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Sarthak Chandra

Showing results (1-10 of 12) with videos related to

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Chaos (Woodbury, N.Y.)|April 1, 2019
Observing microscopic transitions from macroscopic bursts: Instability-mediated resetting in the incoherent regime of the D-dimensional generalized Kuramoto modelSarthak Chandra, Edward Ott
Proceedings of the National Academy of Sciences of the United States of America|March 17, 2023
Network inference from short, noisy, low time-resolution, partial measurements: Application to <i>C. elegans</i> neuronal calcium dynamicsAmitava Banerjee, Sarthak Chandra, Edward Ott
Nature|February 19, 2025
Global modules robustly emerge from local interactions and smooth gradientsMikail Khona, Sarthak Chandra, Ila Fiete
Chaos (Woodbury, N.Y.)|June 4, 2019
Complexity reduction ansatz for systems of interacting orientable agents: Beyond the Kuramoto modelSarthak Chandra, Michelle Girvan, Edward Ott
Physical Review. E|July 22, 2020
Critical network cascades with re-excitable nodes: Why treelike approximations usually work, when they break down, and how to correct themSarthak Chandra, Edward Ott, Michelle Girvan
Nature|January 15, 2025
Episodic and associative memory from spatial scaffolds in the hippocampusSarthak Chandra, Sugandha Sharma, Rishidev Chaudhuri, et al.
Neural Computation|September 19, 2023
Winning the Lottery With Neural Connectivity Constraints: Faster Learning Across Cognitive Tasks With Spatially Constrained Sparse RNNsMikail Khona, Sarthak Chandra, Joy J Ma, et al.
Chaos (Woodbury, N.Y.)|April 3, 2017
Modeling the network dynamics of pulse-coupled neuronsSarthak Chandra, David Hathcock, Kimberly Crain, et al.
Arxiv|September 29, 2025
Characterizing control between interacting subsystems with deep Jacobian estimationAdam J Eisen, Mitchell Ostrow, Sarthak Chandra, et al.
Chaos (Woodbury, N.Y.)|January 8, 2020
Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based modelJaideep Pathak, Alexander Wikner, Rebeckah Fussell, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Chaos (Woodbury, N.Y.)|April 1, 2019
Observing microscopic transitions from macroscopic bursts: Instability-mediated resetting in the incoherent regime of the D-dimensional generalized Kuramoto modelSarthak Chandra, Edward Ott
Proceedings of the National Academy of Sciences of the United States of America|March 17, 2023
Network inference from short, noisy, low time-resolution, partial measurements: Application to <i>C. elegans</i> neuronal calcium dynamicsAmitava Banerjee, Sarthak Chandra, Edward Ott
Nature|February 19, 2025
Global modules robustly emerge from local interactions and smooth gradientsMikail Khona, Sarthak Chandra, Ila Fiete
Chaos (Woodbury, N.Y.)|June 4, 2019
Complexity reduction ansatz for systems of interacting orientable agents: Beyond the Kuramoto modelSarthak Chandra, Michelle Girvan, Edward Ott
Physical Review. E|July 22, 2020
Critical network cascades with re-excitable nodes: Why treelike approximations usually work, when they break down, and how to correct themSarthak Chandra, Edward Ott, Michelle Girvan
Nature|January 15, 2025
Episodic and associative memory from spatial scaffolds in the hippocampusSarthak Chandra, Sugandha Sharma, Rishidev Chaudhuri, et al.
Neural Computation|September 19, 2023
Winning the Lottery With Neural Connectivity Constraints: Faster Learning Across Cognitive Tasks With Spatially Constrained Sparse RNNsMikail Khona, Sarthak Chandra, Joy J Ma, et al.
Chaos (Woodbury, N.Y.)|April 3, 2017
Modeling the network dynamics of pulse-coupled neuronsSarthak Chandra, David Hathcock, Kimberly Crain, et al.
Arxiv|September 29, 2025
Characterizing control between interacting subsystems with deep Jacobian estimationAdam J Eisen, Mitchell Ostrow, Sarthak Chandra, et al.
Chaos (Woodbury, N.Y.)|January 8, 2020
Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based modelJaideep Pathak, Alexander Wikner, Rebeckah Fussell, et al.
Pageof 2