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Bryan C Daniels

Showing results (1-10 of 24) with videos related to

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Nature Communications|August 22, 2015
Automated adaptive inference of phenomenological dynamical modelsBryan C Daniels, Ilya Nemenman
Nature Communications|September 2, 2021
The basis of easy controllability in Boolean networksEnrico 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-makingBryan C Daniels, Pawel Romanczuk
Plos One|March 26, 2015
Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regressionBryan C Daniels, Ilya Nemenman
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|May 24, 2011
Nucleation at the DNA supercoiling transitionBryan 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 bistabilityColin 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 dynamicsBryan C Daniels, William S Ryu, Ilya Nemenman
Plos Computational Biology|May 27, 2022
Discovering sparse control strategies in neural activityEdward D Lee, Xiaowen Chen, Bryan C Daniels
Frontiers in Neuroscience|June 22, 2017
Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-MakingBryan 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 networkDeryc T Painter, Bryan C Daniels, Manfred D Laubichler
Pageof 3

Showing results (1-10 of 24) with videos related to

Sort By:
Pageof 3
Nature Communications|August 22, 2015
Automated adaptive inference of phenomenological dynamical modelsBryan C Daniels, Ilya Nemenman
Nature Communications|September 2, 2021
The basis of easy controllability in Boolean networksEnrico 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-makingBryan C Daniels, Pawel Romanczuk
Plos One|March 26, 2015
Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regressionBryan C Daniels, Ilya Nemenman
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|May 24, 2011
Nucleation at the DNA supercoiling transitionBryan 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 bistabilityColin 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 dynamicsBryan C Daniels, William S Ryu, Ilya Nemenman
Plos Computational Biology|May 27, 2022
Discovering sparse control strategies in neural activityEdward D Lee, Xiaowen Chen, Bryan C Daniels
Frontiers in Neuroscience|June 22, 2017
Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-MakingBryan 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 networkDeryc T Painter, Bryan C Daniels, Manfred D Laubichler
Pageof 3