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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

LEGEND: Lorentzian electro-modal graph encoder for neural decoding for SCI rehabilitation.

Computers in biology and medicine·2026
Same author

Visualizing and Quantifying microRNA-Induced DNA Origami Separation at the Nanoscale.

Angewandte Chemie (International ed. in English)·2026
Same author

Maximizing Lithium Recovery in Direct Recycling: Relithiation of Layered Oxide Cathodes Using Residual Lithium.

ACS applied materials & interfaces·2026
Same author

Activating High-Loading Cathodes Using Percolative Graphite in Rechargeable Alkaline Zn-MnO<sub>2</sub> Batteries.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Thyroid hormone levels among adults in India: A cross-sectional study.

Bioinformation·2025
Same author

Mutations in histone lysine methyltransferase genes are associated with autoimmune cytopenias: a single-center study.

Blood vessels, thrombosis & hemostasis·2025
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

Related Experiment Video

Updated: May 31, 2026

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

Neuronal model with distributed delay: analysis and simulation study for gamma distribution memory kernel.

Karmeshu1, Varun Gupta, K V Kadambari

  • 1Jawaharlal Nehru University, New Delhi, 110067, India. karmeshu@mail.jnu.ac.in

Biological Cybernetics
|June 25, 2011
PubMed
Summary
This summary is machine-generated.

This study proposes a novel neuronal model with distributed delay, analyzing its behavior using stochastic integro-differential equations. The model reveals how memory influences neuronal firing patterns and can switch between bursting and non-bursting states.

More Related Videos

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Modeling Neuronal Death and Degeneration in Mouse Primary Cerebellar Granule Neurons
10:36

Modeling Neuronal Death and Degeneration in Mouse Primary Cerebellar Granule Neurons

Published on: November 6, 2017

Related Experiment Videos

Last Updated: May 31, 2026

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Modeling Neuronal Death and Degeneration in Mouse Primary Cerebellar Granule Neurons
10:36

Modeling Neuronal Death and Degeneration in Mouse Primary Cerebellar Granule Neurons

Published on: November 6, 2017

Area of Science:

  • Computational Neuroscience
  • Mathematical Biology
  • Stochastic Processes

Background:

  • Neuronal models often simplify or omit the role of distributed delays (memory).
  • Understanding the impact of memory on neuronal dynamics is crucial for explaining complex firing patterns.
  • Existing models like the Leaky Integrate-and-Fire (LIF) model may not fully capture memory effects.

Purpose of the Study:

  • To propose and analyze a single neuronal model that incorporates distributed delay.
  • To investigate the non-Markovian nature of the model and its transformation into a Markovian system.
  • To study the effect of weak and strong delay kernels on neuronal firing statistics and dynamics.

Main Methods:

  • Formulation of a stochastic model as a Stochastic Integro-Differential Equation (SIDE).
  • Transformation of the non-Markovian SIDE into a Markovian model in an extended state space for exponential delay kernels.
  • Conversion to coupled Stochastic Differential Equations (SDEs) for First Passage Time (FPT) analysis.
  • Simulation studies to analyze Inter-Spike Interval (ISI) distributions and Coefficient of Variation (CV).
  • Analysis of membrane potential auto-correlation and power spectral density.
  • Examination of models with strong delay kernels (Gamma distribution).

Main Results:

  • The model demonstrates that distributed delay significantly affects ISI distribution and neuronal firing.
  • A Jensen-Shannon divergence measure is proposed for model comparison.
  • Neuronal behavior can transition between bursting and non-bursting states based on noise intensity and memory kernel parameters.
  • Membrane potential exhibits decaying auto-correlation, with or without damped oscillations, matching empirical observations.
  • Power spectral density shows single or double peaks.
  • Strong delay kernels lead to slower decay of damped oscillations in ISI compared to weak delay kernels.

Conclusions:

  • The proposed distributed delay neuronal model provides a more comprehensive framework for understanding neuronal dynamics.
  • The model captures essential features of neuronal firing, including state transitions and auto-correlation patterns.
  • The distinction between weak and strong delay effects offers insights into different regimes of neuronal memory.
  • The developed analytical and simulation tools are valuable for analyzing complex neuronal systems.