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

Synaptic Signaling01:12

Synaptic Signaling

79.2K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
79.2K
Synaptic Signaling01:09

Synaptic Signaling

6.6K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
6.6K
What is Cell Signaling?02:03

What is Cell Signaling?

129.9K
Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate to respond to the environment.
129.9K
Second-Order Circuits01:17

Second-Order Circuits

3.3K
Integrating two fundamental energy storage elements in electrical circuits results in second-order circuits, encompassing RLC circuits and circuits with dual capacitors or inductors (RC and RL circuits). Second-order circuits are identified by second-order differential equations that link input and output signals.
Input signals typically originate from voltage or current sources, with the output often representing voltage across the capacitor and/or current through the inductor. For example, in...
3.3K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.5K
First-Order Circuits01:15

First-Order Circuits

3.3K
First-order electrical circuits, which comprise resistors and a single energy storage element - either a capacitor or an inductor, are fundamental to many electronic systems. These circuits are governed by a first-order differential equation that describes the relationship between input and output signals.
One common example of a first-order circuit is the RC (resistor-capacitor) circuit. These circuits are used in relaxation oscillators such as neon lamp oscillator circuits. When voltage is...
3.3K

You might also read

Related Articles

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

Sort by
Same author

The clinical characteristics and prognosis in adult Ph negative acute lymphoblastic leukemia with TP53 aberrations.

Experimental hematology & oncology·2022
Same author

A Method to Reduce off-Targets in CRISPR/Cas9 System in Plants.

Methods in molecular biology (Clifton, N.J.)·2022
Same author

Mercury can be transported into marine copepod by polystyrene nanoplastics but is not bioaccumulated: An increased risk?

Environmental pollution (Barking, Essex : 1987)·2022
Same author

Electrochemical data mining: from information to knowledge: general discussion.

Faraday discussions·2022
Same author

Advanced nanoelectrochemistry implementation: from concept to application: general discussion.

Faraday discussions·2022
Same author

Comparison of multi-omics results between patients with acute myeloid leukemia with long-term survival and healthy controls.

Annals of translational medicine·2022
Same journal

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026
Same journal

Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.

Frontiers in computational neuroscience·2026
Same journal

New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.

Frontiers in computational neuroscience·2026
Same journal

NMDA receptor kinetics drive distinct routes to chaotic firing in pyramidal neurons.

Frontiers in computational neuroscience·2026
Same journal

Schumann-anchored golden ratio organization of human neural oscillations.

Frontiers in computational neuroscience·2026
Same journal

Toward model-guided electrophysiology-Encoding of chirps in the electrosensory periphery of <i>Apteronotus leptorhynchus</i>.

Frontiers in computational neuroscience·2026
See all related articles

Related Experiment Video

Updated: Jan 22, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

942

A Temporal Signal-Processing Circuit Based on Spiking Neuron and Synaptic Learning.

Hui Wei1, Yi-Fan Du1

  • 1Laboratory of Cognitive Model and Algorithm, Shanghai Key Laboratory of Data Science, Department of Computer Science, Fudan University, Shanghai, China.

Frontiers in Computational Neuroscience
|July 19, 2019
PubMed
Summary
This summary is machine-generated.

This study models temporal processing in the brain using spiking neurons. The new model successfully reproduces synchronization-continuation tapping task results, offering insights into neural circuits for time perception.

Keywords:
SCTramp activityspiking-neuronsynaptic learningtime-processing circuittime-related neuron

More Related Videos

In Vitro Wedge Slice Preparation for Mimicking In Vivo Neuronal Circuit Connectivity
10:31

In Vitro Wedge Slice Preparation for Mimicking In Vivo Neuronal Circuit Connectivity

Published on: August 18, 2020

6.0K
Vibrodissociation of Neurons from Rodent Brain Slices to Study Synaptic Transmission and Image Presynaptic Terminals
08:38

Vibrodissociation of Neurons from Rodent Brain Slices to Study Synaptic Transmission and Image Presynaptic Terminals

Published on: May 25, 2011

16.0K

Related Experiment Videos

Last Updated: Jan 22, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

942
In Vitro Wedge Slice Preparation for Mimicking In Vivo Neuronal Circuit Connectivity
10:31

In Vitro Wedge Slice Preparation for Mimicking In Vivo Neuronal Circuit Connectivity

Published on: August 18, 2020

6.0K
Vibrodissociation of Neurons from Rodent Brain Slices to Study Synaptic Transmission and Image Presynaptic Terminals
08:38

Vibrodissociation of Neurons from Rodent Brain Slices to Study Synaptic Transmission and Image Presynaptic Terminals

Published on: May 25, 2011

16.0K

Area of Science:

  • Neuroscience
  • Computational Neuroscience

Background:

  • The brain's mechanism for processing temporal information is not fully understood.
  • Previous research suggests medial premotor cortex (MPC) utilizes four ramp cell populations for time-keeping.

Purpose of the Study:

  • To develop a computational model of temporal processing in the MPC.
  • To investigate the role of specific cell populations in neural time perception.

Main Methods:

  • Constructed a spiking neuron model incorporating four ramp cell populations.
  • Integrated the time-adaptive drift-diffusion model (TDDM) with impulse transmission.
  • Used the model to simulate the synchronization-continuation tapping task (SCT).

Main Results:

  • The model successfully reproduced results from the synchronization-continuation tapping task (SCT).
  • Simulated neurons exhibited firing properties consistent with experimentally observed time-related neurons.

Conclusions:

  • The proposed model reflects physiological aspects of neural circuits involved in temporal perception.
  • The model provides a framework for understanding how the brain processes time.