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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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...
Observational Learning01:12

Observational Learning

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 because...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

You might also read

Related Articles

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

Sort by
Same author

Statistical physics of principal minors: Cavity approach.

Physical review. E·2024
Same author

Statistical physics of loopy interactions: independent-loop approximation and beyond.

Physical review. E, Statistical, nonlinear, and soft matter physics·2015
Same author

Bethe free-energy approximations for disordered quantum systems.

Physical review. E, Statistical, nonlinear, and soft matter physics·2014
Same author

Computing loop corrections by message passing.

Physical review. E, Statistical, nonlinear, and soft matter physics·2013
Same author

Large deviations of cascade processes on graphs.

Physical review. E, Statistical, nonlinear, and soft matter physics·2013
Same author

Cavity approach to sphere packing in Hamming space.

Physical review. E, Statistical, nonlinear, and soft matter physics·2012
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

Related Experiment Video

Updated: May 31, 2026

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

Inference and learning in sparse systems with multiple states.

A Braunstein1, A Ramezanpour, R Zecchina

  • 1Human Genetics Foundation, Via Nizza 52, I-10126 Torino, Italy. alfredo.braunstein@polito.it

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|July 7, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a cavity method for inferring system parameters from limited data in non-ergodic systems. It also proposes a local learning protocol for neural networks capable of storing patterns effectively.

Related Experiment Videos

Last Updated: May 31, 2026

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

Area of Science:

  • Statistical physics
  • Computational neuroscience

Background:

  • Inference in non-ergodic systems with multiple attractors is challenging.
  • The Hopfield model in its memory phase provides a relevant system for study.

Purpose of the Study:

  • To develop methods for parameter inference from limited data in non-ergodic systems.
  • To propose a novel learning protocol for neural networks.

Main Methods:

  • Utilizing a cavity method approach for parameter reconstruction.
  • Sampling data from attractor states of the Hopfield model.
  • Converting inference results into a local learning protocol.

Main Results:

  • Successfully reconstructed couplings from sparse data in the Hopfield model.
  • Developed a simple, fully local learning protocol for neural networks.
  • Demonstrated pattern storage with finite overlap without entering a spin-glass phase.

Conclusions:

  • The cavity method offers a viable approach for inference in complex systems.
  • The proposed learning protocol is efficient and robust for neural network pattern storage.