ArticleJournal of computational neuroscience2011
The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites.
Article in Journal of computational neuroscience, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- An integratediScience · 2026Article
- The Neural Analysis Toolkit Unifies Semi-Analytical Techniques to Simplify, Understand, and Simulate Dendrites.Neuroinformatics · 2026Article
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- A Multi-Scale, High-Fidelity Computational Model of the Mouse Triceps Surae Motor Pool.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Regulation of XOR function of reduced human L2/3 pyramidal neurons.Cognitive neurodynamics · 2024Article
- Onion skin is not a universal firing pattern for spinal motoneurons: simulation study.Journal of neurophysiology · 2024Article
- Introducing the Dendrify framework for incorporating dendrites to spiking neural networks.Nature communications · 2023Article
- Bifurcation analysis of motoneuronal excitability mechanisms under normal and ALS conditions.Frontiers in cellular neuroscience · 2023Article
- Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons.eLife · 2022Article
- An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain.PLoS computational biology · 2021Article
- In-silico development and assessment of a Kalman filter motor decoder for prosthetic hand control.Computers in biology and medicine · 2021Article
- Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses.eLife · 2021Article
- An efficient analytical reduction of detailed nonlinear neuron models.Nature communications · 2020Article
- The effects of model composition design choices on high-fidelity simulations of motoneuron recruitment and firing behaviors.Journal of neural engineering · 2018Article
- Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes.Frontiers in neuroinformatics · 2018Article
- A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells.Journal of neuroscience methods · 2018Article
- Distinct current modules shape cellular dynamics in model neurons.Neuroscience · 2016Article
- Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons.Journal of computational neuroscience · 2016Article
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3 authors.
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Abstract
Conductance-based neuron models are frequently employed to study the dynamics of biological neural networks. For speed and ease of use, these models are often reduced in morphological complexity. Simplified dendritic branching structures may process inputs differently than full branching structures, however, and could thereby fail to reproduce important aspects of biological neural processing. It is not yet well understood which processing capabilities require detailed branching structures. Therefore, we analyzed the processing capabilities of full or partially branched reduced models. These models were created by collapsing the dendritic tree of a full morphological model of a globus pallidus (GP) neuron while preserving its total surface area and electrotonic length, as well as its passive and active parameters. Dendritic trees were either collapsed into single cables (unbranched models) or the full complement of branch points was preserved (branched models). Both reduction strategies allowed us to compare dynamics between all models using the same channel density settings. Full model responses to somatic inputs were generally preserved by both types of reduced model while dendritic input responses could be more closely preserved by branched than unbranched reduced models. However, features strongly influenced by local dendritic input resistance, such as active dendritic sodium spike generation and propagation, could not be accurately reproduced by any reduced model. Based on our analyses, we suggest that there are intrinsic differences in processing capabilities between unbranched and branched models. We also indicate suitable applications for different levels of reduction, including fast searches of full model parameter space.
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