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

Observational Learning01:12

Observational Learning

600
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
600
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

357
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
357
Propagation of Action Potentials01:23

Propagation of Action Potentials

7.9K
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...
7.9K
Neural Circuits01:25

Neural Circuits

2.1K
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...
2.1K
Long-term Potentiation01:35

Long-term Potentiation

57.0K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
57.0K
Associative Learning01:27

Associative Learning

831
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
831

You might also read

Related Articles

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

Sort by
Same author

Self-Organized Neural Integrators in Noisy Spiking Networks.

bioRxiv : the preprint server for biology·2026
Same author

PKMζ-KIBRA interactions, molecular turnover, and memory.

bioRxiv : the preprint server for biology·2026
Same author

Maintenance of memory by negative feedback of synaptic protein elimination: modeling KIBRA-PKMζ dynamics in LTP.

Learning & memory (Cold Spring Harbor, N.Y.)·2025
Same author

Maintenance of memory by negative-feedback of synaptic protein elimination: Modeling KIBRA- <math><mi>P</mi> <mi>K</mi> <mi>M</mi> <mi>ζ</mi></math> dynamics in LTP.

bioRxiv : the preprint server for biology·2024
Same author

Learning to express reward prediction error-like dopaminergic activity requires plastic representations of time.

Nature communications·2024
Same author

KIBRA anchoring the action of PKMζ maintains the persistence of memory.

Science advances·2024

Related Experiment Video

Updated: Nov 12, 2025

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

10.5K

Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network.

Ian Cone1,2, Harel Z Shouval1

  • 1Neurobiology and Anatomy, University of Texas Medical School at Houston, Houston, TX, United States.

Elife
|March 18, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a spiking neural network model for learning and recalling temporal sequences. The model, using biologically plausible learning rules, can remember and reproduce sequences after training.

Keywords:
neurosciencenonereinforcement learningsequencessystems modeling

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

11.8K
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.3K

Related Experiment Videos

Last Updated: Nov 12, 2025

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

10.5K
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

11.8K
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.3K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Temporal sequence learning is crucial for memory and cognition.
  • Existing models often lack biological plausibility or struggle with variable sequences.

Purpose of the Study:

  • To propose a novel network model for biologically plausible learning and recall of discrete temporal sequences.
  • To investigate the model's capacity for variable order, duration, and non-Markovian sequences.

Main Methods:

  • Developed a spiking neural network with a modular microcolumnar architecture.
  • Employed a biophysically realistic learning rule incorporating synaptic eligibility traces.
  • Tested the network's ability to learn and recall sequences after training.

Main Results:

  • The trained network successfully recalled entire sequences upon presentation of the first element.
  • An extended model demonstrated learning and recall of non-Markovian sequences.
  • The model exhibited biologically plausible sequence representation and recall.

Conclusions:

  • The proposed spiking neural network offers a viable framework for understanding biological sequence learning and memory.
  • The model aligns with recent experimental findings on neural sequence processing.
  • This work contributes to computational models of learning and memory.