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

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
Associative Learning01:27

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...
Introduction to Learning01:18

Introduction to Learning

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...
Neural Circuits01:25

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...
Observational Learning01:12

Observational Learning

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 because...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

You might also read

Related Articles

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

Sort by
Same author

[A case of mucopolysaccharidosis type ⅢA with ventricular hypertrophy as the first clinical presentation].

Zhonghua xin xue guan bing za zhi·2025
Same author

[Mechanisms by which Mettl3 regulates pericyte-myofibroblast transdifferentiation through PI3K/AKT signaling pathway].

Zhonghua xin xue guan bing za zhi·2024
Same author

[Expression and protective effect of chemerin in idiopathic pulmonary fibrosis].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases·2023
Same author

[Advancement in endovascular therapy of aortoiliac occlusive disease].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2022
Same author

[Establishment of a cytokine release syndrome associated with chimeric antigen receptor T cell treatment in SCID/Beige mice model].

Zhonghua zhong liu za zhi [Chinese journal of oncology]·2021
Same author

[Drug-coated balloons versus bare metal stent for treatment of femoropopliteal lesions:36 month follow-up results of single center].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2021

Related Experiment Video

Updated: Jul 7, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

The ensemble approach to neural-network learning and generalization.

B Igelnik1, Y H Pao, S R LeClair

  • 1Case Western Reserve University, Cleveland, OH 44106, USA.

IEEE Transactions on Neural Networks
|February 7, 2008
PubMed
Summary
This summary is machine-generated.

A novel method enhances learning and generalization in feedforward neural networks using adaptive optimization and linear regression. This approach efficiently determines network parameters and node count for diverse applications.

Related Experiment Videos

Last Updated: Jul 7, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • General one-hidden layer feedforward neural networks require efficient learning and generalization methods.
  • Existing techniques may lack computational efficiency or adaptability.

Purpose of the Study:

  • To introduce a new, computationally efficient method for learning and generalization in feedforward neural networks.
  • To enable adaptive optimization of network parameters and selection of an appropriate number of nodes.

Main Methods:

  • Utilizes a linear combination of heterogeneous nodes with random parameters.
  • Employs adaptive stochastic optimization for parameter learning using generalization data.
  • Applies linear regression for learning linear coefficients using training data, processing one node at a time.

Main Results:

  • The method allows for determining the optimal number of network nodes.
  • Demonstrates computational efficiency in learning and generalization.
  • Successfully tested on both mathematical problems and real-world materials science applications.

Conclusions:

  • The proposed method offers an effective and efficient approach for training feedforward neural networks.
  • It provides flexibility in network design and parameter optimization.
  • Shows promise for applications in diverse scientific and technological fields.