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 Experiment Videos

Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine.

Marcelo Guzman1, Simone Ciarella2,3, Andrea J Liu1,4

  • 1Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104.

Proceedings of the National Academy of Sciences of the United States of America
|May 21, 2026
PubMed
Summary

Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Associative Learning01:27

Associative Learning

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...

You might also read

Related Articles

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

Sort by
Same author

Training of physical neural networks.

Nature·2025
Same author

Cornerstones are the key stones: using interpretable machine learning to probe the clogging process in 2D granular hoppers.

Soft matter·2025
Same author

Shape matters: inferring the motility of confluent cells from static images.

Soft matter·2025
Same author

A minimal vertex model explains how the amnioserosa avoids fluidization during <i>Drosophila</i> dorsal closure.

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

Machine learning without a processor: Emergent learning in a nonlinear analog network.

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

Model-free characterization of topological edge and corner states in mechanical networks.

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

In This Issue.

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

Correction for Otsuki et al., Extracellular sulfatases support cartilage homeostasis by regulating BMP and FGF signaling pathways.

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

Hive mind: Microbial communities and the making of memory.

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

Targets for disease modification in schizophrenia: New findings add to evidence for the involvement of the immune complement system.

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

Correction for Wang et al., The role of reduced aerosol masking from air pollutant emission reductions in recent global warming acceleration (2013-2023).

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

Correction for Mishra, Ecology is not yet ready for AI-and why that matters.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles
This summary is machine-generated.

Researchers developed a self-learning resistor network, the Restricted Kirchhoff Machine, for energy-efficient computation. This physical system demonstrates unsupervised learning capabilities comparable to the Restricted Boltzmann Machine algorithm.

Area of Science:

  • Physics
  • Computer Science
  • Machine Learning

Background:

  • Autonomous physical learning systems offer energy-efficient computation by leveraging physical dynamics.
  • Traditional computers rely on external computation, limiting efficiency.

Purpose of the Study:

  • Introduce a novel self-learning resistor network, the Restricted Kirchhoff Machine (RKM).
  • Demonstrate RKM's capability to solve unsupervised learning tasks.
  • Compare RKM's performance and scalability against traditional algorithms.

Main Methods:

  • Designed a self-learning resistor network based on Contrastive Local Learning Networks.
  • Implemented a contrastive local learning rule using two identical networks.
  • Simulated RKM training on handwritten digit datasets.
Keywords:
emergent learningmachine learningneuromorphic computingphysical learning

Related Experiment Videos

  • Analyzed scaling behavior of time, power, and energy per operation.

    • Main Results:

    • The Restricted Kirchhoff Machine successfully learned unsupervised tasks, proving its concept.
    • Simulations showed RKM's potential for distributed and energy-efficient learning.
    • Performance scaling was compared with Restricted Boltzmann Machines on CPU and GPU.

    Conclusions:

    • Physical learning systems like RKM offer a promising alternative for energy-efficient AI.
    • RKM demonstrates a viable approach to hardware-based unsupervised learning.
    • Further research can explore RKM's scalability and application in complex tasks.