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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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    PMC Citations indicate the number of times the publication was cited by articles in PubMed Central, and the Altmetric score represents citations in news articles and social media. (Note that publications are often cited in additional ways that are not shown here.) Fields are based on how the National Library of Medicine (NLM) classifies the publication's journal and might not represent the specific topic of the publication. Translation tags are based on the publication type and the MeSH terms NLM assigns to the publication. Some publications (especially newer ones and publications not in PubMed) might not yet be assigned Field or Translation tags.) Click a Field or Translation tag to filter the publications.
    1. Abookasis D, Shemesh D, Litwin A, Siegelmann HT, Didkovsky E, Ad-El DD. Single probe light reflectance spectroscopy and parameter spectrum feature extraction in experimental skin cancer detection and classification. J Biophotonics. 2023 Apr 20; e202300001. PMID: 37078262.
      Citations:    Fields:    
    2. Kohan A, Rietman EA, Siegelmann HT. Signal Propagation: The Framework for Learning and Inference in a Forward Pass. IEEE Trans Neural Netw Learn Syst. 2023 Jan 27; PP. PMID: 37022224.
      Citations:    Fields:    
    3. Hayes TL, Krishnan GP, Bazhenov M, Siegelmann HT, Sejnowski TJ, Kanan C. Replay in Deep Learning: Current Approaches and Missing Biological Elements. Neural Comput. 2021 10 12; 33(11):2908-2950. PMID: 34474476.
      Citations: 9     Fields:    Translation:Animals
    4. 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. PMID: 33674620.
      Citations:    Fields:    Translation:HumansCells
    5. 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. PMID: 33154155.
      Citations: 4     Fields:    Translation:Humans
    6. van de Ven GM, Siegelmann HT, Tolias AS. Brain-inspired replay for continual learning with artificial neural networks. Nat Commun. 2020 08 13; 11(1):4069. PMID: 32792531.
      Citations: 16     Fields:    
    7. Shifrin M, Siegelmann H. Near-optimal insulin treatment for diabetes patients: A machine learning approach. Artif Intell Med. 2020 07; 107:101917. PMID: 32828456.
      Citations: 3     Fields:    Translation:Humans
    8. 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.
      Citations: 1     Fields:    Translation:HumansCells
    9. 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. PMID: 31499357.
      Citations: 4     Fields:    Translation:Cells
    10. 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 Dec; 120:108-115. PMID: 31500931.
      Citations: 5     Fields:    
    11. 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.
      Citations:    
    12. 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.
      Citations: 1     Fields:    Translation:Animals
    13. 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.
      Citations:    
    14. 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.
      Citations: 24     Fields:    Translation:Humans
    15. 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.
      Citations: 2     Fields:    
    16. Tal A, Peled N, Siegelmann HT. Biologically inspired load balancing mechanism in neocortical competitive learning. Front Neural Circuits. 2014; 8:18. PMID: 24653679.
      Citations: 1     Fields:    Translation:AnimalsCells
    17. Nowicki D, Verga P, Siegelmann H. Modeling reconsolidation in kernel associative memory. PLoS One. 2013; 8(8):e68189. PMID: 23936300.
      Citations: 2     Fields:    Translation:Humans
    18. Siegelmann HT. Turing on Super-Turing and adaptivity. Prog Biophys Mol Biol. 2013 Sep; 113(1):117-26. PMID: 23583352.
      Citations: 3     Fields:    
    19. 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.
      Citations:    Fields:    
    20. Cabessa J, Siegelmann HT. The computational power of interactive recurrent neural networks. Neural Comput. 2012 Apr; 24(4):996-1019. PMID: 22295978.
      Citations: 2     Fields:    Translation:Cells
    21. Glass L, Siegelmann HT. Logical and symbolic analysis of robust biological dynamics. Curr Opin Genet Dev. 2010 Dec; 20(6):644-9. PMID: 20961750.
      Citations: 4     Fields:    Translation:HumansAnimals
    22. Siegelmann HT. Complex systems science and brain dynamics. Front Comput Neurosci. 2010; 4. PMID: 20877423.
      Citations:    
    23. Siegelmann HT, Holzman LE. Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference. Chaos. 2010 Sep; 20(3):037112. PMID: 20887078.
      Citations: 4     Fields:    Translation:HumansCells
    24. Nowicki D, Siegelmann H. Flexible kernel memory. PLoS One. 2010 Jun 11; 5(6):e10955. PMID: 20552013.
      Citations: 2     Fields:    
    25. 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. PMID: 20498709.
      Citations:    Fields:    Translation:Humans
    26. Tu K, Cooper DG, Siegelmann HT. Memory reconsolidation for natural language processing. Cogn Neurodyn. 2009 Dec; 3(4):365-72. PMID: 19862641.
      Citations:    
    27. 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.
      Citations: 199     Fields:    Translation:HumansAnimalsCells
    28. 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.
      Citations: 20     Fields:    Translation:HumansAnimalsCells
    29. 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.
      Citations: 3     Fields:    
    30. 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.
      Citations:    Fields:    Translation:Cells
    31. Leise T, Siegelmann H. Dynamics of a multistage circadian system. J Biol Rhythms. 2006 Aug; 21(4):314-23. PMID: 16864651.
      Citations: 7     Fields:    Translation:HumansAnimals
    32. Ben-Hur A, Siegelmann HT. Computation in gene networks. Chaos. 2004 Mar; 14(1):145-51. PMID: 15003055.
      Citations: 4     Fields:    
    33. 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.
      Citations: 6     Fields:    Translation:Humans
    34. 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.
      Citations: 11     Fields:    
    35. Lipson H, Siegelmann HT. Clustering irregular shapes using high-order neurons. Neural Comput. 2000 Oct; 12(10):2331-53. PMID: 11032037.
      Citations:    Fields:    Translation:Cells
    36. 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.
      Citations: 2     Fields:    Translation:Humans
    37. Siegelmann HT, Margenstern M. Nine switch-affine neurons suffice for Turing universality. Neural Netw. 1999 Jun; 12(4-5):593-600. PMID: 12662670.
      Citations:    Fields:    
    38. Gavald? R, Siegelmann HT. Discontinuities in recurrent neural networks. Neural Comput. 1999 Apr 01; 11(3):715-46. PMID: 10085427.
      Citations:    Fields:    
    39. 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.
      Citations: 8     Fields:    
    40. Siegelmann HT. Computation beyond the turing limit. Science. 1995 Apr 28; 268(5210):545-8. PMID: 17756722.
      Citations: 16     Fields:    
    41. 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.
      Citations:    Fields:    
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