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

Auditory Pathway01:15

Auditory Pathway

5.5K
Auditory pathways constitute the complex neural circuits responsible for transmitting and interpreting auditory information from the peripheral auditory system to the brain. Sound waves are initially captured by the outer ear, funneled through the ear canal, and reach the tympanic membrane (eardrum). These vibrations are transmitted via the middle ear's ossicles to the inner ear's cochlea.
When viewed cross-sectionally, the cochlea reveals the scala vestibuli and scala tympani flanking...
5.5K
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

239
The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
239
Auditory Perception01:17

Auditory Perception

364
The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
364
Associative Learning01:27

Associative Learning

441
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...
441

You might also read

Related Articles

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

Sort by
Same author

Analyzing Health Care Professionals' Resilience and Emotional Responses to COVID-19 via Twitter: Retrospective Cohort and Matched Comparison Group Study.

Journal of medical Internet research·2025
Same author

Inductive link prediction facilitates the discovery of missing links and enables cross-community inference in ecological networks.

Nature ecology & evolution·2025
Same author

The use of trigger warnings on social media: a text analysis study of X.

PloS one·2025
Same author

Global and Local Trends Affecting the Experience of US and UK Healthcare Professionals during COVID-19: Twitter Text Analysis.

International journal of environmental research and public health·2022
Same author

The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses.

Journal of medical Internet research·2021
Same author

The interplay between vaccination and social distancing strategies affects COVID19 population-level outcomes.

PLoS computational biology·2021

Related Experiment Video

Updated: Jul 18, 2025

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

30.6K

Centrality Learning: Auralization and Route Fitting.

Xin Li1, Liav Bachar2, Rami Puzis2

  • 1Department of Mechatronics Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.

Entropy (Basel, Switzerland)
|August 26, 2023
PubMed
Summary
This summary is machine-generated.

We developed a deep learning model to automatically learn network centrality measures. This approach accurately approximates various centrality indices, outperforming existing methods for network analysis.

Keywords:
auralizationcentralitydeep learningroutingsound recognition

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K
A Method to Study Adaptation to Left-Right Reversed Audition
07:14

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

6.6K

Related Experiment Videos

Last Updated: Jul 18, 2025

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

30.6K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.7K
A Method to Study Adaptation to Left-Right Reversed Audition
07:14

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

6.6K

Area of Science:

  • Network Science
  • Machine Learning
  • Graph Theory

Background:

  • Developing custom centrality measures requires significant expertise and effort.
  • Automating the learning of centrality measures is crucial for ground-truth node scoring.

Purpose of the Study:

  • To propose a generic deep-learning architecture for learning arbitrary centrality measures.
  • To leverage Routing Betweenness Centrality (RBC) and spectral graph theory for centrality learning.

Main Methods:

  • Implemented a novel differentiable version of Routing Betweenness Centrality (RBC).
  • Designed a deep learning architecture that learns routing policies to approximate centrality measures.
  • Validated the approach on various network topologies.

Main Results:

  • The proposed architecture successfully learns multiple types of centrality indices.
  • Achieved higher accuracy in approximating centrality measures compared to state-of-the-art methods.
  • Demonstrated the effectiveness of RBC and spectral graph theory insights.

Conclusions:

  • The deep learning architecture provides an effective method for automated centrality learning.
  • This approach reduces the need for domain expertise in developing centrality measures.
  • Offers a scalable and accurate solution for network analysis and node scoring.