title: Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models.
abstract: The increase in available computational power and the higher quality of experimental recordings have turned the tuning of neuron model parameters into a problem that can be solved by automatic global optimization algorithms. Neurofitter is a software tool that interfaces existing neural simulation software and sophisticated optimization algorithms with a new way to compute the error measure. This error measure represents how well a given parameter set is able to reproduce the experimental data. It is based on the phase-plane trajectory density method, which is insensitive to small phase differences between model and data. Neurofitter enables the effortless combination of many different time-dependent data traces into the error measure, allowing the neuroscientist to focus on what are the seminal properties of the model.We show results obtained by applying Neurofitter to a simple single compartmental model and a complex multi-compartmental Purkinje cell (PC) model. These examples show that the method is able to solve a variety of tuning problems and demonstrate details of its practical application.
authors.forename: Werner
authors.initials: W
authors.lastname: Van Geit
authors.forename: Pablo
authors.initials: P
authors.lastname: Achard
authors.forename: Erik
authors.initials: E
authors.lastname: De Schutter