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

Upsampling01:22

Upsampling

314
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
314
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

135
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....
135
Exponential Fourier series01:24

Exponential Fourier series

330
In audio signal processing, the exponential Fourier series plays a crucial role in sound synthesis, allowing complex sounds to be broken down into simpler sinusoidal components. This decomposition process is fundamental in analyzing and reconstructing musical notes and other audio signals. The exponential Fourier series expresses periodic signals as the sum of complex exponentials at both positive and negative harmonic frequencies, providing a powerful tool for signal analysis.
Euler's identity...
330
Sampling Theorem01:15

Sampling Theorem

771
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
771
Neural Circuits01:25

Neural Circuits

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Modularity-dependent storage of dynamic spiking patterns: Bridging micro- and mesoscopic representations.

Physical review. E·2026
Same author

Efficient simulation of a pair of dissipative qubits antiferromagnetically coupled.

Physical review. E·2026
Same author

Physiologic effects of THRIVE versus facemask preoxygenation in obese patients undergoing bariatric surgery: A pilot observational electrical impedance tomography study.

Journal of clinical anesthesia·2026
Same author

Symmetry breaking and avalanche shapes in modular neural networks.

Frontiers in computational neuroscience·2026
Same author

The embodied digital divide: how sensorimotor experience shapes touchscreen typing performance and strategy.

Experimental brain research·2026
Same author

Fluctuation-dissipation relations in the imbalanced Wilson-Cowan model.

Physical review. E·2023
Same journal

Advancing microalgae biomass cultivation for an integrated sustainable wastewater treatment and resource recovery.

iScience·2026
Same journal

Corrigendum to "Human adipose ECM alleviates radiation-induced skin fibrosis via endothelial cell-mediated M2 macrophage polarization" [iScience, Volume 26, Issue 9 (2023) 107660].

iScience·2026
Same journal

High-definition transcranial direct current stimulation enhances exercise-induced hypoalgesia in patients with chronic low back pain.

iScience·2026
Same journal

From pre-tumor to tumor: Decoding the endoscopic-pathologic spectrum of neoplastic lesions in autoimmune gastritis.

iScience·2026
Same journal

Corrigendum to "A cobalt-aluminium layered double hydroxide with a nickel core-shell structure nanocomposite for supercapacitor applications" [iScience, 28 (2025) 111672].

iScience·2026
Same journal

Repurposing primaquine diphosphate for imatinib-resistant chronic myeloid leukemia via targeting BCR-ABL and Wnt/β-catenin pathway.

iScience·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 2025

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

10.4K

Inferring global exponents in subsampled neural systems.

Davide Conte1, Antonio de Candia2,3

  • 1Department of Mathematics & Physics, University of Campania "Luigi Vanvitelli", viale Lincoln 5, 81100 Caserta, Italy.

Iscience
|July 29, 2025
PubMed
Summary
This summary is machine-generated.

Subsampling avalanche activity can distort critical exponents. However, some exponents, like those in power spectrum and detrended fluctuation analysis (DFA), remain robust, preserving long-time correlations for unbiased analysis.

Keywords:
Natural sciencesbiological sciencesneural networks

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K

Related Experiment Videos

Last Updated: Sep 13, 2025

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

10.4K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K

Area of Science:

  • Complex systems
  • Statistical physics
  • Network science

Background:

  • Avalanche-like activity is common in complex systems.
  • Critical exponents characterize system dynamics and connectivity.
  • Subsampling can lead to inaccurate exponent measurements.

Purpose of the Study:

  • To investigate the impact of subsampling on critical exponents.
  • To identify robust exponents unaffected by partial observation.
  • To develop methods for unbiased exponent estimation from subsampled data.

Main Methods:

  • Utilizing branching process models.
  • Employing (2 + 1)D directed percolation simulations.
  • Analyzing power spectrum and detrended fluctuation analysis (DFA).

Main Results:

  • Certain critical exponents (power spectrum, DFA) are robust to subsampling.
  • Robustness is linked to preserved long-time correlations.
  • Observed exponents remain accurate within specific frequency intervals.

Conclusions:

  • Subsampling does not always obscure critical dynamics.
  • Robust exponents offer a reliable way to study unobserved systems.
  • Findings are model-independent and broadly applicable.