Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition

Brunton, Bingni W.; Johnson, L. Clark; Ojemann, Jeffrey G.; Kutz, J. Nathan

doi:10.1016/j.jneumeth.2015.10.010

Cited by 398 publications

(303 citation statements)

References 48 publications

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“…Indeed, the impact that DMD is having on complex fluid flows is already known and has already been mentioned in the introduction. Fields like neuroscience, which are rich in multi-scale, complex dynamics are also ideal candidates for exploration using the mrDMD infrastructure as DMD has already had recent demonstrated success in this arena [23]. The outlook of these techniques is highlighted here:…”

Section: Discussion and Outlookmentioning

confidence: 99%

Multiresolution Dynamic Mode Decomposition

Kutz

Brunton

2016

SIAM J. Appl. Dyn. Syst.

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331

186

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We demonstrate that the integration of the recently developed dynamic mode decomposition (DMD) with a multi-resolution analysis allows for a decomposition method capable of robustly separating complex systems into a hierarchy of multi-resolution time-scale components. A one-level separation allows for background (low-rank) and foreground (sparse) separation of dynamical data, or robust principal component analysis. The multi-resolution dynamic mode decomposition is capable of characterizing nonlinear dynamical systems in an equation-free manner by recursively decomposing the state of the system into low-rank terms whose temporal coefficients in time are known. DMD modes with temporal frequencies near the origin (zero-modes) are interpreted as background (low-rank) portions of the given dynamics, and the terms with temporal frequencies bounded away from the origin are their sparse counterparts. The multi-resolution dynamic mode decomposition (mrDMD) method is demonstrated on several examples involving multi-scale dynamical data, showing excellent decomposition results, including sifting the El Niño mode from ocean temperature data. It is further applied to decompose a video data set into separate objects moving at different rates against a slowly varying background. These examples show that the decomposition is an effective dynamical systems tool for data-driven discovery.

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Section: Discussion and Outlookmentioning

confidence: 99%

Multiresolution Dynamic Mode Decomposition

Kutz

Brunton

2016

SIAM J. Appl. Dyn. Syst.

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331

186

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“…Dynamic mode decomposition [14][15][16][17] is a new technique used successfully for mode analysis and model reduction in many fields such as fluid mechanics [18], neuroscience [19], video streaming, and pattern recognition [20]. There is a significant number of papers dedicated to the method.…”

Section: Dynamic Mode Decompositionmentioning

confidence: 99%

“…Developed initially by the fluid mechanics community to address turbulent flows, it has proven to be a successful technique in many different areas [18][19][20]. One important feature of this method is the direct calculation of mode dynamics.…”

Section: Introductionmentioning

confidence: 99%

Simulation of transverse modes with their intrinsic Landau damping for bunched beams in the presence of space charge

Macridin

Burov

Stern

et al. 2015

Phys. Rev. ST Accel. Beams

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Transverse dipole modes in bunches with space charge are simulated using the SYNERGIA accelerator modeling package and analyzed with dynamic mode decomposition. The properties of the first three space charge modes, including their shape, damping rates, and tune shifts are described over the entire range of space charge strength. The intrinsic Landau damping predicted and estimated in 2009 by one of the authors is confirmed with a reasonable scaling factor of ≃2.4. For the KV distribution, very good agreement with PATRIC simulations performed by Kornilov and Boine-Frankenheim is obtained.

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“…These modes essentially capture different large-scale to smallscale structures (sparse components) including a background structure (low-rank model) [7]. DMD has gained significant applications in various fields [2,3,16], including for detecting spoof samples from facial authentication video data sets [33] and for detecting spoofed finger-vein images [31]. The advantage of this method is its ability to identify regions of dominant motion in an image sequence in a completely data-driven manner without relying on any prior assumptions about the patterns of behaviour within the data.…”

Section: Motivation: Dynamic Mode Decomposition (Dmd)mentioning

confidence: 99%

Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

Tirunagari

Poh

Wells

et al. 2017

Machine Vision and Applications

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Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition

Abstract: The resulting analysis combines key features of performing PCA in space and power spectral analysis in time, making it particularly suitable for analyzing large-scale neural recordings.

Cited by 398 publications

References 48 publications

Multiresolution Dynamic Mode Decomposition

Multiresolution Dynamic Mode Decomposition

Simulation of transverse modes with their intrinsic Landau damping for bunched beams in the presence of space charge

Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

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