{"id":8,"date":"2019-05-29T16:14:40","date_gmt":"2019-05-29T16:14:40","guid":{"rendered":"https:\/\/groups.cs.umass.edu\/binds\/?page_id=8"},"modified":"2025-02-02T04:26:12","modified_gmt":"2025-02-02T04:26:12","slug":"publications","status":"publish","type":"page","link":"https:\/\/groups.cs.umass.edu\/binds\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n

Journal Publications<\/a>   Book Chapters<\/a>    In Proceedings<\/a>   Abstracts and Short Papers<\/a><\/p>\n\n\n\n\n\n\n\n

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Books<\/h2>\n\n\n\n
\"Book<\/figure>
\n

Neural Networks and Analog Computation: Beyond the Turing Limit<\/a><\/h3>\n\n\n\n

H.T. Siegelmann
Neural Networks and Analog Computation:
Beyond the Turing Limit, Birkhauser
Boston, December 1998<\/p>\n<\/div><\/div>\n\n\n\n

\"\"<\/figure>
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Artificial Intelligence in the Age of Neural Networks and Brain Computing<\/a><\/h3>\n\n\n\n

Robert Kozma, Cesare Alippi, Yoonsuck Choe, Francesco Morabito
Artificial Intelligence in the Age of Neural Networks and Brain Computing<\/em>
November 2018
<\/p>\n\n\n<\/div><\/div>\n\n\n\n

Journal Publications<\/h2>\n
    \n
  1. Prithviraj Tarale, Edward Rietman and Hava T Siegelmann, \u201cDistributed Multi-Agent Lifelong Learning,\u201d TMLR (Transactions on Machine Learning Research), 21 January 2025
    https:\/\/openreview.net\/pdf?id=IIVr4Hu3Oi<\/a>

    <\/li>\n
  2. Roy Siegelmann and Hava T. Siegelmann. \u201cMeta-Analytic Operation of Threshold-independent Filtering (MOTiF) reveals sub-threshold genomic robustness in trisomy: The J\u00f6rmungandr Effect,\u201d BBRC (Biochemical and Biophysical Research Communications),<\/em> Vol 737, Dec 10, 2024

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  3. Patel D., Siegelmann H. \u201cNavigating the Unknown: Leveraging Self-Information and Diversity in Partially Observable Environments\u201d (2024). BBRC (Biochemical and Biophysical Research Communications), <\/em>Vol 737, 2024.\u00a0 https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0006291X24014591

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  4. Patel, D., Sejnowski, T., & Siegelmann, H. (2024). Optimizing Attention and Cognitive Control Costs Using Temporally Layered Architectures. Neural Computation, 2024 Oct 8:1-30.

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  5. Pozzati , J. Zhou, H. Hazan, G. Lakka Klement, H. T. Siegelmann, J. A. Tuszynski and E. A. Rietman, \u201cA Systems Biology Analysis of Chronic Lymphocytic Leukemia,\u201d Onco July 2024.

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  6. Karuvally, T. Sejnowski, and H. Siegelmann, \u201cEnergy-based Sequential Memory Networks at the Adiabatic Limit,\u201d Bulletin of the American Physical Society, 2024, publisher: APS. [Online]. Available: https:\/\/meetings.aps.org\/Meeting\/MAR24\/Session\/M28.13

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  7. Andrea Soltoggio\u2009, Eseoghene Ben-Iwhiwhu, Vladimir Braverman, Eric Eaton, Benjamin Epstein, Yunhao Ge, Lucy Halperin, Jonathan How, Laurent Itti, Michael A. Jacobs, Pavan Kantharaju, Long Le, Steven Lee, Xinran Liu, Sildomar T. Monteiro, David Musliner, Saptarshi Nath, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash, Vishwa Parekh, Kaushik Roy, Shahaf Shperberg, Hava T. Siegelmann, Peter Stone, Kyle Vedder, Jingfeng Wu, Lin Yang, Guangyao Zheng & Soheil Kolouri. \u201cA collective AI via lifelong Learning and sharing at the edge,\u201d Nature Machine Intelligence, 6(3):251-264, April 30 2024. DOI:1038\/s42256-024-00800-2<\/a>

