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-species Conserved Sequences02:51

Multi-species Conserved Sequences

3.9K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
3.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

29
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...
29
Chunking01:12

Chunking

35
Chunking is a powerful cognitive technique that improves short-term memory retention by organizing information into smaller, more manageable units. The brain, limited by working memory capacity, can more easily process and store information when it is divided into "chunks" rather than presented as discrete, unrelated elements. Chunking is especially useful when dealing with large amounts of information, such as numerical sequences, words, or complex ideas.
The principle behind chunking...
35
Downsampling01:20

Downsampling

112
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
112
Elaborative Rehearsals01:07

Elaborative Rehearsals

64
Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
64
Retrieval01:12

Retrieval

51
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
51

You might also read

Related Articles

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

Sort by
Same author

Resilience in collective behaviors of "next generation reservoir computer" oscillators via transmitting signal distortion.

Chaos (Woodbury, N.Y.)·2026
Same author

Nonlinear dynamics of reservoir computing: Theory, realization, and application.

Chaos (Woodbury, N.Y.)·2026
Same author

Chaos, computation and the century of complexity.

Chaos (Woodbury, N.Y.)·2026
Same author

Replicator dynamics and behavior-augmented multiscale epidemic modeling.

Chaos (Woodbury, N.Y.)·2025
Same author

Machine learning informed by micro- and mesoscopic statistical physics methods for community detection.

Chaos (Woodbury, N.Y.)·2025
Same author

Synchronization and chimera states in time-varying multilayer networks with higher-order interactions.

Chaos (Woodbury, N.Y.)·2025
Same journal

Topological dependence of viral mutation spread in complex host-interaction networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Multifractal signatures of Hamiltonian chaos in Hyperion's rotational dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026
Same journal

State estimation in spatiotemporal chaos via low-rank StatFEM.

Chaos (Woodbury, N.Y.)·2026
Same journal

Universal response functions in driven dissipative tunneling dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A network-based approach to characterize the dynamics of the coupling field of thermoacoustic oscillators in annular geometry.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: May 13, 2025

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

457

Reservoir computing with the minimum description length principle.

Antony Mizzi1, Michael Small1,2, David M Walker1

  • 1Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Perth, WA 6009, Australia.

Chaos (Woodbury, N.Y.)
|April 15, 2025
PubMed
Summary
This summary is machine-generated.

The minimum description length (MDL) principle enhances echo-state network forecasting accuracy by selecting optimal readout subsets. This method improves predictions for chaotic systems and benefits higher-order network terms.

More Related Videos

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
08:02

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

12.4K
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K

Related Experiment Videos

Last Updated: May 13, 2025

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

457
Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
08:02

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

12.4K
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K

Area of Science:

  • Computational neuroscience
  • Machine learning
  • Information theory

Background:

  • Echo-state networks (ESNs) are recurrent neural networks effective for time-series forecasting.
  • Model selection is crucial for optimizing ESN performance.
  • The minimum description length (MDL) principle offers a principled approach to model selection.

Purpose of the Study:

  • To apply the MDL principle for selecting echo-state network readout subsets.
  • To evaluate the impact of MDL-based subset selection on forecasting accuracy.
  • To investigate the influence of MDL selection on higher-order term performance.

Main Methods:

  • Utilized the minimum description length (MDL) principle as an information-theoretic criterion.
  • Applied MDL to determine optimal readout subsets in echo-state networks.
  • Tested the method on forecasting the Lorenz, Rössler, and Thomas attractors.

Main Results:

  • MDL subset selection significantly improved forecasting accuracy for all tested chaotic attractors.
  • The performance benefit of including higher-order terms in the readout layer was enhanced.
  • Improvements were attributed to reduced linear dependence and increased consistency in the selected subsets.

Conclusions:

  • The MDL principle provides an effective strategy for optimizing echo-state network readout layers.
  • MDL-based selection enhances the predictive capabilities of ESNs, particularly for complex dynamical systems.
  • This approach offers a robust method for improving model performance and interpretability in recurrent neural networks.