ArticleJournal of computational neuroscience2005
Local diameter fully constrains dendritic size in basal but not apical trees of CA1 pyramidal neurons.
Article in Journal of computational neuroscience, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Article
- Comparing basal dendrite branches in human and mouse hippocampal CA1 pyramidal neurons with Bayesian networks.Scientific reports · 2020Article
- Topological characterization of neuronal arbor morphology via sequence representation: I--motif analysis.BMC bioinformatics · 2015Article
- Models and simulation of 3D neuronal dendritic trees using Bayesian networks.Neuroinformatics · 2011Article
- Quantifying neuronal size: summing up trees and splitting the branch difference.Seminars in cell & developmental biology · 2008Review
- A comparative computer simulation of dendritic morphology.PLoS computational biology · 2008Article
- Non-parametric algorithmic generation of neuronal morphologies.Neuroinformatics · 2008Article
- L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies.Nature protocols · 2008Article
- Optimization principles of dendritic structure.Theoretical biology & medical modelling · 2007Article
- Successes and rewards in sharing digital reconstructions of neuronal morphology.Neuroinformatics · 2007Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
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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