Abstract
The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.
Highlights
Currently available experimental data make it possible to create increasingly complex multi-compartmental conductance-based neuron models, which have the potential to imitate the behavior of real neurons with great accuracy (De Schutter and Bower, 1994a,b; Poirazi et al, 2003; Hay et al, 2011)
One alternative to using detailed biophysical models, which is often used in network simulations, is to utilize much simpler model neurons
We demonstrate the utility of this approach in one of the use cases described below, where input to the neuron consisted of two consecutive current pulses of different duration and amplitude
Summary
Available experimental data make it possible to create increasingly complex multi-compartmental conductance-based neuron models, which have the potential to imitate the behavior of real neurons with great accuracy (De Schutter and Bower, 1994a,b; Poirazi et al, 2003; Hay et al, 2011). These models have many parameters, which are often poorly (or, at best, indirectly) constrained by the available data. The task of finding the optimal parameter values is highly non-trivial, and has been the subject of extensive research (Vanier and Bower, 1999; Keren et al, 2005; Huys et al, 2006; Druckmann et al, 2007, 2008; Gurkiewicz and Korngreen, 2007; Van Geit et al, 2007, 2008; Huys and Paninski, 2009; Rossant et al, 2010, 2011; Eichner and Borst, 2011; Hendrickson et al, 2011; Bahl et al, 2012; Svensson et al, 2012; Vavoulis et al, 2012)
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