Generating realistic neurophysiological time series with denoising diffusion probabilistic models

Julius Vetter; Jakob H. Macke; Richard Gao

doi:10.1016/j.patter.2024.101047

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Generating realistic neurophysiological time series with denoising diffusion probabilistic models

Julius Vetter Jakob H. Macke Richard Gao

Year: 2024 Journal: Patterns Vol: 5 (9)Pages: 101047-101047 Publisher: Elsevier BV

DOI: 10.1016/j.patter.2024.101047

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Keywords:

Computer science Probabilistic logic Series (stratigraphy) Neurophysiology Diffusion Noise reduction Artificial intelligence Psychology Neuroscience Physics Biology

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Topics

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

Functional Brain Connectivity Studies

Life Sciences → Neuroscience → Cognitive Neuroscience

Neural dynamics and brain function

Life Sciences → Neuroscience → Cognitive Neuroscience

Generating realistic neurophysiological time series with denoising diffusion probabilistic models

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