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

MOS Capacitor01:25

MOS Capacitor

A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
Associative Learning01:27

Associative Learning

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...
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
Understanding Memory01:19

Understanding Memory

Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
MOSFET: Enhancement Mode01:22

MOSFET: Enhancement Mode

Enhancement-mode MOSFETs are pivotal components in electronics, distinguished by their capacity to act as highly efficient switches. They are part of the larger family of metal-oxide Semiconductor Field-Effect Transistors (MOSFETs). They are available in two types: p-channel and n-channel, each tailored to specific polarity operations.
In their basic form, enhancement-mode MOSFETs are typically non-conductive when the gate-source voltage (Vgs) is zero. This default 'off' state means no current...
Cognitive Learning01:21

Cognitive Learning

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...

You might also read

Related Articles

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

Sort by
Same author

Transient Large-Scale Anisotropy in TeV Cosmic Rays due to an Interplanetary Coronal Mass Ejection.

Physical review letters·2026
Same author

[Examining Wang Jiufeng and his works].

Zhonghua yi shi za zhi (Beijing, China : 1980)·2026
Same author

[Tuberous sclerosis complex-associated multifocal micronodular pneumocyte hyperplasia: a clinicopathological features and TSC1/TSC2 gene mutation analysis of eight cases].

Zhonghua bing li xue za zhi = Chinese journal of pathology·2026
Same author

First Detection of Ultrahigh Energy Emission from Gamma-Ray Binary LS I +61° 303.

Physical review letters·2026
Same author

Evidence of Cosmic-Ray Acceleration up to Sub-PeV Energies in the Supernova Remnant IC 443.

Physical review letters·2026
Same author

[The interpretation of pathological content in the 2025 ERS/ATS international multidisciplinary classification of interstitial pneumonia].

Zhonghua bing li xue za zhi = Chinese journal of pathology·2026

Related Experiment Video

Updated: Jul 7, 2026

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

CMOS current-mode neural associative memory design with on-chip learning.

C Y Wu1, J F Lan

  • 1Dept. of Electron. Eng., Nat. Chiao Tung Univ., Hsinchu.

IEEE Transactions on Neural Networks
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel modular neural network based on the outstar model. This design enables on-chip learning and long-term memory storage for associative memory and pattern learning applications.

More Related Videos

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans
07:17

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans

Published on: June 23, 2022

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Related Experiment Videos

Last Updated: Jul 7, 2026

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

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans
07:17

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans

Published on: June 23, 2022

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Integrated Circuit Design

Background:

  • The Grossberg outstar model provides a foundational mathematical framework for associative memory and pattern learning.
  • Implementing neural network functionalities like on-chip learning and memory requires specialized circuit designs.
  • Classical conditioning and associative memory tasks are crucial for developing intelligent systems.

Purpose of the Study:

  • To design and analyze a modular neural network based on the outstar model with on-chip learning and memory.
  • To implement the outstar architecture using CMOS current-mode analog circuits.
  • To verify the functionality and performance of the fabricated circuits through simulation and measurement.

Main Methods:

  • Utilized the Grossberg outstar mathematical model as the basis for neural network design.
  • Developed CMOS current-mode analog dividers for ratio-type memory implementation.
  • Employed a CMOS current-mode analog multiplier for correlation implementation.
  • Verified circuit functions using HSPICE simulations and fabricated CMOS outstar circuits.

Main Results:

  • Successfully implemented an outstar circuit capable of on-chip storage of trained weight ratios.
  • Demonstrated long-term storage of ratio-type memory for up to five minutes without refreshment.
  • Confirmed the on-chip learning capability and associative memory functions of the fabricated circuits.
  • Showcased the outstar's ability to enhance stored pattern contrast over extended periods.

Conclusions:

  • The developed CMOS outstar circuits effectively realize on-chip learning and long-term memory storage.
  • The ratio-type memory and modularity of the outstar circuits make them highly feasible for various applications.
  • This work contributes to the advancement of practical neural network hardware for intelligent systems.