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

Associative Learning01:27

Associative Learning

243
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...
243
Introduction to Learning01:18

Introduction to Learning

303
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...
303
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

467
Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
467
Synaptic Signaling01:09

Synaptic Signaling

5.4K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
5.4K
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

529
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
529
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

337
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...
337

You might also read

Related Articles

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

Sort by
Same author

Stationary covariance spectra of discrete-time non-normal random recurrent dynamics.

ArXiv·2026
Same author

Stimulus symmetries can confound representational similarity analyses.

ArXiv·2026
Same author

Structure, disorder, and dynamics in task-trained recurrent neural circuits.

bioRxiv : the preprint server for biology·2026
Same author

Author Correction: Predictive coding of reward in the hippocampus.

Nature·2026
Same author

Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

A note on the dynamics of extended-context disordered kinetic spin models.

Journal of physics. A, Mathematical and theoretical·2026

Related Experiment Video

Updated: May 12, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.4K

Summary statistics of learning link changing neural representations to behavior.

Jacob A Zavatone-Veth1,2, Blake Bordelon3,1, Cengiz Pehlevan3,1,4

  • 1Center for Brain Science, Harvard University, Cambridge, MA, USA.

Arxiv
|May 2, 2025
PubMed
Summary

Statistical physics theories suggest summary statistics can predict neural network learning performance. This approach offers a framework for analyzing neural data to understand learning in biological and artificial networks.

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K
Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
08:51

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder

Published on: December 15, 2023

1.1K

Related Experiment Videos

Last Updated: May 12, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.4K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K
Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
08:51

Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder

Published on: December 15, 2023

1.1K

Area of Science:

  • Neuroscience
  • Statistical Physics
  • Machine Learning

Background:

  • Understanding large-scale neural activity during learning is a key challenge.
  • Theories from statistical physics offer insights into complex systems, including neural networks.

Purpose of the Study:

  • To review how summary statistics can advance theoretical understanding of neural network learning.
  • To propose the application of this perspective to analyze neural data for improved learning insights.

Main Methods:

  • Review of recent advances in applying summary statistics to neural network learning theories.
  • Conceptual framework development for analyzing neural data using summary statistics.

Main Results:

  • Summary statistics can effectively predict performance in neural networks during learning.
  • This statistical approach provides a powerful lens for interpreting neural data.

Conclusions:

  • Summary statistics derived from statistical physics offer a promising theoretical framework for understanding neural learning.
  • This framework can enhance the analysis of both biological and artificial neural network learning processes.