This will be a progress report on our use of Neurofitter [1], an automated neuron model parame-ter search tool, to fit both the passive and active parameters of a neuron model simultaneously. The approach is based on the phase-plane trajectory density method [2] that evaluates the differ-ence between the experimental voltage traces and the model output. Optimization algorithms like Evolution Strategies and Mesh Adaptive Search were used to search the parameter space of the model. The Neurofitter method was already tested before by fitting the parameters of a Purkinje cell model [3] to output generated by the model itself [4]. The big challenge in fitting to experimental data from a Purkinje cell is to start from a proper model representation as the ‘garbage in, gar-bage out’ theorem also applies to automated parameter fitting.
References
1. Van Geit W, Achard P, De Schutter E: Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models. Frontiers in Neuroinformatics 2007, 1:1.
2. LeMasson, Maex: Introduction to equation solving and parameter fitting. In Computational neuroscience: Realistic modeling for experimentalists. Londen : CRC Press; 2001:1-23.
3. De Schutter E, Bower JM: An active membrane model of the cerebellar Purkinje cell. I. Simu-lation of current clamps in slice. J Neurophysiol 1994, 71:375-400.
4. Achard P, De Schutter E: Complex parameter landscape for a complex neuron model. PLoS Comput Biol 2006, 2:e94.
