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

3.1K
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...
3.1K
Deductive Reasoning01:16

Deductive Reasoning

71.4K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
71.4K
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

2.2K
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...
2.2K
Inductive Reasoning00:59

Inductive Reasoning

69.0K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
69.0K
Observational Learning01:12

Observational Learning

1.1K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.1K
ER Retrieval Pathway01:45

ER Retrieval Pathway

5.0K
In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
5.0K

You might also read

Related Articles

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

Sort by
Same author

Usability of an augmented reality tele-mentoring system for medical specialists and junior doctors operating in rural and regional healthcare settings in Vietnam.

Rural and remote health·2026
Same author

Benchmarking deep learning models for laryngeal cancer staging using the LaryngealCT dataset.

Scientific reports·2026
Same author

Deep Learning-Based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell Morphology.

Ophthalmology science·2024
Same author

Estimating presymptomatic episodic memory impairment using simple hand movement tests: A cross-sectional study of a large sample of older adults.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2023
Same author

Real-time automated detection of older adults' hand gestures in home and clinical settings.

Neural computing & applications·2022
Same author

Differences in clinical manifestations of late-onset, compared to earlier-onset essential tremor: A scoping review.

Journal of the neurological sciences·2022

Related Experiment Video

Updated: Mar 12, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.8K

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

Son N Tran, Artur S d'Avila Garcez

    IEEE Transactions on Neural Networks and Learning Systems
    |November 16, 2016
    PubMed
    Summary

    This study introduces confidence rules for deep learning, enhancing modularity and knowledge insertion in neural networks. Layerwise extraction improves accuracy and understanding of learned representations.

    More Related Videos

    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    833
    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    2.0K

    Related Experiment Videos

    Last Updated: Mar 12, 2026

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    1.8K
    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    833
    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    2.0K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Deep learning utilizes layerwise unsupervised learning combined with supervised learning for fine-tuning.
    • Layerwise approaches enable deep networks to function as modular systems for representation learning.

    Purpose of the Study:

    • Investigate the utility of modularity for inserting background knowledge into deep networks.
    • Assess performance improvements with available background knowledge.
    • Explore knowledge extraction from trained deep networks for better representation understanding.

    Main Methods:

    • Utilized a symbolic language of confidence rules for quantitative reasoning in deep networks.
    • Applied knowledge extraction techniques to layerwise networks, including restricted Boltzmann machines.
    • Developed and evaluated a deep neural-symbolic system integrating confidence rules and knowledge insertion.

    Main Results:

    • Confidence rules provide a low-cost representation for layerwise networks.
    • Layerwise extraction demonstrated improved accuracy in deep belief networks.
    • Experimental results confirmed that modularity and knowledge insertion benefit network performance.

    Conclusions:

    • Modularity through confidence rules facilitates effective knowledge insertion and extraction in deep networks.
    • The proposed deep neural-symbolic system enhances understanding and performance of learned representations.
    • Confidence rules offer a novel method for symbolic characterization and prior knowledge integration in deep learning.