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

Storage01:23

Storage

A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...
Neuroplasticity01:01

Neuroplasticity

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.
Impact of Schemas01:30

Impact of Schemas

Schemas are cognitive structures that provide a framework for interpreting and organizing social information. They help individuals navigate complex environments by offering expectations about people, events, and behaviors. Schemas influence attention, encoding, and retrieval processes, thereby shaping the entire trajectory of information processing in social contexts.Attention and Cognitive LoadDuring initial attention, schemas function as filters that prioritize schema-consistent information,...
Schemas01:42

Schemas

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
Schemata01:17

Schemata

A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
Two types of schemata are:
Plasticity00:58

Plasticity

Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...

You might also read

Related Articles

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

Sort by
Same author

Higher-order epistasis drives evolutionary unpredictability toward novel antibiotic resistance.

bioRxiv : the preprint server for biology·2025
Same author

An invariant schema emerges within a neural network during hierarchical learning of visual boundaries.

bioRxiv : the preprint server for biology·2025
Same author

A lever hypothesis for Synaptotagmin-1 action in neurotransmitter release.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Synergistic and antagonistic drug interactions are prevalent but not conserved across acute myeloid leukemia cell lines.

Research square·2024
Same author

Molecular mechanism underlying SNARE-mediated membrane fusion enlightened by all-atom molecular dynamics simulations.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Hairpin trimer transition state of amyloid fibril.

Nature communications·2024

Related Experiment Video

Updated: Jul 1, 2026

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

Hierarchical learning creates invariant schema within plastic neural networks.

James R Elder1,2,3, Jie Zheng4,5, Lydia B Shimelis6

  • 1Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Journal of Computational Neuroscience
|June 30, 2026
PubMed
Summary

Cognitive neural circuits balance learning and stability using hierarchical learning, which creates fixed schema circuits. This contrasts with standard artificial neural networks, offering a biologically consistent model for persistent cognitive frameworks.

Keywords:
cognitive neuroscienceschema formation

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Jul 1, 2026

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Cognitive neural circuits require balancing plasticity for learning and stability for reasoning schemas.
  • The mechanisms by which learning rules form and protect these schemas from overwriting are not fully understood.

Purpose of the Study:

  • To investigate how hierarchical learning algorithms can create stable cognitive schemas within neural networks.
  • To compare the learning dynamics of hierarchical algorithms with standard end-to-end backpropagation.

Main Methods:

  • A visual boundary detection task was used to train a hierarchical learning algorithm.
  • The weight stability of the hierarchical schema circuit was analyzed post-sparse initial training.
  • Weight changes during training were compared between the hierarchical algorithm and end-to-end backpropagation.

Main Results:

  • Hierarchical learning created an invariant schema circuit with fixed weights after initial training.
  • Additional data refined upstream representations rather than overwriting the core schema.
  • End-to-end backpropagation led to comprehensive weight changes throughout training.

Conclusions:

  • Hierarchical learning is sufficient to encode biologically consistent persistent cognitive models.
  • This approach offers a mechanism for neural networks to maintain stable reasoning frameworks during continual learning.
  • The findings suggest a potential pathway for developing more stable and biologically plausible artificial intelligence.