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  8. \n

    Zhongyang Zhang, Kaidong Chai, Haowen Yu, Ramzi Majaj, Francesca Walsh, Edward Jay Wang, Upal Mahbub, Hava T. Siegelmann, Donghyun Kim, Tauhidur Rahman: Neuromorphic high-frequency 3D dancing pose estimation in dynamic environment. Neurocomputing<\/em><\/strong> 547: 126388 (28 August 2023)

    <\/p>\n<\/li>\n

  9. David Abookasis, David Shemesh, Arik Litwin, Hava T. Siegelmann, Elena Didkovsky, Dean D. Ad-El, \u201cSingle probe light reflectance spectroscopy and parameter spectrum feature extraction in experimental skin cancer detection and classification,\u201d Journal of Biophotonics<\/strong><\/em>, April 2023. DOI: 10.1002\/jbio.202300001

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  10. A. Kohan, E. A. Rietman and H. T. Siegelmann, “Signal Propagation: The Framework for Learning and Inference in a Forward Pass,” in IEEE Transactions on Neural Networks and Learning Systems<\/em><\/strong>, December 2022. doi: 10.1109\/TNNLS.2022.3230914.

    <\/li>\n
  11. E. Rietman, L. Schuum, A. Salik, M. Askenazi, H.T. Siegelmann, \u201cMachine learning with Quantum Matter: An Example Using Lead Zirconate Titanate,\u201d<\/em> Quantum Reports<\/em><\/strong>, Oct 2022.

    <\/li>\n
  12. Dhireesha Kudithipudi \u2026.. and Hava T. Siegelmann, \u201cBiological Underpinnings for Lifelong Learning Machines,\u201d Nature Machine Intelligence<\/em><\/strong>, \u00a0<\/strong>4,\u00a0196\u2013210 (July 2022). https:\/\/www.nature.com\/articles\/s42256-022-00452-0<\/a>

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  13. Keegan, H. T. Siegelmann, E. A. Rietman, G. L. Klement, and J. A. Tuszynski, \u201cGibbs Free Energy, a Thermodynamic Measure of Protein\u2013Protein Interactions, Correlates with Neurologic Disability,\u201d BioMedInformatics <\/em><\/strong>1(3) December 2021. 10.3390\/biomedinformatics1030013<\/a>\u00a0

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  14. L. Hayes, G. P. Krishnan,\u00a0M. Bazhenov,\u00a0H. T. Siegelmann,\u00a0T. J. Sejnowski,\u00a0C. Kanan, \u201cReplay in Deep learning: Current Approaches and Missing Biological Elements,\u201d Neural Computation<\/em><\/strong>, 33, 1-44 October 2021. https:\/\/doi.org\/10.1162\/neco_a_01433<\/a>Yu, C., Rietman, E. A., Siegelmann, H. T., Cavaglia, M., & Tuszynski, J. A. (2021). Application of Thermodynamics and Protein\u2013Protein Interaction Network Topology for Discovery of Potential New Treatments for Temporal Lobe Epilepsy.\u00a0Applied Sciences<\/i>,\u00a0<\/span>11<\/i>(17), 8059. <\/span>https:\/\/doi.org\/10.3390\/app11178059<\/a>

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  15. Chang Yu<\/a>, Edward A. Reitman, Hava T. Siegelmann, Marco Cavaglia<\/a> & Jack A. Tuszynski<\/a>. \u201cApplication of Thermodynamics and Protein\u2013Protein Interaction Network Topology for Discovery of Potential New Treatments for Temporal Lobe Epilepsy,\u201d Science <\/em><\/strong>11<\/em>(17), 8059, August 2021. \u00a0 doi.org\/10.3390\/app11178059<\/a><\/li>\n
  16. Amgalan, A., Taylor, P., Mujica-Parodi, L.R.\u00a0et al.<\/i>\u00a0Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters.\u00a0Sci Rep<\/i>\u00a011,\u00a0<\/b>5331 (2021). https:\/\/doi.org\/10.1038\/s41598-021-82461-4<\/a>

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  17. B. Tsuda, K. M. Tye, H. T. Siegelmann, T. J. Sejnowski, \u201cA modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex,\u201d Proceedings of the National Academy of Sciences, November 2020.

    <\/em><\/li>\n
  18. G.M. van de Ven, H. T. Siegelmann, A. S. Tolias Brain-inspired replay for continual learning with artificial neural networks. Nature Communications<\/em>, 11, Article\u00a0number:\u00a04069, August 2020.

