Jove
Visualize
Contact Us

Related Concept Videos

Evolutionary Psychology01:20

Evolutionary Psychology

455
Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
455
Neuroplasticity01:01

Neuroplasticity

838
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
838
Cognitive Learning01:21

Cognitive Learning

673
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...
673
Purposive Learning01:22

Purposive Learning

212
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
212
Neural Circuits01:25

Neural Circuits

1.7K
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...
1.7K
Neural Regulation01:37

Neural Regulation

40.4K
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.
40.4K

You might also read

Related Articles

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

Sort by
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
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 Experiment Video

Updated: Sep 21, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Personalized Hybrid Education Framework Based on Neuroevolution Methodologies.

Wenjing Yin1

  • 1Zhengzhou Preschool Education College, Zhengzhou, 450000, China.

Computational Intelligence and Neuroscience
|May 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel artificial intelligence (AI) method, growing semiorganizing neural gas (GsONG), for inclusive education. GsONG enhances learning by accurately categorizing student skills and needs within differentiated frameworks.

More Related Videos

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy
07:21

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Published on: June 27, 2025

144
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

12.7K

Related Experiment Videos

Last Updated: Sep 21, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K
Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy
07:21

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Published on: June 27, 2025

144
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

12.7K

Area of Science:

  • Artificial Intelligence in Education
  • Neuroevolutionary Computing
  • Machine Learning for Skill Assessment

Background:

  • Future pedagogical systems require anthropocentric, inclusive programs adaptable to individual student needs.
  • Innovative AI is crucial for informed educational decisions, accurately identifying and categorizing student skills and knowledge.
  • Current methods struggle with multicriteria grouping and differentiation, especially with small datasets.

Purpose of the Study:

  • To propose a neuroevolution emerging technique combining evolutionary computation and hybrid artificial neural networks.
  • To introduce the growing semiorganizing neural gas (GsONG) as a practical AI methodology for enhancing inclusive, differentiated learning.
  • To accurately categorize learner abilities, skills, and needs using advanced clustering.

Main Methods:

  • Developed a neuroevolution emerging technique integrating evolutionary computation and hybrid artificial neural networks.
  • Implemented the growing semiorganizing neural gas (GsONG), a neural network architecture with competing and cooperating neurons.
  • Utilized a heuristic method to optimize neuron topology and identify unknown patterns.

Main Results:

  • The GsONG approach demonstrated high accuracy in categorizing learner abilities, skills, and needs.
  • The algorithm effectively addressed challenges in multicriteria grouping and differentiation of uncertain structures.
  • Successful application shown even with small or sparse datasets.

Conclusions:

  • The proposed GsONG algorithm offers a robust AI solution for inclusive and differentiated educational frameworks.
  • GsONG enhances learning experiences by providing accurate categorization of student attributes.
  • This method shows promise for improving educational decision-making, particularly in data-scarce scenarios.