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

Long-term Potentiation01:35

Long-term Potentiation

56.0K
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.
56.0K
Cognitive Learning01:21

Cognitive Learning

686
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
686
Graded Potential01:19

Graded Potential

5.1K
Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
5.1K
Associative Learning01:27

Associative Learning

630
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...
630
Observational Learning01:12

Observational Learning

349
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...
349
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
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...
1.9K

You might also read

Related Articles

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

Sort by
Same author

1D-(GaN/AlN)/2D-Gr/3D-(SiO<sub>2</sub>/Si) Combined High-Performance Flash Memory Device.

ACS applied materials & interfaces·2025
Same author

High current density heterojunction bipolar transistors with 3D-GaN/2D-WSe<sub>2</sub> as emitter junctions.

Materials horizons·2025
Same author

Low Barriers and Faster Electron/Ion Transport Rates through the Ga<sub>2</sub>O<sub>3</sub>/MnCO<sub>3</sub> Anode with a Heterojunction Structure for Lithium-Ion Batteries.

Langmuir : the ACS journal of surfaces and colloids·2024
Same author

Synergistic Ion-Anchoring Passivation for Perovskite Solar Cells with Efficiency Exceeding 24% and Ultra-Ambient Stability.

ACS applied materials & interfaces·2023
Same author

Diagnosis of Metastatic Lymph Nodes in Patients With Hepatocellular Carcinoma Using Dual-Energy Computed Tomography.

Journal of computer assisted tomography·2023
Same author

GaN/Gr (2D)/Si (3D) Combined High-Performance Hot Electron Transistors.

ACS nano·2023
Same journal

Scalable batch-type synthesis of layered 2D-SnS<sub>2</sub> transistors integration enabled by BEOL-compatible low-thermal budget processes.

Nanoscale·2026
Same journal

Self-powered and gate-reconfigurable photodetection and logic operations in the Ta<sub>2</sub>PdS<sub>6</sub>/WSe<sub>2</sub> van der Waals heterostructure.

Nanoscale·2026
Same journal

Elucidating interfacial charge extraction from CdTe@ZnS quantum dots by pyridinium ionic liquids.

Nanoscale·2026
Same journal

Tailoring charged nanochannels in covalent organic framework membranes for efficient lithium recovery.

Nanoscale·2026
Same journal

Restoring the powerhouse: mitochondrial transplantation in regenerative medicine and cancer therapy.

Nanoscale·2026
Same journal

Enhanced circular dichroism of molecular J-aggregate-surface plasmon polariton hybrid modes.

Nanoscale·2026
See all related articles

Related Experiment Video

Updated: Sep 28, 2025

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

7.2K

Correction: 'Stateful' threshold switching for neuromorphic learning.

Zhijian Zhong1, Zhiguo Jiang1, Jianning Huang1

  • 1Guangdong Engineering Research Center of Optoelectronic Functional Materials and Devices, Institute of Semiconductors, South China Normal University, Guangzhou 510631, PR China. gaofl@m.scnu.edu.cn.

Nanoscale
|March 28, 2022
PubMed
Summary
This summary is machine-generated.

This correction addresses a previous publication on stateful threshold switching for neuromorphic learning. It clarifies details regarding the implementation and performance of the described neuromorphic devices.

More Related Videos

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

8.0K
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.6K

Related Experiment Videos

Last Updated: Sep 28, 2025

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

7.2K
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

8.0K
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.6K

Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Context:

  • Neuromorphic computing aims to mimic the human brain's structure and function.
  • Stateful threshold switching is a key mechanism for implementing artificial neurons.
  • Accurate device characterization is crucial for advancing neuromorphic hardware.

Purpose:

  • To correct inaccuracies in the original publication regarding stateful threshold switching.
  • To provide precise details on device operation and experimental results.
  • To ensure the scientific record accurately reflects the capabilities for neuromorphic learning.

Summary:

  • The correction clarifies the behavior of 'stateful' threshold switching devices used in neuromorphic applications.
  • It provides revised information on the switching characteristics and endurance of the memristive devices.
  • The updated details ensure a more accurate understanding of their potential for artificial intelligence hardware.

Impact:

  • Ensures the integrity of research in neuromorphic engineering and artificial intelligence.
  • Facilitates accurate replication and further development of stateful switching devices.
  • Improves the reliability of data used for designing next-generation AI hardware.