Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

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

W R Holmes1, J Ambros-Ingerson, L M Grover

  • 1Neuroscience Program, Department of Biological Sciences, Ohio University, Athens, OH 45701, USA. holmes@ohio.edu

Journal of Computational Neuroscience
|May 10, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Elevated hydrostatic pressure destabilizes VE-cadherin junctions in a time and shear stress dependent manner: An endothelium-on-chip study.

APL bioengineering·2025
Same author

Microfluidics as a Powerful Tool to Investigate Microvascular Dysfunction in Trauma Conditions: A Review of the State-of-the-Art.

Advanced biology·2024
Same author

Local injection of a hexametaphosphate formulation reduces heterotopic ossification <i>in vivo</i>.

Materials today. Bio·2020
Same author

A gellan-based fluid gel carrier to enhance topical spray delivery.

Acta biomaterialia·2019
Same author

Periostitis-Its Causes and Remedies.

The American journal of dental science·2019
Same author

Duplicating Vulcanite Plates.

The American journal of dental science·2019
Same journal

Hierarchical learning creates invariant schema within plastic neural networks.

Journal of computational neuroscience·2026
Same journal

Intrinsic chaos control in cortical circuits: A minimal E-I-M rate model for primary visual cortex.

Journal of computational neuroscience·2026
Same journal

Modeling developmental spiking behavior driven by ionic current dynamics of mouse and human inner hair cells using a calcium-enhanced Izhikevich framework.

Journal of computational neuroscience·2026
Same journal

A biophysically grounded model of glutamatergic synaptic transmission integrating glutamate transport, receptor kinetics, and electrotonic effects.

Journal of computational neuroscience·2026
Same journal

When can neuronal activity-dependent homeostatic plasticity maintain circuit-level properties?

Journal of computational neuroscience·2026
Same journal

A charge conservative finite volume discretization of the Hodgkin-Huxley model.

Journal of computational neuroscience·2026
See all related articles

Detailed neuron models require cell-specific data. Using standard parameters with reconstructed morphologies often yields unrepresentative results, highlighting the need for experimental calibration and multiple reconstructions for accurate modeling.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Biophysics

Background:

  • Detailed neuron models ideally integrate morphological and electrophysiological data from the same cell, but this is rarely achieved.
  • Current practices often involve using public morphology databases and standard parameter values, assuming model representativeness.

Purpose of the Study:

  • To test the assumption that models with standard parameters and reconstructed morphologies yield representative experimental results.
  • To investigate the impact of morphological variability and parameter fitting on neuron model accuracy.

Main Methods:

  • Developed CA1 hippocampal pyramidal neuron models using four distinct morphologies from public databases.
  • Employed the NEURON simulation environment's multiple run fitter to adjust parameter values against experimental data from 19 CA1 pyramidal cells.

Related Experiment Videos

Main Results:

  • Models with fixed standard parameters failed to represent experimental data.
  • Allowing parameter values to vary resulted in excellent fits, but fitted values differed significantly across reconstructions and deviated from standard values.
  • Fitted parameter differences correlated with variations in cell diameter, length, membrane area, and volume, suggesting compensation for morphological discrepancies.

Conclusions:

  • Neuron models incorporating reconstructed morphologies necessitate calibration with experimental data, even when data originates from the same cell.
  • Generating model results using multiple reconstructions is crucial.
  • Ensuring morphological and experimental cells originate from the same animal strain and age is important.
  • Relying on standard parameter values without calibration may lead to unrepresentative model outcomes.