Hava Siegelmann

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Hava Siegelmann is a professor of computer science working in the areas of neuroscience, system biology and biomedical engineering in the school of Computer Science and the Program of Neuroscience and Behavior at the University of Massachusetts Amherst and is the director of the school's Biologically Inspired Neural and Dynamical Systems Lab.


Siegelmann in an American Computer Scientist, who founded the field of Super-Turing Computation. She earned her PhD at Rutgers University [1993] in New Jersey [1]

In the early 1990s, together with Eduardo D. Sontag, Siegelmann proposed a new computational model, the Artificial Recurrent Neural Network (ARNN), which demonstrated both practical and mathematical utilities. They showed how such interconnections provide very significant computation, and proved mathematically that ARNN have very well-defined computational power that is beyond the classical Universal Turing Machine. Her initial publications on the computational power of Neural Networks culminated in a sole-authored paper in Science[2][3] and her monograph, "Neural Networks and Analog Computation: Beyond the Turing Limit". Siegelmann demonstrates in her paper in Science [2] that all chaotic systems (that cannot be described by Turing computation) are now described by the Super-Turing model. This is significant since many biological systems not describable by standard means (e.g., heart, brain) can be described as a chaotic system and can now be modeled mathematically.[4][5]

The theory of Super-Turing computation has attracted followers beyond Computer Science from Physics, Biology, and Medicine.[6][7][8] Siegelmann is also one of the originators of the Support Vector Clustering http://www.scholarpedia.org/article/Support_vector_clustering, a widely used algorithm in industry, together with Vladimir Vapnik and colleagues.[9] Siegelmann also introduced a new notion in the field of Dynamical Systems.,[10] which describes diseases in the terminology and analysis of dynamical system theory, meaning that in treating disorders, it is too limiting to seek only to repair primary causes of the disorder; any method of returning system dynamics to the balanced range, even under physiological challenges (e.g., by repairing the primary source, activating secondary pathways, or inserting specialized signaling), can ameliorate the system and be extremely beneficial to healing. Employing this new concept, she revealed the source of disturbance during shift work and travel leading to jet-lag[11] and is currently studying human memory and cancer [12] in this light.

Siegelmann has been active throughout her career in advancing and supporting minorities and women in the fields of Computer Science and Engineering. Among other roles, Siegelmann is on the governing board of the International Neural Networks Society and has served as Program Chair of the 2011 International Joint Conference on Neural Networks.



  • J. Cabessa and H. T. Siegelmann, "The Computational Power of Interactive Recurrent Neural Networks," Neural Computation. 2012, 24(4): 996-1019.
  • H.T. Siegelmann and L.E. Holtzman, "Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference," Chaos: Focus issue: Intrinsic and Designed Computation: Information Processing in Dynamical Systems 20 (3): DOI: 10.1063/1.3491237, September 2010. (7 pages)
  • D. Nowicki and H.T. Siegelmann, “Flexible Kernel Memory,” PLOS One 5: e10955, June 2010.
  • M.M. Olsen, N. Siegelmann-Danieli, H.T. Siegelmann. “Dynamic Computational Model Suggests that Cellular Citizenship is Fundamental for Selective Tumor Apoptosis,” PLoS One 5(5):e10637, May 2010.
  • A. Z. Pietrzykowski, R. M. Friesen, G. E. Martin, S.I. Puig, C. L. Nowak, P. M. Wynne, H. T. Siegelmann, S. N. Treistman, “Post-transcriptional regulation of BK channel splice variant stability by miR-9 underlies neuroadaptation to alcohol,” Neuron 59, July 2008: 274-287.
  • Lu, S., Becker, K.A., Hagen, M.J., Yan, H., Roberts, A.L., Mathews, L.A., Schneider, S.S., Siegelmann, H.T., Tirrell, S.M., MacBeth, K.J., Blanchard, J.L. and Jerry, D.J., “Transcriptional responses to estrogen and progesterone in Mammary gland identify networks regulating p53 activity,” Endocrinology 149(10), June 2008: 4809-4820.
  • H.T. Siegelmann, “Analog-Symbolic Memory that Tracks via Reconsolidation,” Physica D: Nonlinear Phenomena 237 (9), 2008: 1207-1214.
  • F. Roth, H. Siegelmann, R. J. Douglas. “The Self-Construction and -Repair of a Foraging Organism by Explicitly Specified Development from a Single Cell,” Artificial Life 13(4), 2007: 347-368.
  • T. Leise and H.T. Siegelmann, “Dynamics of a multistage circadian system,” Journal of Biological Rhythms 21(4), August 2006: 314-323.
  • O. Loureiro, and H. Siegelmann, "Introducing an Active Cluster-Based Information Retrieval Paradigm," Journal of the American Society for Information Science and Technology 56(10), August 2005: 1024-1030.
  • A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik, “Support vector clustering,” Journal of Machine Learning Research 2, 2001: 125-137.
  • H.T. Siegelmann, A. Ben-Hur and S. Fishman, “Computational Complexity for Continuous Time Dynamics,” Physical Review Letters, 83(7), 1999: 1463-1466.
  • H.T. Siegelmann and S. Fishman, “Computation by Dynamical Systems,” Physica D 120, 1998 (1-2): 214-235.
  • H.T. Siegelmann, “Computation Beyond the Turing Limit,” Science 238(28), April 1995: 632-637.

