and outputs (behaviors) provide a characteristic description of the model that describes the model in a more useful way than just model behaviors. This description can be used to compare a model to different implementations of the same motoneuron and to experiment data. Data mining and data reduction techniques were used to process the data. Principal component analysis was used to indicate a significant, consistent reduction in dimensionality in an intermediate, mechanistic layer between model inputs and outputs. This layer represents the non-linear relationships between input and output, implying that if the non-linear relationships of a model were better understood and accessible, a model could be manipulated by varying the mechanism layer members, or rather the model parameters that primarily affect a mechanism layer member. Hierarchical cluster analysis showed similarity between sensitivity analyses data from models with random parameter sets. A main cluster represented the main region of model behavior with outlying clusters representing non-physiological behavior. This indicates that sensitivity analysis data is a good candidate for a model signature. The results demonstrate the usefulness of cluster analysis in identifying the similarities between data used as a model characterization metric or model signature. Its application is also valuable in identifying the main region of useful activity of a model, thus helping to identify a potential 'average' parameter set. Furthermore, factor analysis also proves functional in identifying members of the mechanism layer as well as the degree to which model outputs are affected by these members.