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

Understanding Memory01:19

Understanding Memory

1.3K
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
1.3K
System of Memory01:23

System of Memory

7.2K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
7.2K
Implicit Memories01:24

Implicit Memories

438
Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...
438
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

292
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
292
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

358
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
358
Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

150
Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
150

You might also read

Related Articles

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

Sort by
Same author

Evaluating non-equilibrium trajectories via mean back relaxation: Dependence on length and time scales.

The Journal of chemical physics·2025
Same author

Mean back relaxation for position and densities.

Physical review. E·2024
Same author

Nonlinear Langevin functionals for a driven probe.

The Journal of chemical physics·2024
Same author

Accessing activity and viscoelastic properties of artificial and living systems from passive measurement.

Nature materials·2024
Same author

Scale-Dependent Heat Transport in Dissipative Media via Electromagnetic Fluctuations.

Physical review letters·2024
Same author

Friction of a driven chain: role of momentum conservation, Goldstone and radiation modes.

Journal of physics. Condensed matter : an Institute of Physics journal·2024
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Jan 17, 2026

Author Spotlight: Deciphering Memory and Learning Through Neural Implants for Multi-Region Brain Studies
08:51

Author Spotlight: Deciphering Memory and Learning Through Neural Implants for Multi-Region Brain Studies

Published on: April 26, 2024

1.9K

Identities for nonlinear memory kernels.

Juliana Caspers1, Matthias Krüger1

  • 1Georg-August-Universität Göttingen, Institute for Theoretical Physics, 37073 Göttingen, Germany.

Physical Review. E
|September 16, 2025
PubMed
Summary
This summary is machine-generated.

Researchers derived new identities for nonlinear memory kernels in systems far from equilibrium. These findings extend the fluctuation-dissipation theorem and offer a new way to analyze nonequilibrium systems using Volterra series.

More Related Videos

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

2.0K
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

8.3K

Related Experiment Videos

Last Updated: Jan 17, 2026

Author Spotlight: Deciphering Memory and Learning Through Neural Implants for Multi-Region Brain Studies
08:51

Author Spotlight: Deciphering Memory and Learning Through Neural Implants for Multi-Region Brain Studies

Published on: April 26, 2024

1.9K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

2.0K
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

8.3K

Area of Science:

  • Statistical mechanics
  • Nonlinear dynamics
  • Theoretical physics

Background:

  • Systems far from equilibrium are challenging to model.
  • Nonlinear Volterra series offer a formal description for time-dependent perturbations.
  • Understanding memory kernels is crucial for characterizing system dynamics.

Purpose of the Study:

  • Derive novel identities for nonlinear memory kernels.
  • Extend the fluctuation-dissipation theorem to nonlinear regimes.
  • Establish a framework for analyzing nonequilibrium systems.

Main Methods:

  • Formal derivation of identities for nonlinear memory kernels.
  • Utilizing the principle of local detailed balance.
  • Testing derived identities via simulations of driven Brownian particles.

Main Results:

  • Identities for nonlinear memory kernels were successfully derived.
  • The fluctuation-dissipation theorem was identified as the lowest-order identity.
  • A series relation for nonequilibrium cumulants was established.

Conclusions:

  • The derived identities provide a powerful tool for studying nonequilibrium systems.
  • These findings offer new insights into the behavior of driven systems.
  • The framework connects nonlinear response theory with statistical mechanics principles.