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Nature Communications
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August 22, 2015
Automated adaptive inference of phenomenological dynamical models
Bryan C Daniels, Ilya Nemenman
Nature Communications
|
September 2, 2021
The basis of easy controllability in Boolean networks
Enrico Borriello, Bryan C Daniels
Theory in Biosciences = Theorie in Den Biowissenschaften
|
February 26, 2021
Quantifying the impact of network structure on speed and accuracy in collective decision-making
Bryan C Daniels, Pawel Romanczuk
Plos One
|
March 26, 2015
Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression
Bryan C Daniels, Ilya Nemenman
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|
May 24, 2011
Nucleation at the DNA supercoiling transition
Bryan C Daniels, James P Sethna
Journal of the Royal Society, Interface
|
February 10, 2026
Tuning regimes in ant foraging dynamics depend on the existence of bistability
Colin M Lynch, Bryan C Daniels
Proceedings of the National Academy of Sciences of the United States of America
|
March 24, 2019
Automated, predictive, and interpretable inference of <i>Caenorhabditis elegans</i> escape dynamics
Bryan C Daniels, William S Ryu, Ilya Nemenman
Plos Computational Biology
|
May 27, 2022
Discovering sparse control strategies in neural activity
Edward D Lee, Xiaowen Chen, Bryan C Daniels
Frontiers in Neuroscience
|
June 22, 2017
Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making
Bryan C Daniels, Jessica C Flack, David C Krakauer
Theory in Biosciences = Theorie in Den Biowissenschaften
|
November 13, 2021
Innovations are disproportionately likely in the periphery of a scientific network
Deryc T Painter, Bryan C Daniels, Manfred D Laubichler
Page
of 3
Search research articles
Search
Showing results (1-10 of 24) with videos related to
Sort By:
Page
of 3
Nature Communications
|
August 22, 2015
Automated adaptive inference of phenomenological dynamical models
Bryan C Daniels, Ilya Nemenman
Nature Communications
|
September 2, 2021
The basis of easy controllability in Boolean networks
Enrico Borriello, Bryan C Daniels
Theory in Biosciences = Theorie in Den Biowissenschaften
|
February 26, 2021
Quantifying the impact of network structure on speed and accuracy in collective decision-making
Bryan C Daniels, Pawel Romanczuk
Plos One
|
March 26, 2015
Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression
Bryan C Daniels, Ilya Nemenman
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|
May 24, 2011
Nucleation at the DNA supercoiling transition
Bryan C Daniels, James P Sethna
Journal of the Royal Society, Interface
|
February 10, 2026
Tuning regimes in ant foraging dynamics depend on the existence of bistability
Colin M Lynch, Bryan C Daniels
Proceedings of the National Academy of Sciences of the United States of America
|
March 24, 2019
Automated, predictive, and interpretable inference of <i>Caenorhabditis elegans</i> escape dynamics
Bryan C Daniels, William S Ryu, Ilya Nemenman
Plos Computational Biology
|
May 27, 2022
Discovering sparse control strategies in neural activity
Edward D Lee, Xiaowen Chen, Bryan C Daniels
Frontiers in Neuroscience
|
June 22, 2017
Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making
Bryan C Daniels, Jessica C Flack, David C Krakauer
Theory in Biosciences = Theorie in Den Biowissenschaften
|
November 13, 2021
Innovations are disproportionately likely in the periphery of a scientific network
Deryc T Painter, Bryan C Daniels, Manfred D Laubichler
Page
of 3