Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Associative Learning

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 Circuits

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...

Higher Mental Functions of Brain: Learning and Memory

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...

Storage

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...

Understanding Memory

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...

Implicit Memories

Implicit Memories

Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...

You might also read

Related Articles

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

Sort by

Same author

Region-of-interest histopathology with fine-tuned CNNs enables colorectal cancer grading and pathologic stage estimation.

Scientific reports·2026

Same author

AI system for diagnosing mucosa-associated lymphoid tissue lymphoma and diffuse large B cell lymphoma using ImageNet and hematoxylin and eosin-stained specimens.

PNAS nexus·2025

Same author

Biologging intelligent Platform (BiP): an integrated and standardized platform for sharing, visualizing, and analyzing biologging data.

Movement ecology·2025

Same author

Real-world evaluation of novel eye drop bottle sensors: Cloud-based AI support for eye drop adherence.

Heliyon·2024

Same author

Multi-scale dynamics by adjusting the leaking rate to enhance the performance of deep echo state networks.

Frontiers in artificial intelligence·2024

Same author

Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks.

Scientific reports·2024

Search research articles

Related Experiment Videos

Associative memory in quaternionic Hopfield neural network.

Teijiro Isokawa¹, Haruhiko Nishimura, Naotake Kamiura

¹Division of Computer Engineering, Graduate School of Engineering, University of Hyogo, Japan. isokawa@eng.u-hyogo.ac.jp

International Journal of Neural Systems

|May 3, 2008

Summary

This study explores quaternionic Hopfield neural networks for associative memory. Researchers found these networks can store up to 16 stable states, known as multiplet components, within their basins.

Related Experiment Videos

Area of Science:

Computational Neuroscience
Artificial Intelligence
Hypercomplex Systems

Background:

Associative memory networks are crucial for cognitive functions.
Hopfield neural networks are a foundational model for associative memory.
Quaternions, a type of hypercomplex number, offer unique mathematical properties.

Purpose of the Study:

To investigate associative memory networks utilizing quaternionic Hopfield neural networks.
To explore the representation of network components using quaternions.
To analyze the energy function and learning rules within these quaternionic networks.

Main Methods:

Development of a quaternionic Hopfield neural network model.
Introduction of an energy function for the network.
Application of the Hebbian rule for pattern embedding.
Analysis of stable states and their basins in small-scale networks (3 and 4 neurons).

Main Results:

Quaternionic neurons and weights were successfully implemented.
The energy function and Hebbian learning rule were defined for the quaternionic network.
Networks with three and four neurons were analyzed for stable states.
At most 16 stable states, termed multiplet components, were identified as degenerated stored patterns.

Conclusions:

Quaternionic Hopfield neural networks provide a framework for associative memory.
These networks exhibit distinct stable states (multiplet components) with associated basins.
The use of quaternions offers a novel approach to enhancing associative memory capabilities.