Hava Siegelmann to Computer Simulation
This is a "connection" page, showing publications Hava Siegelmann has written about Computer Simulation.
Connection Strength
2.201
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Amgalan A, Taylor P, Mujica-Parodi LR, Siegelmann HT. Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters. Sci Rep. 2021 03 05; 11(1):5331.
Score: 0.600
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Tal A, Peled N, Siegelmann HT. Biologically inspired load balancing mechanism in neocortical competitive learning. Front Neural Circuits. 2014; 8:18.
Score: 0.370
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Siegelmann HT. Turing on Super-Turing and adaptivity. Prog Biophys Mol Biol. 2013 Sep; 113(1):117-26.
Score: 0.347
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Cabessa J, Siegelmann HT. The computational power of interactive recurrent neural networks. Neural Comput. 2012 Apr; 24(4):996-1019.
Score: 0.320
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Olsen M, Siegelmann-Danieli N, Siegelmann HT. Dynamic computational model suggests that cellular citizenship is fundamental for selective tumor apoptosis. PLoS One. 2010 May 13; 5(5):e10637.
Score: 0.284
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Glass L, Siegelmann HT. Logical and symbolic analysis of robust biological dynamics. Curr Opin Genet Dev. 2010 Dec; 20(6):644-9.
Score: 0.073
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Roth F, Siegelmann H, Douglas RJ. The self-construction and -repair of a foraging organism by explicitly specified development from a single cell. Artif Life. 2007; 13(4):347-68.
Score: 0.056
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Sivan S, Filo O, Siegelmann H. Application of expert networks for predicting proteins secondary structure. Biomol Eng. 2007 Jun; 24(2):237-43.
Score: 0.056
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Leise T, Siegelmann H. Dynamics of a multistage circadian system. J Biol Rhythms. 2006 Aug; 21(4):314-23.
Score: 0.055
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Gavald? R, Siegelmann HT. Discontinuities in recurrent neural networks. Neural Comput. 1999 Apr 01; 11(3):715-46.
Score: 0.033
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Lange DH, Siegelmann HT, Pratt H, Inbar GF. Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials. IEEE Trans Biomed Eng. 2000 Jun; 47(6):822-6.
Score: 0.009