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Hava Siegelmann PhD

TitleProfessor
InstitutionUniversity of Massachusetts Amherst
DepartmentCollege of Natural Sciences
Address242 Computer Science Building
Amherst, MA 01003
Phone413-545-2744
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    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
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    1. 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 Nov 24; 117(47):29872-29882. PMID: 33154155.
      View in: PubMed
    2. van de Ven GM, Siegelmann HT, Tolias AS. Brain-inspired replay for continual learning with artificial neural networks. Nat Commun. 2020 Aug 13; 11(1):4069. PMID: 32792531.
      View in: PubMed
    3. Shifrin M, Siegelmann H. Near-optimal insulin treatment for diabetes patients: A machine learning approach. Artif Intell Med. 2020 Jul; 107:101917. PMID: 32828456.
      View in: PubMed
    4. Rietman EA, Taylor S, Siegelmann HT, Deriu MA, Cavaglia M, Tuszynski JA. Using the Gibbs Function as a Measure of Human Brain Development Trends from Fetal Stage to Advanced Age. Int J Mol Sci. 2020 Feb 07; 21(3). PMID: 32046179.
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    5. Saunders DJ, Patel D, Hazan H, Siegelmann HT, Kozma R. Locally connected spiking neural networks for unsupervised feature learning. Neural Netw. 2019 Aug 26; 119:332-340. PMID: 31499357.
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    6. Patel D, Hazan H, Saunders DJ, Siegelmann HT, Kozma R. Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game. Neural Netw. 2019 Aug 25. PMID: 31500931.
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    7. Hazan H, Saunders DJ, Khan H, Patel D, Sanghavi DT, Siegelmann HT, Kozma R. BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python. Front Neuroinform. 2018; 12:89. PMID: 30631269.
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    8. McGuire SH, Rietman EA, Siegelmann H, Tuszynski JA. Gibbs free energy as a measure of complexity correlates with time within C. elegans embryonic development. J Biol Phys. 2017 Dec; 43(4):551-563. PMID: 28929407.
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    9. Burroni J, Taylor P, Corey C, Vachnadze T, Siegelmann HT. Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks. Front Neurosci. 2017; 11:80. PMID: 28289370.
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    10. Taylor P, Hobbs JN, Burroni J, Siegelmann HT. The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions. Sci Rep. 2015 Dec 16; 5:18112. PMID: 26669858.
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    11. Cabessa J, Siegelmann HT. The super-Turing computational power of plastic recurrent neural networks. Int J Neural Syst. 2014 Dec; 24(8):1450029. PMID: 25354762.
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    12. Tal A, Peled N, Siegelmann HT. Biologically inspired load balancing mechanism in neocortical competitive learning. Front Neural Circuits. 2014; 8:18. PMID: 24653679.
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    13. Nowicki D, Verga P, Siegelmann H. Modeling reconsolidation in kernel associative memory. PLoS One. 2013; 8(8):e68189. PMID: 23936300.
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    14. Siegelmann HT. Turing on Super-Turing and adaptivity. Prog Biophys Mol Biol. 2013 Sep; 113(1):117-26. PMID: 23583352.
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    15. Thivierge JP, Minai A, Siegelmann H, Alippi C, Geourgiopoulos M. A year of neural network research: special issue on the 2011 International Joint Conference on Neural Networks. Neural Netw. 2012 Aug; 32:1-2. PMID: 22551620.
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    16. Cabessa J, Siegelmann HT. The computational power of interactive recurrent neural networks. Neural Comput. 2012 Apr; 24(4):996-1019. PMID: 22295978.
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    17. Glass L, Siegelmann HT. Logical and symbolic analysis of robust biological dynamics. Curr Opin Genet Dev. 2010 Dec; 20(6):644-9. PMID: 20961750.
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    18. Siegelmann HT. Complex systems science and brain dynamics. Front Comput Neurosci. 2010; 4. PMID: 20877423.
