Fitting experimental data to models that use morphological data from public databases

Abstract

Ideally detailed neuron models should make use of morphological and electrophysiological data from the same cell. However, this rarely happens. Typically a modeler will choose a cell morphology from a public database, assign standard values for Ra, Cm, and other parameters and then do the modeling study. The assumption is that the model will produce results representative of what might be obtained experimentally. To test this assumption we developed models of CA1 hippocampal pyramidal neurons using 4 different morphologies obtained from 3 public databases. The multiple run fitter in NEURON was used to fit parameter values in each of the 4 morphological models to match experimental data recorded from 19 CA1 pyramidal cells. Fits with fixed standard parameter values produced results that were generally not representative of our experimental data. However, when parameter values were allowed to vary, excellent fits were obtained in almost all cases, but the fitted parameter values were very different among the 4 reconstructions and did not match standard values. The differences in fitted values can be explained by very different diameters, total lengths, membrane areas and volumes among the reconstructed cells, reflecting either cell heterogeneity or issues with the reconstruction data. The fitted values compensated for these differences to make the database cells and experimental cells more similar electrotonically. We conclude that models using fully reconstructed morphologies need to be calibrated with experimental data (even when morphological and electrophysiological data come from the same cell), model results should be generated with multiple reconstructions, morphological and experimental cells should come from the same strain of animal at the same age, and blind use of standard parameter values in models that use reconstruction data may not produce representative experimental results.

Successes and Rewards in Sharing Digital Reconstructions of Neuronal Morphology

“…On the one hand, this fact implies that particular attention should be exercised when fitting experimental data to models (Holmes et al 2006), an issue also related to the effect of reconstruction errors (Jaeger 2000). On the other hand, it suggests the need to verify the robustness and generality of the conclusions derived from computational simulations by using several available digital reconstructions for a given neuronal type.…”

Quantifying the Magnitude of Changes in Synaptic Level Parameters With Long-Term Potentiation

“…We showed previously that the hyperpolarization that followed minimal synaptic stimulation was caused by a GABA B current (Holmes et al 2006), so we added a GABA B current at the soma to our synaptic simulations. The functional form was where GABA B -amp is a scaling factor that was fit in the simulations (but does not represent the maximum amplitude of the current).…”

“…For this reason, we believe that for modeling results to be robust, simulations should be done with several reconstructions of the same cell type, as we have done here. We argue elsewhere (Holmes et al 2006) that model calibration can overcome inherent problems with cell heterogeneity and reconstructions to make modeled cells similar to experimental cells electronically while preserving the basic structure of the cell type modeled. While such calibration can lead to model parameter values that are not traditional, as in Table 2, we believe this is the correct approach to use.…”

“…Quantifying the magnitude of changes in synaptic level parameters with longterm potentiation. J Neurophysiol 96: 1478 -1491, 2006. First published June 7, 2006 doi:10.1152/jn.00248.2006.…”

Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons

“…Although this approach provides estimates of the conductance densities, it was shown that different parameter sets can give the same output (Bhalla and Bower 1993;Marder et al 2007), and that generally, the relationship between model output and the values of the parameters is often very complex (Foster et al 1993;Golowasch et al 2002;Achard and De Schutter 2006;Taylor et al 2006). It was suggested that for such models, more meaningful and robust estimates are obtained if one uses the morphology and recordings from the same cell (Holmes et al 2006). Examples of fitting morphologically-reconstructed model cells to the recordings from the same cell are available for different types of neurons (Cauller and Connors 1992;Rall et al 1992;Stratford et al 1989;Major et al 1994;Rapp et al 1994;Destexhe et al 1998;Stuart and Spruston 1998).…”