Effects of variability in anatomical reconstruction techniques on models of synaptic integration by dendrites: a comparison of three internet archives

Szilágyi, Tibor; Schutter, Erik De

doi:10.1111/j.1460-9568.2004.03222.x

Cited by 21 publications

(20 citation statements)

References 37 publications

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“…In the present study, for a given cell morphology, fits to the 19 sets of experimental data yielded remarkably consistent parameter values indicating that the cells we sampled in our study were much more morphologically homogeneous than those we selected from the databases. Also, previous analyses of data from databases found that cells reconstructed in the same lab tend to have similar morphological parameters with much more heterogeneity found between labs (Ambros-Ingerson and Holmes, 2005;Scorcioni et al, 2004;Szilagy and De Schutter, 2004). Animal strain differences do not explain the wide differences in morphological characteristics shown in Table 4.…”

Section: Biologically Accurate Modelsmentioning

confidence: 56%

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

2006

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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.

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Section: Biologically Accurate Modelsmentioning

confidence: 56%

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

2006

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“…While there are several other archives of rat hippocampal CA1 cells, they are heterogeneous in terms of animal strains and ages, reconstruction techniques, and morphometric properties (Scorcioni et al, 2004;Szilagyi and De Schutter, 2004). We have selected one of the largest homogenous archives of CA1 cells for this model, but we have also measured the number of bifurcations in CA1 dendritic trees from four other publicly available data sets.…”

Section: Methodsmentioning

confidence: 99%

Local Diameter Fully Constrains Dendritic Size in Basal but not Apical Trees of CA1 Pyramidal Neurons

Donohue

Ascoli

2005

J Comput Neurosci

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Computational modeling of dendritic morphology is a powerful tool for quantitatively describing complex geometrical relationships, uncovering principles of dendritic development, and synthesizing virtual neurons to systematically investigate cellular biophysics and network dynamics. A feature common to many morphological models is a dependence of the branching probability on local diameter. Previous models of this type have been able to recreate a wide variety of dendritic morphologies. However, these diameter-dependent models have so far failed to properly constrain branching when applied to hippocampal CA1 pyramidal cells, leading to explosive growth. Here we present a simple modification of this basic approach, in which all parameter sampling, not just bifurcation probability, depends on branch diameter. This added constraint prevents explosive growth in both apical and basal trees of simulated CA1 neurons, yielding arborizations with average numbers and patterns of bifurcations extremely close to those observed in real cells. However, simulated apical trees are much more varied in size than the corresponding real dendrites. We show that, in this model, the excessive variability of simulated trees is a direct consequence of the natural variability of diameter changes at and between bifurcations observed in apical, but not basal, dendrites. Conversely, some aspects of branch distribution were better matched by virtual apical trees than by virtual basal trees. Dendritic morphometrics related to spatial position, such as path distance from the soma or branch order, may be necessary to fully constrain CA1 apical tree size and basal branching pattern.

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“…To test the generalization of our results (Szilagyi and De Schutter 2004), we used a separate set of seven reconstructed young rat TTCs, which were provided to NeuroMorpho by A. Korngreen.…”

Section: Markrammentioning

confidence: 99%

Preserving axosomatic spiking features despite diverse dendritic morphology

Hay

Schürmann

Markram

et al. 2013

Journal of Neurophysiology

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Hay E, Schürmann F, Markram H, Segev I. Preserving axosomatic spiking features despite diverse dendritic morphology. J Neurophysiol 109: 2972-2981, 2013. First published March 27, 2013 doi:10.1152/jn.00048.2013.-Throughout the nervous system, cells belonging to a certain electrical class (e-class)-sharing high similarity in firing response properties-may nevertheless have widely variable dendritic morphologies. To quantify the effect of this morphological variability on the firing of layer 5 thick-tufted pyramidal cells (TTCs), a detailed conductance-based model was constructed for a threedimensional reconstructed exemplar TTC. The model exhibited spike initiation in the axon and reproduced the characteristic features of individual spikes, as well as of the firing properties at the soma, as recorded in a population of TTCs in young Wistar rats. When using these model parameters over the population of 28 three-dimensional reconstructed TTCs, both axonal and somatic ion channel densities had to be scaled linearly with the conductance load imposed on each of these compartments. Otherwise, the firing of model cells deviated, sometimes very significantly, from the experimental variability of the TTC e-class. The study provides experimentally testable predictions regarding the coregulation of axosomatic membrane ion channels density for cells with different dendritic conductance load, together with a simple and systematic method for generating reliable conductance-based models for the whole population of modeled neurons belonging to a particular e-class, with variable morphology as found experimentally.

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Effects of variability in anatomical reconstruction techniques on models of synaptic integration by dendrites: a comparison of three internet archives

Cited by 21 publications

References 37 publications

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

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

Local Diameter Fully Constrains Dendritic Size in Basal but not Apical Trees of CA1 Pyramidal Neurons

Preserving axosomatic spiking features despite diverse dendritic morphology

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