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

Neural Circuits01:25

Neural Circuits

3.0K
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...
3.0K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

590
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
590
Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

1.9K
NMR-active nuclei have energy levels called 'spin states' that are associated with the orientations of their nuclear magnetic moments. In the absence of a magnetic field, the nuclear magnetic moments are randomly oriented, and the spin states are degenerate. When an external magnetic field is applied, the spin states have only 2 + 1 orientations available to them. A proton with = ½ has two available orientations. Similarly, for a quadrupolar nucleus with a nuclear spin value of one, the...
1.9K
Storage01:23

Storage

502
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...
502
Propagation of Action Potentials01:23

Propagation of Action Potentials

15.2K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
15.2K
System of Memory01:23

System of Memory

9.0K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
9.0K

You might also read

Related Articles

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

Sort by
Same author

DLCNN: A Deep Logic Convolutional Network for Interpretable Fault Diagnosis of Hoist Mechanism on Ship-to-Shore Cranes.

IEEE transactions on neural networks and learning systems·2025
Same author

sigRGCN: A Robust Residual Graph Convolutional Network for scRNA-Seq Data Clustering.

IEEE transactions on computational biology and bioinformatics·2025
Same author

Using artifact suppression OCT based on polarization multiplexing for tomographic imaging of LCD screens.

Optics express·2025
Same author

Identification and validation of tricarboxylic acid cycle-related diagnostic biomarkers for diabetic nephropathy via weighted gene co-expression network analysis and single-cell transcriptome analysis.

Acta diabetologica·2025
Same author

Innovative sustained-release Ferrate(VI) composites for controlling algal derived disinfection by-products in waters.

Environmental research·2025
Same author

A fibrin gel-loaded Gouqi-derived nanovesicle (GqDNV) repairs the heart after myocardial infarction by inhibiting p38 MAPK/NF-κB p65 pathway.

Journal of nanobiotechnology·2025
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: Apr 25, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

885

Sequence memory based on coherent spin-interaction neural networks.

Min Xia1, W K Wong, Zhijie Wang

  • 1College of Information and Control Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China, and Institute of Textiles and Clothing, Hong Kong Polytechnic University, 999077, Hong Kong xiamin_wh@hotmail.com.

Neural Computation
|August 24, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new neural network model for sequence memory using coherent spin interaction. This model allows for a controllable steady-state period and increased storage capacity, overcoming limitations of existing models.

More Related Videos

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

6.1K
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.1K

Related Experiment Videos

Last Updated: Apr 25, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

885
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

6.1K
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.1K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Sequence memory is crucial for brain functions, but current models lack control over the steady-state period during recall.
  • Existing heteroassociation-based sequence memory models cannot alter the steady-state period, limiting their flexibility.

Purpose of the Study:

  • To propose a novel neural network model for sequence memory with a controllable steady-state period.
  • To enhance sequence storage capacity and pattern differentiation using coherent spin interaction.

Main Methods:

  • Developed a neural network model incorporating coherent spin interaction.
  • Simulated the model's performance in sequence memory tasks.
  • Investigated the influence of dimension parameters and pattern overlap on the steady-state period.

Main Results:

  • The proposed model successfully demonstrates a controllable steady-state period in sequence recall.
  • Coherent spin interaction enables differential responses of neuron groups to patterns.
  • Sequence storage capacity is significantly enlarged compared to existing models.

Conclusions:

  • The novel coherent spin-interaction model offers enhanced control over the steady-state period in sequence memory.
  • This approach expands sequence storage capacity, showing an exponential relationship with network dimension.
  • The model provides a more flexible and scalable framework for sequence information processing.