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pro vyhledávání: '"Francois Paul"'
Entrainment experiments on the vertebrate segmentation clock have revealed that embryonic oscillators actively change their internal frequency to adapt to the driving signal. This is neither consistent with a one-dimensional clock model nor with a li
Externí odkaz:
http://arxiv.org/abs/2405.05180
Autor:
François, Paul, Mochulska, Victoria
Proper vertebrae formation relies on a tissue-wide oscillator called the segmentation clock. Individual cellular oscillators in the presomitic mesoderm are modulated by intercellular coupling and external signals, leading to the propagation of oscill
Externí odkaz:
http://arxiv.org/abs/2403.00457
Autor:
Boukacem, Nacer Eddine, Leary, Allen, Thériault, Robin, Gottlieb, Felix, Mani, Madhav, François, Paul
Networks in machine learning offer examples of complex high-dimensional dynamical systems reminiscent of biological systems. Here, we study the learning dynamics of Generalized Hopfield networks, which permit a visualization of internal memories. The
Externí odkaz:
http://arxiv.org/abs/2312.03012
Autor:
Bergeron-Sandoval, Louis-Philippe, Kumar, Sandeep, Heris, Hossein Khadivi, Chang, Catherine L. A., Cornell, Caitlin E., Keller, Sarah L., François, Paul, Hendricks, Adam G., Ehrlicher, Allen J., Pappu, Rohit V., Michnick, Stephen W.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Dec . 118(50), 1-12.
Externí odkaz:
https://www.jstor.org/stable/27112717
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Akademický článek
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Physical models of biological systems can become difficult to interpret when they have a large number of parameters. But the models themselves actually depend on (i.e. are sensitive to) only a subset of those parameters. Rigorously identifying this s
Externí odkaz:
http://arxiv.org/abs/1811.10523
Publikováno v:
Phys. Rev. X 9, 031012 (2019)
Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a for
Externí odkaz:
http://arxiv.org/abs/1807.04270
Complex mathematical models of interaction networks are routinely used for prediction in systems biology. However, it is difficult to reconcile network complexities with a formal understanding of their behavior. Here, we propose a simple procedure (c
Externí odkaz:
http://arxiv.org/abs/1707.06300