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  19. M. Shifrin and H.T. Siegelmann, \u201cNear Optimal Insulin Treatment for Diabetes Patients: A machine learning approach,\u201d Artificial Intelligence in Medicine (AIIM), <\/em>107, July 2020.

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  20. E. A. Rietman, S. Taylor, H.T. Siegelmann, M.A. Deriu, M. Cavaglia, and J.A. Tuszynski, (2020) \u201cUsing the Gibbs Function as a Measure of Human Brain Development Trends from Fetal Stage to Advanced Age,\u201d International Journal of Molecular Sciences 21(3), Feature Papers in Molecular Biophysics,” February 2020.

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  21. Brant, Elizabeth J., et al. “Personalized therapy design for systemic lupus erythematosus based on the analysis of protein-protein interaction networks.”\u00a0Plos one<\/i>\u00a015.3 (2020): e0226883.

    <\/li>\n
  22. D. Patel, H. Hazan, D.J. Saunders, H. Siegelmann, R. Kozma (2019). Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games. Neural Networks, 120, 108-115. https:\/\/arxiv.org\/abs\/1903.11012<\/a>

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  23. D.J. Saunders, D. Patel, H. Hazan, H.T. Siegelmann, T. Kozma (2019). Locally Connected Spiking Neural Networks for Unsupervised Feature Learning. Neural Networks, 119, pp. 332-340. https:\/\/arxiv.org\/abs\/1904.06269<\/a>

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  24. de Bruyn Kops, S. M., et al. “Unsupervised Machine Learning to Teach Fluid Dynamicists to Think in 15 Dimensions.”\u00a0arXiv<\/i>\u00a0(2019): arXiv-1907.

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  25. Hazan, H., Saunders, D. J., Sanghavi, D. T., Siegelmann, H., & Kozma, R. (2019). Lattice Map Spiking Neural Networks (LM-SNNs) for Clustering and Classifying Image Data, Annals of Mathematics and Artificial Intelligence, pp. 1-24. https:\/\/arxiv.org\/pdf\/1906.11826.pdf<\/a>

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  26. Kenney, Jack, et al. “Deep Learning Regression of VLSI Plasma Etch Metrology.”\u00a0arXiv preprint arXiv:1910.10067<\/i>\u00a0(2019).

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  27. Heck, Detlef H., Robert Kozma, and Leslie M. Kay. “The rhythm of memory: how breathing shapes memory function.”\u00a0Journal of Neurophysiology<\/i> 122.2 (2019): 563-571.

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  28. Davis, Jeffery Jonathan Joshua, Robert Kozma, and Florian Sch\u00fcbeler. “Stress Reduction, Relaxation, and Meditative States Using Psychophysiological Measurements Based on Biofeedback Systems via HRV and EEG.” (2019).

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  29. Davis, Jeffery Jonathan Joshua, and Robert Kozma. “Movie-Making of Spatiotemporal Dynamics in Complex Systems.” (2019).

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  30. Janson, Svante, et al. “A modified bootstrap percolation on a random graph coupled with a lattice.”\u00a0Discrete Applied Mathematics<\/i>\u00a0258 (2019): 152-165.

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  31. Golas, Stefan M., et al. “Gibbs free energy of protein-protein interactions correlates with ATP production in cancer cells.”\u00a0Journal of Biological Physics<\/i>\u00a045.4 (2019): 423-430.

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  32. H. Hazan, D.J. Saunders, H. Khan, D. Patel, S.T. Sanghavi, H.T. Siegelmann, R. Kozma, BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python, Frontiers in Neuroinformatics, Dec. 2018. (doi: 10.3389\/fninf.2018.00089)

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  33. Hossain, Gahangir, Mark H. Myers, and Robert Kozma. “Spatial directionality found in frontal-parietal attentional networks.”\u00a0Neuroscience journal<\/i>\u00a02018 (2018).

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  34. Myers, Mark H., and Robert Kozma. “Mesoscopic neuron population modeling of normal\/epileptic brain dynamics.”\u00a0Cognitive neurodynamics<\/i>\u00a012.2 (2018): 211-223.

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  35. Kozma, Robert, and Joshua JJ Davis. “Why do phase transitions matter in minds?.”\u00a0Journal of Consciousness Studies<\/i>\u00a025.1-2 (2018): 131-150.

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  36. Bressler, Steven, Leslie Kay, and G. Vitiello. “Freeman neurodynamics: The past 25 years.”\u00a0Journal of Consciousness Studies<\/i>\u00a025.1-2 (2018): 13-32.

