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

Introduction to Learning01:18

Introduction to Learning

471
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
471
Cognitive Learning01:21

Cognitive Learning

421
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...
421
Neural Circuits01:25

Neural Circuits

1.3K
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...
1.3K
Design Example01:23

Design Example

348
The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
348

You might also read

Related Articles

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

Sort by
Same author

Linking hydrocarbon gradients to microbial community variation across full-depth water columns in the Western Pacific Ocean.

Marine environmental research·2026
Same author

Ambroxol hydrochloride and clenbuterol hydrochloride oral solution versus ambroxol hydrochloride injection for pediatric lower respiratory tract infection with mucoid sputum: a multicenter, non-randomized observational study in China.

Translational pediatrics·2026
Same author

Fever and leukemoid reaction in bladder cancer: A case report.

Oncology letters·2026
Same author

Development of a Novel Interpretable Transformer-Based Deep Learning Model for Predicting Postoperative Hypokalemia in Pituitary Adenomas.

Journal of evidence-based medicine·2025
Same author

Ischemic Stroke as a Rare Manifestation of Neurobrucellosis: A Case Report.

Infection and drug resistance·2025
Same author

Proteomic analysis of B cells in peripheral lymphatic system reveals the dynamics during the systemic lupus erythematosus progression.

Biophysics reports·2025
Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026
Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026
Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026
Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 18, 2025

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
07:43

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios

Published on: August 4, 2023

2.0K

Deep Learning in the Ubiquitous Human-Computer Interactive 6G Era: Applications, Principles and Prospects.

Chunlei Chen1, Huixiang Zhang2, Jinkui Hou1

  • 1School of Computer Engineering, Weifang University, Weifang 261061, China.

Biomimetics (Basel, Switzerland)
|August 25, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning offers solutions for six key challenges in 6G systems, enabling human-centric intelligence. This review explores deep learning

Keywords:
6Gdeep learninghuman-centric

More Related Videos

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

638
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

795

Related Experiment Videos

Last Updated: Jul 18, 2025

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
07:43

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios

Published on: August 4, 2023

2.0K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

638
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

795

Area of Science:

  • Telecommunications Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • The advent of virtual reality (VR) and augmented reality (AR) heralds a new era of ubiquitous human-centric intelligence.
  • Sixth-generation (6G) wireless systems are crucial for seamless human-computer interaction in this future.
  • 6G promises significant performance enhancements over previous generations, enabling advanced applications.

Purpose of the Study:

  • To address the challenges hindering the development of 6G systems.
  • To explore the application of deep learning techniques to overcome these 6G challenges.
  • To provide a systematic review of deep learning solutions for 6G.

Main Methods:

  • Reviewing representative deep learning solutions for six key 6G challenges: Terahertz/millimeter-wave communication, low latency/high reliability, energy efficiency, security, edge computing, and service heterogeneity.
  • Analyzing the principles of applying deep learning to specific 6G issues.
  • Investigating the role of deep reinforcement learning and addressing data scarcity in 6G.

Main Results:

  • Deep learning provides effective alternatives to traditional analytical methods for complex 6G problems.
  • Deep reinforcement learning is identified as vital for 6G systems.
  • Solutions for training data scarcity and insights into the synergy between traditional methods and deep learning in 6G are presented.
  • Frequently used deep learning techniques for 6G are identified.

Conclusions:

  • Deep learning is essential for realizing the full potential of 6G human-centric applications.
  • Addressing challenges in communication, efficiency, security, and computing through AI is critical for 6G.
  • Future research should focus on open problems and advanced deep learning applications in 6G networks.