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

Neural Circuits01:25

Neural Circuits

3.3K
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...
3.3K
Neuronal Communication01:28

Neuronal Communication

5.3K
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...
5.3K
Propagation of Action Potentials01:23

Propagation of Action Potentials

14.9K
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...
14.9K
Overview of Synapses01:25

Overview of Synapses

10.8K
A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...
10.8K
Classification of Systems-I01:26

Classification of Systems-I

684
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
684
The Synapse02:47

The Synapse

138.5K
Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
138.5K

You might also read

Related Articles

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

Sort by
Same author

Emergent order in adaptively rewired networks.

Chaos (Woodbury, N.Y.)·2024
Same author

Robustness of the emergence of synchronized clusters in branching hierarchical systems under parametric noise.

Chaos (Woodbury, N.Y.)·2024
Same author

Impact of coupling on neuronal extreme events: Mitigation and enhancement.

Chaos (Woodbury, N.Y.)·2023
Same author

Neuronal diversity can improve machine learning for physics and beyond.

Scientific reports·2023
Same author

Threshold-activated transport stabilizes chaotic populations to steady states.

PloS one·2017
Same journal

Multiscale dynamics of special memristive ion channels in a neural circuit.

Chaos (Woodbury, N.Y.)·2026
Same journal

Symmetry-protected delay spectroscopy in oscillator networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Mesoscale community organization governs epidemic onset and spread in metapopulations.

Chaos (Woodbury, N.Y.)·2026
Same journal

Topological dependence of viral mutation spread in complex host-interaction networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Multifractal signatures of Hamiltonian chaos in Hyperion's rotational dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Apr 9, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

12.2K

Asynchronicity yields regularity in coupled neuronal systems.

Anupama Roy1, Sudeshna Sinha1

  • 1Indian Institute of Science Education and Research Mohali, Manauli, P.O. 140306, India.

Chaos (Woodbury, N.Y.)
|April 8, 2026
PubMed
Summary
This summary is machine-generated.

Asynchronicity in complex systems can suppress neuronal oscillations. Stronger asynchronicity leads to complete cessation of neuronal activity in coupled chaotic neurons, even with strong coupling.

More Related Videos

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes
10:10

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Published on: October 4, 2018

9.4K
Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

10.4K

Related Experiment Videos

Last Updated: Apr 9, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

12.2K
Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes
10:10

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Published on: October 4, 2018

9.4K
Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

10.4K

Area of Science:

  • Computational Neuroscience
  • Complex Systems Dynamics
  • Nonlinear Dynamics

Background:

  • Complex interactive systems often exhibit asynchronous behavior.
  • Understanding the impact of asynchronicity on neuronal dynamics is crucial for neuroscience.
  • Neuronal oscillations are fundamental to brain function.

Purpose of the Study:

  • To investigate the influence of asynchronicity on spatiotemporal dynamics in coupled chaotic neurons.
  • To determine how varying degrees of asynchronicity affect neuronal activity and oscillations.
  • To provide a theoretical and numerical understanding of asynchronicity's role in neuronal systems.

Main Methods:

  • Analysis of spatiotemporal dynamics in a system of coupled chaotic neurons.
  • Numerical simulations of neuronal activity under synchronous, sequential, and asynchronous updates.
  • Stability analysis for a two-coupled neuron system.
  • Time-averaged framework for analyzing large systems.

Main Results:

  • Asynchronicity suppresses neuronal oscillations under strong coupling.
  • Complete cessation of neuronal activity occurs with strong asynchronicity across a wide coupling range.
  • Weaker asynchronicity quenches neuronal activity over a smaller coupling range.
  • Stability analysis aligns with numerical simulations, explaining asynchronicity's effects.
  • Time-averaged analysis yields critical coupling values close to simulations.

Conclusions:

  • Asynchronicity is a key factor in controlling neuronal activity and oscillations in coupled chaotic systems.
  • The degree of asynchronicity dictates the extent of neuronal activity suppression.
  • Mathematical analysis and numerical simulations consistently demonstrate the impact of asynchronicity on neuronal dynamics.