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  37. S. H. McGuire, E. A. Rietman, H. Siegelmann & J. A. Tuszynski, \u201cGibbs free energy as a measure of complexity correlates with time within C. elegans embryonic development,\u201d Journal of Biological Physics, Dec;43(4):551-563, EPub: September 19th 2017. https:\/\/doi.org\/10.1007\/s10867-017-9469-0<\/a>

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  38. Burroni, P. Taylor, C. Corey, T. Vechnadze, H.T. Siegelmann, \u201cEnergetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks,\u201d Frontiers in Neuroscience, 11(80), February 2017, 14 pages. \u00a0https:\/\/doi.org\/10.3389\/fnins.2017.00080<\/a>

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  39. Kozma, Robert, and Walter J. Freeman. “Cinematic operation of the cerebral cortex interpreted via critical transitions in self-organized dynamic systems.”\u00a0Frontiers in systems neuroscience<\/i>\u00a011 (2017): 10.

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  40. Heck, Detlef H., et al. “Breathing as a fundamental rhythm of brain function.”\u00a0Frontiers in neural circuits<\/i>\u00a010 (2017): 115.

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  41. Kozma, Robert, and Raymond Noack. “Freeman\u2019s intentional neurodynamics.”\u00a0Intentional neurodynamics in transition: The dynamical legacy of Walter Jackson Freeman, special issue of Chaos and Complexity Letters<\/i>\u00a011.1 (2017): 93-103.

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  42. Rietman, Edward A., et al. “Personalized anticancer therapy selection using molecular landscape topology and thermodynamics.”\u00a0Oncotarget<\/i>\u00a08.12 (2017): 18735.

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  43. Lee, Minho, Steven Bressler, and Robert Kozma. “Advances in Cognitive Engineering Using Neural Networks.”\u00a0Neural Networks: the Official Journal of the International Neural Network Society<\/i>\u00a092 (2017): 1-2.

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  44. Kay, Leslie M., and Robert Kozma. “Walter J. Freeman: A Tribute.”\u00a0Neuron<\/i>\u00a094.4 (2017): 705-707.

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  45. Rietman, Edward A., and Jack A. Tuszynski. “Thermodynamics and Cancer Dormancy: A Perspective.”\u00a0Tumor Dormancy and Recurrence<\/i>. Humana Press, Cham, 2017. 61-79.

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  46. Capolupo, Antonio, et al. “Bessel-like functional distributions in brain average evoked potentials.”\u00a0Journal of integrative neuroscience<\/i>\u00a016.s1 (2017): S85-S98.

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  47. Rietman, Edward A., et al. “Thermodynamic measures of cancer: Gibbs free energy and entropy of protein\u2013protein interactions.”\u00a0Journal of biological physics<\/i>\u00a042.3 (2016): 339-350.

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  48. Janson, Svante, et al. “Bootstrap percolation on a random graph coupled with a lattice.”\u00a0Electronic Journal of Combinatorics<\/i>\u00a0(2016).

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  49. P. Taylor, J.N. Hobbs, J Burroni, H.T. Siegelmann, \u201cThe global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions,\u201d Nature Scientific Reports<\/em><\/strong> Dec 2015.

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  50. P. Taylor, Z. He, N. Bilgrien, H.T. Siegelmann, \u201cHuman strategies for multitasking, search, and control improved via real-time memory aid for gaze location,\u201d Frontiers in ICT<\/em><\/strong> 2:15, Sept 2015. doi: 10.3389\/fict.2015.00015

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  51. P. Taylor, Z. He, N. Bilgrien, H.T. Siegelmann, \u201cEyeFrame: real-time memory aid improves human multitasking via domain-general eye tracking procedures,\u201d Frontiers in ICT<\/em><\/strong> 2:17, Sept 2015. doi: 10.3389\/fict.2015.00017

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  52. J. Cabessa and H. T. Siegelmann, \u201cThe Super-Turing Computational Power of Plastic Recurrent Neural Networks,\u201d International Journal of Neural Systems<\/em><\/strong> 24(8) 2014.

