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

Social Traps01:41

Social Traps

26.9K
Social traps are negative situations where people get caught in a direction or relationship that later proves to be unpleasant, with no easy way to back out of or avoid. The concept was orignally introduced by John Platt who applied psychology to Garrett Hardin's "Tragedy of the Commons", where in New England herd owners could let their cattle graze in the common ground. This situation seems like a good idea, but an individual could have an advantage. If they owned...
26.9K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Associative Learning01:27

Associative Learning

1.3K
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...
1.3K
Purposive Learning01:22

Purposive Learning

508
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...
508
Observational Learning01:12

Observational Learning

979
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...
979
Learning Disabilities01:25

Learning Disabilities

613
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
613

You might also read

Related Articles

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

Sort by
Same author

Non-Gaussian Mechanical Motion via Single and Multiphonon Subtraction from a Thermal State.

Physical review letters·2021
Same author

Single-Phonon Addition and Subtraction to a Mechanical Thermal State.

Physical review letters·2021
Same author

Antioxidative effect of Gastrodiae Rhizoma-containing herbal formula in PC12 cell model: abridged secondary publication.

Hong Kong medical journal = Xianggang yi xue za zhi·2020
Same author

Topically applied adipose-derived mesenchymal stem cell treatment in experimental focal cerebral ischaemia.

Hong Kong medical journal = Xianggang yi xue za zhi·2019
Same author

Highly-efficient quantum memory for polarization qubits in a spatially-multiplexed cold atomic ensemble.

Nature communications·2018
Same author

Ultranarrow Optical Inhomogeneous Linewidth in a Stoichiometric Rare-Earth Crystal.

Physical review letters·2016
Same journal

Chlorinated VSLSs Surpass HCFCs in CFC-11-Equivalent Emissions for Ozone Layer Depletion in China.

Nature communications·2026
Same journal

Author Correction: Charge transfer in triphenylamine-tetrazine covalent organic frameworks for solar-driven hydrogen peroxide production.

Nature communications·2026
Same journal

Vegetation browning patterns under compound soil and atmospheric dryness in northern permafrost ecosystems.

Nature communications·2026
Same journal

Voltage imaging of CA1 pyramidal cells and SST+ interneurons reveals stability and plasticity mechanisms of spatial firing.

Nature communications·2026
Same journal

Radical-omics reveals the hydrogen-abstraction pathway of isoprene oxidation.

Nature communications·2026
Same journal

Toughening elastomer via sequentially activated multi-pathway energy dissipation.

Nature communications·2026
See all related articles

Related Experiment Video

Updated: Feb 3, 2026

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures
08:01

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

Published on: November 21, 2019

7.7K

Multiparameter optimisation of a magneto-optical trap using deep learning.

A D Tranter1, H J Slatyer1, M R Hush2

  • 1Centre for Quantum Computation and Communication Technologies, Department of Quantum Science, Research School of Physics and Engineering, The Australian National University, Acton, 2601, Australia.

Nature Communications
|October 21, 2018
PubMed
Summary
This summary is machine-generated.

Machine learning optimizes cold atom trapping. Deep neural networks found novel methods that outperform current solutions, achieving higher optical densities in atomic ensembles.

More Related Videos

Optical Trapping of Nanoparticles
13:39

Optical Trapping of Nanoparticles

Published on: January 15, 2013

23.0K
Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
11:21

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

7.9K

Related Experiment Videos

Last Updated: Feb 3, 2026

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures
08:01

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

Published on: November 21, 2019

7.7K
Optical Trapping of Nanoparticles
13:39

Optical Trapping of Nanoparticles

Published on: January 15, 2013

23.0K
Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
11:21

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

7.9K

Area of Science:

  • Atomic Physics
  • Quantum Optics
  • Machine Learning

Background:

  • Cold atomic ensembles are widely used in research.
  • Many-body interactions complicate analytic optimization of cooling and trapping.
  • Precise control over atomic ensembles is crucial for experiments.

Purpose of the Study:

  • To optimize the magneto-optic cooling and trapping of neutral atomic ensembles.
  • To explore the application of deep artificial neural networks in atomic physics.
  • To develop novel strategies for controlling complex atomic dynamics.

Main Methods:

  • Implementation of a deep artificial neural network.
  • Optimization of magneto-optic cooling and trapping parameters.
  • Comparison of machine learning-derived solutions with existing methods.

Main Results:

  • Machine learning identified non-adiabatic solutions for cooling and trapping.
  • The novel solutions significantly outperform current best-known methods.
  • Higher optical densities were achieved using the optimized parameters.

Conclusions:

  • Deep artificial neural networks offer a powerful tool for optimizing complex atomic systems.
  • Machine learning can discover unconventional solutions surpassing traditional approaches.
  • This method enhances the efficiency and performance of cold atom experiments.