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.

Efficient current-based optimization techniques for parameter estimation in multi-compartment neuronal models

“…Work is underway to examine performance on more complex spiking models and preliminary findings indicate that phase plane methods [2,5] may be helpful in this respect. Our aim is to incorporate this work into the CARMEN neuroinformatics infrastructure as a research tool for use by the general neuroscience community.…”

“…Automating this process promises high-throughput computational modeling of use to both experimenters (for rapid feedback on their experimental preparations) and modelers (for investigating the details of neuronal function). Hitherto, attempts to do this have focused on stochastic searches such as genetic algorithms and simulated annealing [1][2][3]. Such methods give robust estimates of model parameters but converge slowly or need to sample a large population of test cases in parallel, and therefore require substantial computing resources.…”

Automated neuron model optimization techniques: a review

“…However, because of the sharp increase in the computational power that is available to neuroscientists, which allows for the exploration of large sets of parameter values, and the increased complexity of neuron models, which makes the hand tuning of models more and more difficult, automated parameter search methods have become increasingly important. Automated tuning of neuronal model parameters is therefore an active topic and different algorithms have been published (Baldi et al 1998;Bhalla and Bower 1993;Bush et al 2005;Druckmann et al 2007;Gerken et al 2006;Keren et al 2005;Lewicki 1998;Prinz et al 2003;Tabak et al 2000;Van Geit et al 2007;Vanier and Bower 1999;Davison et al 2000;Holmes et al 2006;Huys et al 2006;Pettinen et al 2006;Reid et al 2007;Weaver and Wearne 2006;Haufler et al 2007;Tobin and Calabrese 2006).…”

“…It is of course impossible to describe each algorithm and its many variations but our goal is to give a good overview of this field to allow the reader to find the way in the literature. Last, we will discuss Neurofitter (Van Geit et al 2007), a software tool that allows the user to link different general optimization methods to the phase-plane trajectory density (PPTD) error function.…”

Using Neurofitter to fit a Purkinje cell model to experimental data

“…Therefore models of this neuron are difficult to hand-tune. We used Neurofitter [1], an automated neuron model parameter search tool, to fit both the passive parameters of a neuron model and the maximal conductances of the ion channels to an experimental data set.…”