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  53. Hava Siegelmann and Rudolf Freund, \u201cReport on the 13th International Conference on Unconventional Computation and Natural Computation (UCNC\u201914) Ontario, Canada, July 14-18, 2014,\u201d\u00a0Bulletin of European Association for Theoretical Computer Science<\/strong> (EATCS), number 114, October 2014, pp. 265-269. http:\/\/www.eatcs.org\/images\/bulletin\/beatcs114.pdf<\/li>\n
  54. A. Tal, N. Peled and H. T. Siegelmann, \u201cBiologically inspired load balancing mechanism in neocortical competitive learning,\u201d Frontiers in Neural Circuits. <\/em><\/strong>March 2014 | doi: 10.3389\/fncir.2014.00018.

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  55. D. Nowicki, P. Verga and H.T. Siegelmann, \u201cModeling Reconsolidation in Kernel Associative Memory,\u201d PLoS ONE<\/em>. <\/strong>Aug 2013,\u00a0 8(8): e68189. doi:10.1371\/journal.pone.0068189

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  56. H.T. Siegelmann, \u201cTuring on Super-Turing and Adaptivity\u201d. J. Progress in Biophysics & Molecular Biology.<\/em> <\/strong>April (Sep) 2013, 113(1):117-26. doi: 10.1016\/j.pbiomolbio.2013.03.013.

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  57. E. Kagan, A. Rybalov, H. T. Siegelmann, and R. Yager, \u201cProbability-generated aggregators,\u201d International Journal of Intelligent System. <\/em><\/strong>July 2013, 28(7): 709-727.

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  58. J. Cabessa and H. T. Siegelmann, “The Computational Power of Interactive Recurrent Neural Networks,” Neural Computation<\/em><\/strong>. April 2012, 24(4): 996-1019.

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  59. Frederick C. Harris, Jr., Jeffrey L. Krichmar, Hava Siegelmann, Hiroaki Wagatsuma, \u201cBiologically-Inspired Human-Robot Interactions \u2013 Developing More Natural Ways to Communicate with our Machines,\u201d IEEE Transactions on Autonomous Mental Development<\/em><\/strong> 2012 Special issue 4(3): 190-191.

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  60. Jean-Philippe Thivierge, Ali Minai, Hava Siegelmann, Cesare Alippi, Michael Geourgiopoulos, \u201cA year of neural network research: Special Issue on the 2011 International Joint Conference on Neural Networks<\/a>,\u201d Neural Networks<\/em> <\/strong>Special issue, Volume 32, Pages 1-2, 2012.

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  61. H.T. Siegelmann, \u201cAddiction as a Dynamical Rationality Disorder,\u201d Frontiers of Electrical and Electronic Engineering (FEE) in China.<\/em><\/strong> 1(6), 2011:151-158.

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  62. L. Glass and H.T. Siegelmann, \u201cLogical and symbolic analysis of robust biological dynamics,\u201d Current Opinion in Genetics & Development<\/em><\/strong> 20, 2010: 644-649.

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  63. M.M. Olsen, K. Harrington, H. T. Siegelmann, \u201cConspecific Emotional Cooperation Biases Population Dynamics: A Cellular Automata Approach,\u201d International Journal of Natural Computing Research<\/em><\/strong> 1(3) 2010: 51-65.

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  64. 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<\/em><\/strong> 20 (3): DOI: 10.1063\/1.3491237, September 2010. (7 pages)

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  65. D. Nowicki and H.T. Siegelmann, \u201cFlexible Kernel Memory,\u201d PLOS One<\/em><\/strong> 5: e10955, June 2010. http:\/\/www.plosone.org\/article\/info%3Adoi%2F10.1371%2Fjournal.pone.0010955<\/a> (18 pages)

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  66. M.M. Olsen, N. Siegelmann-Danieli, H.T. Siegelmann. \u201cDynamic Computational Model Suggests that Cellular Citizenship is Fundamental for Selective Tumor Apoptosis,\u201d PLoS One<\/em><\/strong> 5(5):e10637, May 2010. http:\/\/www.plosone.org\/article\/info:doi%2F10.1371%2Fjournal.pone.0010637<\/a> \u00a0\u00a0 (6 pages)\u00a0

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  67. Siegelmann, H.T., \u201cComplex Systems Science and Brain Dynamics: A Frontiers in Computational Neuroscience <\/em><\/strong>Special Topic<\/a>,\u201d 2010, doi: 10.3389\/fncom. 2010. 00007

