Articles with public access mandates - Bingni W. BruntonLearn more
Available somewhere: 64
Rats and Humans Can Optimally Accumulate Evidence for Decision-Making
BW Brunton, MM Botvinick, CD Brody
Science 340 (6128), 95-98, 2013
Mandates: Howard Hughes Medical Institute
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature Communications 8, 2017
Mandates: US Department of Defense
Distinct relationships of parietal and prefrontal cortices to evidence accumulation
TD Hanks, CD Kopec, BW Brunton, CA Duan, JC Erlich, CD Brody
Nature 520 (7546), 220-223, 2015
Mandates: US National Institutes of Health, Howard Hughes Medical Institute
Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
BW Brunton, LA Johnson, JG Ojemann, JN Kutz
Journal of neuroscience methods 258, 1-15, 2016
Mandates: US National Science Foundation, US National Institutes of Health
Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns
K Manohar, BW Brunton, JN Kutz, SL Brunton
IEEE Control Systems 38 (3), 63-86, 2018
Mandates: US Department of Defense
Cell shape and cell-wall organization in Gram-negative bacteria
KC Huang, R Mukhopadhyay, B Wen, Z Gitai, NS Wingreen
Proceedings of the National Academy of Sciences 105 (49), 19282-19287, 2008
Mandates: US National Institutes of Health
Anipose: a toolkit for robust markerless 3D pose estimation
P Karashchuk, KL Rupp, ES Dickinson, S Walling-Bell, E Sanders, E Azim, ...
Cell Reports 36 (13), 109730, 2021
Mandates: US National Science Foundation, US National Institutes of Health
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
U Fasel, JN Kutz, BW Brunton, SL Brunton
Proceedings of the Royal Society A 478 (2260), 20210904, 2022
Mandates: US National Science Foundation, US Department of Defense
Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat
JC Erlich, BW Brunton, CA Duan, TD Hanks, CD Brody
Elife 4, e05457, 2015
Mandates: US National Institutes of Health, Howard Hughes Medical Institute
Sparse Sensor Placement Optimization for Classification
BW Brunton, SL Brunton, JL Proctor, JN Kutz
SIAM Journal on Applied Mathematics 76 (5), 2099-2122, 2016
Mandates: US National Science Foundation, Bill & Melinda Gates Foundation
Cortical and Subcortical Contributions to Short-Term Memory for Orienting Movements
CD Kopec, JC Erlich, BW Brunton, K Deisseroth, CD Brody
Neuron 88 (2), 367-377, 2015
Mandates: US National Institutes of Health
Numerical differentiation of noisy data: A unifying multi-objective optimization framework
F van Breugel, JN Kutz, BW Brunton
IEEE Access 8, 196865 - 196877, 2020
Mandates: US Department of Defense, US National Institutes of Health
Learning dominant physical processes with data-driven balance models
JL Callaham, JV Koch, BW Brunton, JN Kutz, SL Brunton
Nature Communications 12 (1), 1-10, 2021
Mandates: US Department of Defense
Sindy with control: A tutorial
U Fasel, E Kaiser, JN Kutz, BW Brunton, SL Brunton
2021 60th IEEE Conference on Decision and Control (CDC), 16-21, 2021
Mandates: US Department of Defense
Centering data improves the dynamic mode decomposition
SM Hirsh, KD Harris, JN Kutz, BW Brunton
SIAM Journal on Applied Dynamical Systems 19 (3), 1920-1955, 2020
Mandates: US National Science Foundation, US Department of Defense
Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data
TL Mohren, TL Daniel, SL Brunton, BW Brunton
Proceedings of the National Academy of Science 115 (42), 10564--10569, 2018
Mandates: US Department of Defense
Data-Driven Methods in Fluid Dynamics: Sparse Classification from Experimental Data
Z Bai, SL Brunton, BW Brunton, JN Kutz, E Kaiser, A Spohn, BR Noack
Whither Turbulence and Big Data in the 21st Century?, 323-342, 2017
Mandates: US Department of Energy, US Department of Defense
AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video
XRN Wang, A Farhadi, R Rao, B Brunton
AAAI Conference on Artificial Intelligence, 2524, 2018
Mandates: US National Science Foundation, Gordon and Betty Moore Foundation
Structured Time-Delay Models for Dynamical Systems with Connections to Frenet-Serret Frame
SM Hirsh, SM Ichinaga, SL Brunton, JN Kutz, BW Brunton
Proc. R. Soc. A. 477, 20210097, 2021
Mandates: US National Science Foundation, US Department of Defense
Generalized neural decoders for transfer learning across participants and recording modalities
SM Peterson, Z Steine-Hanson, N Davis, RPN Rao, BW Brunton
Journal of Neural Engineering, 2021
Mandates: US National Science Foundation, US Department of Defense
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