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 Concept Videos

Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...

You might also read

Related Articles

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

Sort by
Same author

A deformable attractor manifold organizes human resting-state brain dynamics.

bioRxiv : the preprint server for biology·2026
Same author

Modelling low-dimensional interacting brain networks reveals organising principle in human cognition.

Network neuroscience (Cambridge, Mass.)·2025
Same author

Mapping global brain reconfigurations following local targeted manipulations.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Virtual epilepsy patient cohort: Generation and evaluation.

PLoS computational biology·2025
Same author

Synchronization in spiking neural networks with short and long connections and time delays.

Chaos (Woodbury, N.Y.)·2025
Same author

Inference on the Macroscopic Dynamics of Spiking Neurons.

Neural computation·2024

Related Experiment Video

Updated: Jun 9, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Neural population modes capture biologically realistic large scale network dynamics.

Viktor K Jirsa1, Roxana A Stefanescu

  • 1Theoretical Neuroscience Group, Institute Sciences de Mouvement, UMR6233 CNRS, Marseille, France. viktor.jirsa@univmed.fr

Bulletin of Mathematical Biology
|September 8, 2010
PubMed
Summary

This study introduces a neural population model to simplify complex brain network dynamics. The model efficiently captures high-dimensional neural activity, aiding cognitive function research.

More Related Videos

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
10:18

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

Related Experiment Videos

Last Updated: Jun 9, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
10:18

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

Area of Science:

  • Computational neuroscience
  • Systems neuroscience
  • Cognitive neuroscience

Background:

  • Large-scale brain networks underpin cognitive functions, but their mechanisms remain unclear.
  • Studying these networks is challenging due to high dimensionality, complex connectivity, transmission delays, and neuronal stochasticity.
  • Computational effort can be reduced by clustering neurons into neural masses and using reduced population dynamics.

Purpose of the Study:

  • To implement a parsimonious neural population model for capturing diverse population behaviors.
  • To demonstrate the efficacy of this reduced model in simulating complex neural network dynamics.
  • To address the computational challenges in studying large-scale brain networks.

Main Methods:

  • Implementation of a neural population mode approach.
  • Numerical simulation of neural networks with homogeneous local connectivity and large-scale, fiber-like connections with time delay.
  • Utilizing reduced descriptions of population dynamics to approximate clustered neurons.

Main Results:

  • The implemented neural population mode system effectively captures high-dimensional dynamics.
  • The model successfully simulates networks with specific connectivity architectures and time delays.
  • The approach offers a computationally favorable method for studying neural population behavior.

Conclusions:

  • Reduced neural population models can parsimoniously capture complex brain network dynamics.
  • This approach offers a computationally efficient alternative for investigating large-scale neural systems.
  • The findings contribute to understanding the neural basis of cognitive functions.