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...
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...
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...
Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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

Optimum rotation-invariant filter for disjoint-noise scenes.

Applied optics·2010
Same author

Phase calibration and applications of a liquid-crystal spatial light modulator.

Applied optics·2010
Same author

Single-rail translation-invariant optical associative memory.

Applied optics·2010
Same author

Invariant optical-pattern recognition based on a contour bank.

Applied optics·2010
Same author

Optoelectronic thresholding module for winner-take-all operations in optical neural networks.

Applied optics·2010
Same author

Optical binary phase-only filters for circular harmonic correlations.

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 Videos

Optical associative memory model based on neural networks having variable interconnection weights.

B Macukow, H H Arsenault

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

    A novel neural network model enhances content-addressable memory (CAM) capabilities beyond the Hopfield model. This advanced CAM stores more information per element, leading to superior performance in neural network memory systems.

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Neuroscience
    • Computer Engineering

    Background:

    • Content-addressable memory (CAM) is crucial for efficient information retrieval.
    • The Hopfield model provides a foundational neural network approach to CAM.
    • Limitations exist in the storage capacity and performance of existing neural network CAM models.

    Purpose of the Study:

    • To propose a new neural network model for content-addressable memory (CAM).
    • To improve upon the storage capacity and retrieval accuracy of the Hopfield model.
    • To explore the impact of interneuron layers and historical weight dependence on CAM performance.

    Main Methods:

    • Development of a novel neural network architecture for CAM.
    • Incorporation of intermediate interneuron layers.
    • Implementation of a neuron interconnection weight dependence on neuron history.
    • Modification of the storage prescription to allow ternary values (three states) per matrix element.

    Main Results:

    • The proposed model demonstrates improved performance compared to the traditional Hopfield model.
    • The enhanced storage prescription allows for denser information encoding.
    • The inclusion of interneurons and historical dependence contributes to better recall accuracy.

    Conclusions:

    • The new neural network CAM model offers significant advantages over the Hopfield model.
    • This advanced CAM architecture is promising for applications requiring high-density information storage and retrieval.
    • Further research can explore optical implementations and scalability of this model.