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

Observational Learning01:12

Observational Learning

1.2K
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...
1.2K
Associative Learning01:27

Associative Learning

1.9K
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...
1.9K
Neural Circuits01:25

Neural Circuits

3.3K
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...
3.3K
Cognitive Learning01:21

Cognitive Learning

1.6K
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...
1.6K
Introduction to Learning01:18

Introduction to Learning

1.6K
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...
1.6K
Purposive Learning01:22

Purposive Learning

581
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...
581

You might also read

Related Articles

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

Sort by
Same author

Steric Hindrance Enhanced the Bowl-Shaped Curvature of Bridged Triphenylamine.

Organic letters·2026
Same author

Nitrogen-Doped 5-7-7 Ring-Fused Hexa-<i>peri</i>-hexabenzocoronene: A Saddle-Planar Hybrid Nanographene for Fullerene Recognition.

Organic letters·2026
Same author

Perceptual Inductive Bias Is What You Need Before Contrastive Learning.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition·2026
Same author

Self-Attention-Based Contextual Modulation Improves Neural System Identification.

... International Conference on Learning Representations·2026
Same author

A Biomimetic Nanovesicle-Based Synthetic Vaccine Platform Through Co-Anchoring Oxidized Phospholipid and CpG for Rejuvenating Antitumor Immunity in Aged Mice.

Angewandte Chemie (International ed. in English)·2026
Same author

Positively Charged Polymer-Brush MOFs for Large-Area, Pressure-Resistant Gas Separation Membranes.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Interpretable Failure Detection with Human-Level Concepts.

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence·2026
Same journal

ChatCLIDS: Simulating Persuasive AI Dialogues to Promote Closed-Loop Insulin Adoption in Type 1 Diabetes Care.

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence·2026
Same journal

Beyond Accuracy: On the Effects of Fine-tuning Towards Vision-Language Model's Prediction Rationality.

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence·2026
Same journal

<i>OrgaCast</i>: A Trustworthy Spatiotemporal Diffusion Model for Fluorescence Organoid Forecasting.

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence·2026
Same journal

Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models.

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence·2026
Same journal

iDT-diet: Toward Personalized Health Forecasting-An Intelligent Digital Twin Model for Diet-Influenced Biomarker Trajectories (Student Abstract).

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence·2026
See all related articles
  1. Home
  3. Modeling Rapid Contextual Learning In The Visual Cortex With Fast-weight Deep Autoencoder Networks.
  1. Home
  3. Modeling Rapid Contextual Learning In The Visual Cortex With Fast-weight Deep Autoencoder Networks.

Related Experiment Video

Author Spotlight: Advancing Alzheimer's Research &#8211; 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

Modeling Rapid Contextual Learning in the Visual Cortex with Fast-Weight Deep Autoencoder Networks.

Yue Li1, Weifan Wang1, Tai Sing Lee1

  • 1Carnegie Mellon University, Pittsburgh, USA.

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence
|April 2, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Familiarity training rapidly enhances global context sensitivity in early visual networks. Using a Vision Transformer (ViT) and Low-Rank Adaptation (LoRA), researchers modeled how fast weights enable this learning, improving network robustness.

More Related Videos

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

874
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K

Related Experiment Videos

Author Spotlight: Advancing Alzheimer's Research &#8211; 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
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

874
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K

Area of Science:

  • Computational neuroscience
  • Deep learning
  • Visual processing

Background:

  • Early visual cortex learns global context via local recurrent interactions.
  • This learning sparsifies neural responses and reduces mean activity for familiar contexts.
  • Recurrent neural circuits reshape neural manifolds, improving robustness and invariance.

Purpose of the Study:

  • Investigate how familiarity training induces global context sensitivity in early deep neural network layers.
  • Explore the role of fast weights in rapid learning using Low-Rank Adaptation (LoRA).
  • Model the functional consequences of rapid global context learning in the brain.

Main Methods:

  • Employed a Vision Transformer (ViT)-based autoencoder for functional investigation.
  • Utilized Low-Rank Adaptation (LoRA) to implement fast weights within Transformer layers.
  • Analyzed self-attention mechanisms and latent representations during familiarity training.

    • Main Results:

    • ViT autoencoder's self-attention performed manifold transforms similar to neural models of familiarity.
    • Familiarity training aligned early layer representations with global context information in top layers.
    • Self-attention broadened its scope to encompass more image details, not just object recognition features.
    • LoRA-based fast weights significantly amplified these familiarity-induced effects.

    Conclusions:

    • Familiarity training can introduce global sensitivity to earlier layers in hierarchical networks.
    • A hybrid fast-and-slow weight architecture offers a computational model for rapid global context learning.
    • This approach provides insights into the neurobiological mechanisms of visual context processing.