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  68. K. Tu, D. G. Cooper, H. T. Siegelmann, \u201cMemory Reconsolidation for Natural Language Processing,\u201d Cognitive Neurodynamics<\/em><\/strong> 3(4), 2009: 365-372. \u00a0<\/li>\n
  69. A. Z. Pietrzykowski, R. M. Friesen, G. E. Martin, S.I. Puig, C. L. Nowak, P. M. Wynne, H. T. Siegelmann, S. N. Treistman, \u201cPost-transcriptional regulation of BK channel splice variant stability by miR-9 underlies neuroadaptation to alcohol,\u201d Neuron<\/em><\/strong> 59, July 2008: 274-287.\u00a0\u00a0 \u00a0\u00a0

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  70. 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., \u201cTranscriptional responses to estrogen and progesterone in Mammary gland identify networks regulating p53 activity,\u201d Endocrinology<\/em><\/strong> 149(10), June 2008:\u00a0 4809-4820.\u00a0

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  71. H.T. Siegelmann, \u201cAnalog-Symbolic Memory that Tracks via Reconsolidation,\u201d Physica D: Nonlinear Phenomena<\/em><\/strong> 237 (9), 2008: 1207-1214.\u00a0

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  72. M.M. Olsen, N. Siegelmann-Danieli and H.T. Siegelmann, \u201cRobust Artificial Life Via Artificial Programmed Death,\u201d Artificial Intelligence<\/em><\/strong>\u00a0 172(6-7), April 2008: 884-898.\u00a0

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  73. F. Roth, H. Siegelmann, R. J. Douglas.\u00a0 \u201cThe Self-Construction and -Repair of a Foraging Organism by Explicitly Specified Development from a Single Cell,\u201d Artificial Life<\/em><\/strong> 13(4), 2007: 347-368.

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  74. S. Sivan, O. Filo and H. Siegelman, \u201cApplication of Expert Networks for Predicting Proteins Secondary Structure,\u201d Biomolecular Engineering<\/em><\/strong> 24(2), June 2007: 237-243.<\/li>\n
  75. W. Bush and H.T. Siegelmann, \u201cCircadian Synchronicity in Networks of Protein Rhythm Driven Neurons,\u201d Complexity<\/em><\/strong> 12(1), September\/October 2006: 67-72.

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  76. T. Leise and H.T. Siegelmann, \u201cDynamics of a multistage circadian system,\u201d Journal of Biological Rhythms<\/em><\/strong> 21(4), August 2006: 314-323.\u00a0

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  77. L. Glass, T. J. Perkins, J. Mason, H. T. Siegelmann and R. Edwards, \u201cChaotic Dynamics in an Electronic Model of a Genetic Network,\u201d Journal of Statistical Physics<\/em><\/strong> 121(5-6), November 2005: 969-994.

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  78. O. Loureiro, and H. Siegelmann, “Introducing an Active Cluster-Based Information Retrieval Paradigm,” Journal of the American Society for Information Science and Technology<\/em><\/strong> 56(10), August 2005: 1024-1030.

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  79. Roitershtein, A. Ben-Hur and H.T. Siegelmann \u201cOn probabilistic analog automata,\u201d Theoretical Computer Science<\/em><\/strong> 320(2-3), June 2004: 449-464.

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  80. A. Ben-Hur and H.T. Siegelmann, \u201cComputing with Gene Networks,\u201d Chaos<\/em><\/strong> 14(1), March 2004: 145-151.

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  81. A. Ben-Hur, J. Feinberg, S. Fishman and H. T. Siegelmann, \u201cRandom matrix theory for the analysis of the performance of an analog computer: a scaling theory,\u201d Physics Letters A<\/em>.<\/strong> 323(3-4), March 2004: 204-209.

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  82. A. Ben-Hur, J. Feinberg, S. Fishman and H. T. Siegelmann, \u201cProbabilistic analysis of a differential equation for linear programming,\u201d Journal of Complexity<\/em><\/strong> 19(4), August 2003: 474-510.

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  83. J. P. Neto, H. T. Siegelmann, and J. F. Costa, \u201cSymbolic processing in neural networks,\u201d Journal of the Brazilian Computer Society<\/em><\/strong> 8(3), July 2003: 58-70.

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  84. H. T. Siegelmann, \u201cNeural and Super-Turing Computing,\u201d Minds and Machines<\/em><\/strong>
    13(1),\u00a0February 2003: 103-114.\u00a0\u00a0

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  85. S Eldar, H. T. Siegelmann, D. Buzaglo, I. Matter, A. Cohen, E. Sabo, J. Abrahamson, \u201cConversion of Laparoscopic Cholecystectomy to open cholecystectomy in acute cholecystitis: Artificial neural networks improve the prediction of conversion,\u201d World Journal of Surgery<\/em><\/strong> 26(1), Jan 2002: 79-85.

