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

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...
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
Echo01:06

Echo

The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case, then the...
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Transfer Function to State Space01:23

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
Equivalent Resistance01:16

Equivalent Resistance

In circuit analysis, situations often arise where resistors are neither in series nor parallel configurations. To tackle such scenarios, three-terminal equivalent networks like the wye (Y) (Figure 1 (a)) or tee (T) and delta (Δ) (Figure 1 (b)) or pi (π) networks come into play. These networks offer versatile solutions and are frequently encountered in various applications, including three-phase electrical systems, electrical filters, and matching networks.

You might also read

Related Articles

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

Sort by
Same author

Choosing observables that capture critical slowing down before tipping points: A Fokker-Planck operator approach.

Physical review. E·2026
Same author

Looks like SNARC spirit: Coexistence of short- and long-term associations between letters and space.

Quarterly journal of experimental psychology (2006)·2025
Same author

Contextualizing predictive minds.

Neuroscience and biobehavioral reviews·2024
Same author

Infants infer and predict coherent event interactions: Modeling cognitive development.

PloS one·2024
Same author

Virtual reality assessment of a high-calorie food bias: Replication and food-specificity in healthy participants.

Behavioural brain research·2024
Same author

Multistability and intermediate tipping of the Atlantic Ocean circulation.

Science advances·2024
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: May 18, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Balanced echo state networks.

Danil Koryakin1, Johannes Lohmann, Martin V Butz

  • 1Cognitive Modeling, Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany.

Neural Networks : the Official Journal of the International Neural Network Society
|October 6, 2012
PubMed
Summary
This summary is machine-generated.

Optimizing output feedback and network size in echo state networks (ESNs) is key. Proper parameter choices significantly improve performance in approximating complex dynamics, revealing ESNs

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Related Experiment Videos

Last Updated: May 18, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Complex Systems

Background:

  • Echo state networks (ESNs) are a type of recurrent neural network.
  • Understanding the interplay between network parameters and internal dynamics is crucial for ESN effectiveness.

Purpose of the Study:

  • To investigate the interaction between output feedback and reservoir dynamics in ESNs.
  • To determine the impact of reservoir size and output feedback strength on ESN performance.

Main Methods:

  • Experimental study on the multiple superimposed oscillators (MSOs) benchmark.
  • Analysis of the dual effect of output feedback strength on reservoir dynamics.
  • Evaluation of reservoir size's influence on ESN effectiveness.

Main Results:

  • Output feedback strength exhibits a dual effect, driving dynamics but potentially blocking suitable ones.
  • Reservoir size is a critical factor in generating effective ESNs.
  • Smaller networks can outperform larger ones depending on MSO complexity.

Conclusions:

  • Optimizing output feedback weight range and network size is crucial for effective ESNs.
  • ESNs can approximate MSOs with significantly reduced errors compared to previous reports.
  • ESNs possess untapped potential, warranting further research into their capabilities.