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

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...
Neural Circuits01:25

Neural Circuits

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...
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
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...
Storage01:23

Storage

A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...
Long-Term Memory01:18

Long-Term Memory

Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...

You might also read

Related Articles

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

Sort by
Same author

Limitations of a class of binary phase-only filters.

Applied optics·2010
Same author

Performance analysis of associative memories with nonlinearities in the correlation domain.

Applied optics·2010
Same author

Synchronous vs asynchronous behavior of Hopfield's CAM neural net.

Applied optics·2010
Same author

Composite matched filter output partitioning.

Applied optics·2010
Same author

Conventional and composite matched filters with error correction: a comparison.

Applied optics·2010
Same author

Effects of sampling on closed form bandlimited signal interval interpolation: erratum.

Applied optics·2010
Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026
Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026
Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026
Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026
Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026
Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Class of continuous level associative memory neural nets.

R J Marks Ii

    Applied Optics
    |May 11, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a neural network for reconstructing data vectors from memory, similar to Hopfield networks. It extrapolates missing vector parts, offering insights into computational efficiency and fault tolerance.

    Related Experiment Videos

    Last Updated: Jun 13, 2026

    Artificial Intelligence-Based System for Detecting Attention Levels in Students
    06:37

    Artificial Intelligence-Based System for Detecting Attention Levels in Students

    Published on: December 15, 2023

    Area of Science:

    • Artificial Intelligence
    • Computational Neuroscience
    • Machine Learning

    Background:

    • Content-addressable memory systems are crucial for information retrieval.
    • Hopfield neural networks provide a model for associative memory.
    • Restoring continuous-level vectors from partial information presents computational challenges.

    Purpose of the Study:

    • To introduce a novel neural network architecture for continuous-level vector restoration.
    • To explore the use of library vectors for programming neural interconnects.
    • To analyze the convergence, fault tolerance, and computational efficiency of the proposed network.

    Main Methods:

    • A neural network model is designed to store and retrieve continuous-level vectors.
    • The network's interconnects are programmed using vectors from a memory library.
    • Extrapolation of remaining vector segments from a given portion is performed.
    • Sufficient conditions for network convergence are mathematically derived.
    • The impact of processor inaccuracies and network faults is analyzed.

    Main Results:

    • The neural network demonstrates the capability to restore continuous-level library vectors from partial inputs.
    • Sufficient conditions for the network's convergence are established.
    • The study quantines the effects of processor inexactitude and network faults on performance.
    • A more computationally efficient extrapolation technique is developed, trading fault tolerance for speed.
    • The specific application to table lookup memories is examined.

    Conclusions:

    • The proposed neural network offers a viable method for continuous-level vector restoration from memory.
    • The trade-off between computational efficiency and fault tolerance in memory extrapolation is highlighted.
    • The findings have implications for designing robust and efficient content-addressable memory systems.