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

You might also read

Related Articles

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

Sort by
Same author

Neural timescales from a computational perspective.

Nature neuroscience·2026
Same author

Corrigendum to "Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes" [Medical Image Analysis 103 (2025) 103580].

Medical image analysis·2026
Same author

EDAPT: towards calibration-free BCIs with continual online adaptation.

Journal of neural engineering·2026
Same author

JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.

Nature methods·2025
Same author

Simulation-based inference for subject-specific tuning of middle ear finite-element models towards personalized objective diagnosis.

Scientific reports·2025
Same author

Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types.

Patterns (New York, N.Y.)·2025

Related Experiment Video

Updated: Jun 7, 2025

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

9.9K

Generating realistic neurophysiological time series with denoising diffusion probabilistic models.

Julius Vetter1, Jakob H Macke1,2, Richard Gao1

  • 1Machine Learning in Science, University of Tübingen and Tübingen AI Center, Tübingen, Germany.

Patterns (New York, N.Y.)
|November 21, 2024
PubMed
Summary
This summary is machine-generated.

Denoising diffusion probabilistic models (DDPMs) can now generate realistic neurophysiological data. This advance offers new tools for neuroscience research, improving data analysis and synthetic data generation.

Keywords:
computational neurosciencediffusion modelsgenerative modelingmachine learningneurophysiological recordings

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

5.6K
Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.1K

Related Experiment Videos

Last Updated: Jun 7, 2025

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

9.9K
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

5.6K
Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.1K

Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Denoising diffusion probabilistic models (DDPMs) excel at generating complex data like images and audio.
  • Accurate generation of neurophysiological time series is crucial for advancing neuroscience applications.

Purpose of the Study:

  • To present a flexible DDPM-based method for modeling multichannel, densely sampled neurophysiological recordings.
  • To demonstrate the utility of DDPMs for generating realistic synthetic neurophysiological data.

Main Methods:

  • Developed a DDPM-based approach tailored for multichannel neurophysiological recordings.
  • Validated the model on diverse datasets across species and recording techniques.

Main Results:

  • DDPMs successfully generated synthetic neurophysiological data capturing key statistics (e.g., frequency spectra, phase-amplitude coupling) and fine-grained features (e.g., sharp wave ripples).
  • Generated data reflected experimental conditions.
  • Demonstrated applications in brain-state classification and missing-data imputation.

Conclusions:

  • DDPMs provide an accurate generative modeling framework for neurophysiological recordings.
  • The method offers broad utility for probabilistic generation of synthetic data in neuroscience research.