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

3.0K
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...
3.0K
Neuronal Communication01:28

Neuronal Communication

5.5K
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...
5.5K
Neurons: The Axon01:21

Neurons: The Axon

13.5K
Axons are long, cytoplasmic processes of nerve cells capable of propagating electrical impulses known as action potentials. The cytoplasm or axoplasm of an axon contains neurofibrils, neurotubules, small vesicles, lysosomes, mitochondria, and various enzymes, all encased within the axolemma, the plasma membrane of the axon.
The axon attaches to the cell body at a cone-shaped elevation called the axon hillock. The initial part of the axon, closest to the hillock, is known as the initial segment....
13.5K
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

1.0K
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
1.0K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.2K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.2K
The Synapse02:47

The Synapse

99.9K
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.
99.9K

You might also read

Related Articles

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

Sort by
Same author

Biliary Abnormality on Imaging During Lenvatinib Plus Hepatic Arterial Infusion Chemotherapy With Cisplatin for Hepatocellular Carcinoma: A Pilot Descriptive Study.

Hepatology research : the official journal of the Japan Society of Hepatology·2026
Same author

Subcutaneous Emphysema and Postoperative Complications in Robot-Assisted Gastrectomy: Impact of a Lower-Pressure Insufflation Strategy.

Asian journal of endoscopic surgery·2026
Same author

A novel ergodic sequential logic cochlear model: reproductions of multiple nonlinear sound processing functions of mammalian cochlea and efficient FPGA implementation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

A novel piece-wise constant pancreatic β-cell cluster model: reproductions of various spiking phenomena of pancreatic β-cell cluster and efficient FPGA implementation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

A novel ergodic sequential logic olfactory bulb model towards hardware-efficient electronic nose.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Case of simultaneous occurrence of hepatitis, cholangitis, and pancreatitis as immune-related adverse events induced by immune checkpoint inhibitor therapy: a case report.

Abdominal radiology (New York)·2025
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Apr 30, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

11.5K

Asynchronous cellular automaton-based neuron: theoretical analysis and on-FPGA learning.

Takashi Matsubara, Hiroyuki Torikai

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a novel theoretical analysis and learning algorithm for asynchronous cellular automaton neuron models. These advancements allow FPGA-implemented models to replicate complex nonlinear dynamics found in biological neurons.

    More Related Videos

    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

    11.1K
    Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
    10:50

    Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

    Published on: June 21, 2022

    2.3K

    Related Experiment Videos

    Last Updated: Apr 30, 2026

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    11.5K
    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

    11.1K
    Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
    10:50

    Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

    Published on: June 21, 2022

    2.3K

    Area of Science:

    • Computational Neuroscience
    • Artificial Intelligence
    • Digital Circuit Design

    Background:

    • Cellular automaton models offer a framework for simulating neuronal nonlinear dynamics.
    • Field-programmable gate arrays (FPGAs) provide a hardware implementation platform for complex computational models.

    Purpose of the Study:

    • To develop a novel theoretical analysis method for generalized asynchronous cellular automaton-based neuron models.
    • To introduce a new learning algorithm for these models.
    • To demonstrate the ability of FPGA-implemented models to reproduce biological neuron behaviors.

    Main Methods:

    • Theoretical analysis of neuron-like orbits and bifurcations.
    • Development of a novel learning algorithm for the automaton model.
    • Implementation of the model and learning algorithm on an FPGA device.

    Main Results:

    • Theoretical clarification of the stability of neuron-like orbits and mechanisms of neuron-like bifurcations.
    • Successful implementation of a learning algorithm on an FPGA.
    • Demonstration that the FPGA-implemented model can automatically reproduce typical nonlinear responses and occurrence mechanisms of biological and model neurons.

    Conclusions:

    • The novel theoretical analysis method effectively clarifies the model's dynamics.
    • The FPGA-implemented learning algorithm enables adaptive behavior in the neuron model.
    • This work bridges computational neuroscience and hardware implementation for advanced neuron modeling.