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

A spatial stochastic neuronal model with Ornstein-Uhlenbeck input current.

Henry C Tuckwell1, Frederic Y M Wan, Jean-Pierre Rospars

  • 1Department of Mathematics, University of California, Irvine 92697, USA. tuckwell@b3e.jussieu.fr

Biological Cybernetics
|March 26, 2002
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

Stochastic Chlamydia Dynamics and Optimal Spread.

Bulletin of mathematical biology·2021
Same author

How fast do mobile organisms respond to stimuli? Response times from bacteria to elephants and whales.

Physical biology·2020
Same author

Regulatory feedback on receptor and non-receptor synthesis for robust signaling.

Developmental dynamics : an official publication of the American Association of Anatomists·2020
Same author

Replication-dependent size reduction precedes differentiation in Chlamydia trachomatis.

Nature communications·2018
Same author

Olfactory coding in the turbulent realm.

PLoS computational biology·2017
Same author

Optimal Proliferation and Differentiation of <i>Chlamydia Trachomatis</i>.

Studies in applied mathematics (Cambridge, Mass.)·2017
Same journal

Harmonic memory in phasor neural networks.

Biological cybernetics·2026
Same journal

Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.

Biological cybernetics·2026
Same journal

Foundational issues of network models in biology.

Biological cybernetics·2026
Same journal

Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.

Biological cybernetics·2026
Same journal

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Biological cybernetics·2026
Same journal

Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.

Biological cybernetics·2026
See all related articles

This study analyzes neuron models with Ornstein-Uhlenbeck (OU) process input currents, comparing them to white-noise models. Correlated synaptic input significantly alters neuron voltage responses and firing patterns compared to uncorrelated noise.

Area of Science:

  • Computational Neuroscience
  • Mathematical Biology
  • Biophysics

Background:

  • Neurons receive synaptic inputs that are inherently noisy and dynamic.
  • Previous models often simplify synaptic input as white noise, neglecting temporal correlations.
  • The Ornstein-Uhlenbeck (OU) process offers a more realistic model for correlated synaptic input.

Purpose of the Study:

  • To analyze the statistical properties of a neuron's membrane potential under OU process input.
  • To compare the effects of OU input with white-noise input on neuronal dynamics.
  • To investigate the impact of input current correlation on interspike interval distributions.

Main Methods:

  • Analytical solutions for mean, variance, and covariance of membrane potential under OU input.

Related Experiment Videos

  • Numerical simulations to estimate interspike interval distributions.
  • Comparison of results between OU and white-noise driven cable models.

    • Main Results:

    • Analytical expressions for voltage mean and variance derived for OU input.
    • Demonstrated substantial differences between OU and white-noise driven models.
    • Estimated interspike interval distributions reveal effects of input correlation.

    Conclusions:

    • Ornstein-Uhlenbeck process input provides a more nuanced understanding of neuronal responses than white noise.
    • Input current correlation significantly impacts neuronal firing statistics.
    • The study highlights the importance of realistic synaptic input modeling in neuroscience.