Hava Siegelmann to Models, Neurological
This is a "connection" page, showing publications Hava Siegelmann has written about Models, Neurological.
Connection Strength
2.307
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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: 0.689
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Cabessa J, Siegelmann HT. The super-Turing computational power of plastic recurrent neural networks. Int J Neural Syst. 2014 Dec; 24(8):1450029.
Score: 0.443
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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: 0.424
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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: 0.367
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Tsuda B, Tye KM, Siegelmann HT, Sejnowski TJ. A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex. Proc Natl Acad Sci U S A. 2020 11 24; 117(47):29872-29882.
Score: 0.168
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Lipson H, Siegelmann HT. Clustering irregular shapes using high-order neurons. Neural Comput. 2000 Oct; 12(10):2331-53.
Score: 0.167
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Saunders DJ, Patel D, Hazan H, Siegelmann HT, Kozma R. Locally connected spiking neural networks for unsupervised feature learning. Neural Netw. 2019 Nov; 119:332-340.
Score: 0.039
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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: 0.010