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

1.6K
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...
1.6K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.2K
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
1.2K
Storage01:23

Storage

430
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
430
Classification of Systems-I01:26

Classification of Systems-I

630
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:
630
Neural Circuits01:25

Neural Circuits

3.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.0K
Parallel Processing01:20

Parallel Processing

806
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
806

You might also read

Related Articles

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

Sort by
Same author

Reservoir computing with generalized readout based on generalized synchronization.

Scientific reports·2024
Same author

Characterizing Small-Scale Dynamics of Navier-Stokes Turbulence with Transverse Lyapunov Exponents: A Data Assimilation Approach.

Physical review letters·2024
Same author

Fluid mixing optimization with reinforcement learning.

Scientific reports·2022
Same author

Heat transport in nonlinear lattices free from the umklapp process.

Physical review. E·2022
Same author

Transfer learning for nonlinear dynamics and its application to fluid turbulence.

Physical review. E·2020
Same author

Frequency of Switching Touching Mode Reflects Tactile Preference Judgment.

Scientific reports·2020
Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026
Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026
Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026
Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026
Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026
Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Feb 23, 2026

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.3K

Reservoir Computing Beyond Memory-Nonlinearity Trade-off.

Masanobu Inubushi1, Kazuyuki Yoshimura2

  • 1NTT Communication Science Laboratories, NTT Corporation, 3-1, Morinosato Wakamiya Atsugi-shi, Kanagawa, 243-0198, Japan. inubushi.masanobu@lab.ntt.co.jp.

Scientific Reports
|September 2, 2017
PubMed
Summary
This summary is machine-generated.

Reservoir computing uses nonlinear dynamics for information processing. This study clarifies the memory-nonlinearity trade-off and proposes a mixture reservoir with linear and nonlinear dynamics to enhance performance.

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
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

8.4K

Related Experiment Videos

Last Updated: Feb 23, 2026

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.3K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
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

8.4K

Area of Science:

  • Nonlinear dynamics
  • Information theory
  • Machine learning

Background:

  • Reservoir computing, a brain-inspired machine learning method, utilizes signal-driven dynamical systems and nonlinear phenomena like synchronization.
  • Understanding reservoir computing offers insights into information storage and processing in nonlinear dynamical systems, benefiting nonlinear sciences.

Purpose of the Study:

  • Investigate the memory-nonlinearity trade-off in reservoir computing from a nonlinear physics and information theory perspective.
  • Clarify the dynamical mechanism causing nonlinear dynamics to degrade memory in dynamical systems.
  • Propose and validate a novel 'mixture reservoir' to improve information processing performance.

Main Methods:

  • Analysis of a variational equation to understand the memory-nonlinearity trade-off.
  • Development of a mixture reservoir combining linear and nonlinear dynamics.
  • Numerical verification using the echo state network model for function approximation and complex tasks.

Main Results:

  • A dynamical mechanism explaining how nonlinear dynamics degrade memory was identified.
  • The proposed mixture reservoir demonstrated improved information processing performance.
  • Adding a small amount of linear dynamics to nonlinear systems significantly enhanced performance for certain tasks.

Conclusions:

  • The memory-nonlinearity trade-off is a fundamental challenge in nonlinear dynamical systems.
  • Mixture reservoirs offer a promising approach to optimize information processing by balancing linearity and nonlinearity.
  • This work provides a foundation for advancing reservoir computing and related nonlinear sciences.