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

Stochastic models of neuronal dynamics.

L M Harrison1, O David, K J Friston

  • 1The Wellcome Department of Imaging Neuroscience, Institute of Neurology, UCL, 12 Queen Square, London WC1N 3BG, UK. l.harrison@fil.ion.ucl.ac.uk

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|August 10, 2005
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

The effect of dynamic mobilization exercises and therapeutic trunk exercises on superficial epaxial and hypaxial muscle activity in horses.

Journal of equine veterinary science·2026
Same author

Topographic Variation in Human Neurotransmitter Receptor Densities Explains Differences in Intracranial EEG Spectra.

Human brain mapping·2025
Same author

Resilience strengthening in youth with a chronic medical condition: a randomized controlled feasibility trial of a combined app and coaching program.

European child & adolescent psychiatry·2024
Same author

DaTSCAN® dilution with 0.9% NaCl - A stability evaluation.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2023
Same author

Canalization and plasticity in psychopathology.

Neuropharmacology·2022
Same author

An unusual presentation of dysarthria in a young patient, a stroke mimic.

Acute medicine·2021

This study introduces a population density approach to model evoked response potentials (ERPs), offering a biologically plausible framework for analyzing neuronal population dynamics and estimating parameters from electrophysiological data.

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Electrophysiology

Background:

  • Cortical activity arises from neuronal population interactions, generating macroscopic electrophysiological phenomena.
  • Evoked response potentials (ERPs) reflect average electrical activity measured on the scalp in response to stimuli.
  • Existing models often simplify neuronal behavior, limiting the incorporation of detailed biological mechanisms.

Purpose of the Study:

  • To outline a population density approach for modeling evoked response potentials (ERPs).
  • To propose a biologically plausible model of neuronal activity for estimating physiologically meaningful parameters from electrophysiological data.
  • To develop a framework that accounts for neuronal dynamics, stochastic forces, population organization, and network interactions.

Main Methods:

Related Experiment Videos

  • Formulated population dynamics using the Fokker-Planck equation to describe the temporal evolution of probability density over neuronal states.
  • Developed a population model incorporating biologically informed model-neurons with spike-rate adaptation and synaptic dynamics.
  • Approximated time-dependent solutions using bi-orthogonal sets and first-order perturbation expansion, extending from deterministic to stochastic effects.

Main Results:

  • Demonstrated the model's ability to estimate parameters from synthetic data using a Bayesian estimation scheme.
  • Showcased the utility of modeling density dynamics, which captures interactions among moments of neuronal states, unlike conventional neural mass models.
  • Provided a method for inferring coupling among neuronal sub-populations from electrophysiological data.

Conclusions:

  • The population density approach offers a powerful framework for modeling ERPs by capturing the collective behavior of neuronal populations.
  • This method enables the estimation of biologically relevant parameters, advancing our understanding of neural network function.
  • The proposed model provides a more comprehensive representation of neuronal dynamics compared to traditional approaches, paving the way for more accurate electrophysiological data analysis.