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    19. Siegelmann HT, Holzman LE. Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference. Chaos. 2010 Sep; 20(3):037112. PMID: 20887078.
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    20. Nowicki D, Siegelmann H. Flexible kernel memory. PLoS One. 2010; 5(6):e10955. PMID: 20552013.
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    21. Olsen M, Siegelmann-Danieli N, Siegelmann HT. Dynamic computational model suggests that cellular citizenship is fundamental for selective tumor apoptosis. PLoS One. 2010; 5(5):e10637. PMID: 20498709.
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    22. Tu K, Cooper DG, Siegelmann HT. Memory reconsolidation for natural language processing. Cogn Neurodyn. 2009 Dec; 3(4):365-72. PMID: 19862641.
      View in: PubMed
    23. Pietrzykowski AZ, Friesen RM, Martin GE, Puig SI, Nowak CL, Wynne PM, Siegelmann HT, Treistman SN. Posttranscriptional regulation of BK channel splice variant stability by miR-9 underlies neuroadaptation to alcohol. Neuron. 2008 Jul 31; 59(2):274-87. PMID: 18667155.
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    24. Lu S, Becker KA, Hagen MJ, Yan H, Roberts AL, Mathews LA, Schneider SS, Siegelmann HT, MacBeth KJ, Tirrell SM, Blanchard JL, Jerry DJ. Transcriptional responses to estrogen and progesterone in mammary gland identify networks regulating p53 activity. Endocrinology. 2008 Oct; 149(10):4809-20. PMID: 18556351.
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    25. 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. PMID: 17716016.
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    26. Sivan S, Filo O, Siegelmann H. Application of expert networks for predicting proteins secondary structure. Biomol Eng. 2007 Jun; 24(2):237-43. PMID: 17236807.
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    27. Leise T, Siegelmann H. Dynamics of a multistage circadian system. J Biol Rhythms. 2006 Aug; 21(4):314-23. PMID: 16864651.
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    28. Ben-Hur A, Siegelmann HT. Computation in gene networks. Chaos. 2004 Mar; 14(1):145-51. PMID: 15003055.
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    29. Eldar S, Siegelmann HT, Buzaglo D, Matter I, Cohen A, Sabo E, Abrahamson J. Conversion of laparoscopic cholecystectomy to open cholecystectomy in acute cholecystitis: artificial neural networks improve the prediction of conversion. World J Surg. 2002 Jan; 26(1):79-85. PMID: 11898038.
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    30. Edwards R, Siegelmann HT, Aziza K, Glass L. Symbolic dynamics and computation in model gene networks. Chaos. 2001 Mar; 11(1):160-169. PMID: 12779450.
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    31. Lipson H, Siegelmann HT. Clustering irregular shapes using high-order neurons. Neural Comput. 2000 Oct; 12(10):2331-53. PMID: 11032037.
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    32. 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. PMID: 10833858.
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    33. Siegelmann HT, Margenstern M. Nine switch-affine neurons suffice for Turing universality. Neural Netw. 1999 Jun; 12(4-5):593-600. PMID: 12662670.
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    34. Gavaldá R, Siegelmann HT. Discontinuities in recurrent neural networks. Neural Comput. 1999 Apr 1; 11(3):715-46. PMID: 10085427.
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    35. Siegelmann HT, Horne BG, Giles CL. Computational capabilities of recurrent NARX neural networks. IEEE Trans Syst Man Cybern B Cybern. 1997; 27(2):208-15. PMID: 18255858.
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    36. Siegelmann HT. Computation beyond the turing limit. Science. 1995 Apr 28; 268(5210):545-8. PMID: 17756722.
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    37. Dasgupta B, Siegelmann HT, Sontag E. On the complexity of training neural networks with continuous activation functions. IEEE Trans Neural Netw. 1995; 6(6):1490-504. PMID: 18263442.
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