Partial List of Applications[edit]

  • S. Sivan, O. Filo and H. Siegelman, “Application of Expert Networks for Predicting Proteins Secondary Structure,” Biomolecular Engineering 24(2), June 2007: 237-243.
  • S Eldar, H. T. Siegelmann, D. Buzaglo, I. Matter, A. Cohen, E. Sabo, J. Abrahamson, “Conversion of Laparoscopic Cholecystectomy to open cholecystectomy in acute cholecystitis: Artificial neural networks improve the prediction of conversion,” World Journal of Surgery 26(1), Jan 2002: 79-85.
  • D. Lange, H.T. Siegelmann, H. Pratt, and G.F. Inbar, “Overcoming Selective Ensemble Averaging: Unsupervised Identification of Event Related Brain Potentials.” IEEE Transactions on Biomedical Engineering 47(6), June 2000: 822-826.
  • H. Karniely and H.T. Siegelmann, “Sensor Registration Using Neural Networks,” IEEE transactions on Aerospace and Electronic Systems 36(1), 2000: 85-98.
  • H.T. Siegelmann, E. Nissan, and A. Galperin, “A Novel Neural/Symbolic Hybrid Approach to Heuristically Optimized Fuel Allocation and Automated Revision of Heuristics in Nuclear Engineering,” Advances in Engineering Software 28(9), 1997: 581-592.


  • Neural Networks and Analog Computation : Beyond the Turing Limit, Birkhauser, Boston, December 1998 ISBN 0-8176-3949-7

She has also contributed 21 book chapters.

Notes and references[edit]

  1. ^ Biography at UMass
  2. ^ a b Siegelmann, H. T. (28 April 1995). "Computation Beyond the Turing Limit". Science 268 (5210): 545–548. doi:10.1126/science.268.5210.545. PMID 17756722. 
  3. ^ Siegelmann, H.T. (1996). "Reply: Analog Computational Power". Science 271 (5247): 373. doi:10.1126/science.271.5247.373. 
  4. ^ Barkai, N.; Leibler, S. (26 June 1997). "Robustness in simple biochemical networks". Nature 387 (6636): 913–917. doi:10.1038/43199. PMID 9202124. 
  5. ^ McGowan, PO; Szyf, M (July 2010). "The epigenetics of social adversity in early life: implications for mental health outcomes". Neurobiology of disease 39 (1): 66–72. doi:10.1016/j.nbd.2009.12.026. PMID 20053376. 
  6. ^ Yasuhiro Fukushima, Makoto Yoneyama, Minoru Tsukada, Ichiro Tsuda, Yutaka Yamaguti, Shigeru Kuroda (2008). "Physiological Evidence for Cantor Coding Output in Hippocampal CA1". In Rubin Wang, Fanji Gu, Enhua Chen. Advances in cognitive neurodynamics ICCN 2007 proceedings of the International Conference on Cognitive Neurodynamics. Dordrecht: Springer. pp. 43–45. ISBN 978-1-4020-8387-7. 
  7. ^ Bodén, Mikael; Alan Blair (March 2003). "Learning the Dynamics of Embedded Clauses". Applied Intelligence 19 (1/2): 51–63. doi:10.1023/A:1023816706954. 
  8. ^ Toni, R; Spaletta, G; Casa, CD; Ravera, S; Sandri, G (2007). "Computation and brain processes, with special reference to neuroendocrine systems". Acta bio-medica : Atenei Parmensis. 78 Suppl 1: 67–83. PMID 17465326. 
  9. ^ A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik, “Support vector clustering,” Journal of Machine Learning Research 2, 2001: 125-137
  10. ^ Ben-Hur,, A.; Horn, D.; Siegelmann, H.T.; Vapnik, V. (2000). "A support vector clustering method". Pattern Recognition, 2000. Proceedings. 15th International Conference on 2: 724–727. doi:10.1109/ICPR.2000.906177. ISBN 0-7695-0750-6. 
  11. ^ Leise, T.; Hava Siegelmann (1 August 2006). "Dynamics of a Multistage Circadian System". Journal of Biological Rhythms 21 (4): 314–323. doi:10.1177/0748730406287281. PMID 16864651. 
  12. ^ Olsen, Megan; Siegelmann-Danieli, Nava; Siegelmann, Hava T.; Ben-Jacob, Eshel (May 13, 2010). "Dynamic Computational Model Suggests That Cellular Citizenship Is Fundamental for Selective Tumor Apoptosis". In Ben-Jacob, Eshel. PLoS ONE 5 (5): e10637. doi:10.1371/journal.pone.0010637. PMC 2869358. PMID 20498709.