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  86. A. Ben-Hur, H.T. Siegelmann and S. Fishman, \u201cA theory of complexity for continuous time dynamics,\u201d Journal of Complexity<\/em><\/strong> 18(1), 2002: 51-86.

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  87. A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik, \u201cSupport vector clustering,\u201d Journal of Machine Learning Research<\/em><\/strong> 2, 2001: 125-137.

    <\/li>\n
  88. H. T. Siegelmann, \u201cNeural Computing,\u201d Bulletin of the European Association of Theoretical Computer Science (EATCS)<\/em> <\/strong>73, 2001: 107-130.

    <\/li>\n
  89. H. T. Siegelmann A., Ben-Hur, S. Fishman, \u201cComments on Attractor Computing,\u201d International Journal of Computing Anticipatory Systems<\/em><\/strong> 6, 1999. (from CASY\u201999 International Conference on Computing Anticipatory Systems, Belgium, August 9-14, D.M. Dubois editor)

    <\/li>\n
  90. R. Edwards, H.T. Siegelmann, K. Aziza and L. Glass, \u201cSymbolic dynamics and computation in model gene networks\u201d, Chaos<\/em><\/strong> 11(1), 2001: 160-169.

    <\/li>\n
  91. H. Lipson and H.T. Siegelmann, \u201cGeometric Neurons for Clustering,\u201d Neural Computation<\/em><\/strong> 12(10)<\/em>, August 2000: 2331-2353.

    <\/li>\n
  92. D. Lange, H.T. Siegelmann, H. Pratt, and G.F. Inbar, \u201cOvercoming Selective Ensemble Averaging: Unsupervised Identification of Event Related Brain Potentials.\u201d IEEE Transactions on Biomedical Engineering<\/strong><\/em> 47(6), June 2000: 822-826.

    <\/li>\n
  93. H. Karniely and H.T. Siegelmann, \u201cSensor Registration Using Neural Networks,\u201d IEEE transactions on Aerospace and Electronic Systems<\/strong><\/em> 36(1), 2000: 85-98.

    <\/li>\n
  94. H. T. Siegelmann, \u201cStochastic Analog Networks and Computational Complexity,\u201d Journal of Complexity<\/em><\/strong> 15(4), 1999: 451-475.

    <\/li>\n
  95. T. Siegelmann, A. Ben-Hur and S. Fishman, \u201cComputational Complexity for Continuous Time Dynamics,\u201d Physical Review Letters<\/em><\/strong>, 83(7), 1999: 1463-1466.

    <\/li>\n
  96. H. T. Siegelmann and M. Margenstern, \u201cNine Neurons Suffice for Turing Universality,\u201d Neural Networks<\/em><\/strong> 12, 1999: 593-600.

    <\/li>\n
  97. Gavald\u00e0 and H.T. Siegelmann, \u201cDiscontinuities in Recurrent Neural Networks,\u201d Neural Computation<\/em><\/strong> 11(3), April 1999: 715-745.

    <\/li>\n
  98. H. T. Siegelmann and S. Fishman, \u201cComputation by Dynamical Systems,\u201d Physica D<\/strong> 120<\/em>, 1998 (1-2): 214-235.

    <\/li>\n
  99. Galperin, Y. Kimhi, E. Nissan, and H.T. Siegelmann, \u201cFULECON\u2019s Heuristics, their Rationale, and their Representations,\u201d The New Review of Applied Expert Systems<\/em><\/strong> 4<\/em>, 1998: 163-176.

    <\/li>\n
  100. H. T. Siegelmann, E. Nissan, and A. Galperin, \u201cA Novel Neural\/Symbolic Hybrid Approach to Heuristically Optimized Fuel Allocation and Automated Revision of Heuristics in Nuclear Engineering,\u201d Advances in Engineering Software<\/em><\/strong> 28(9), 1997: 581-592.

    <\/li>\n
  101. J. L. Balc\u00e1zar, R. Gavald\u00e0, and H.T. Siegelmann, \u201cComputational Power of Neural Networks: A Characterization in Terms of Kolmogorov Complexity,\u201d IEEE Transactions on Information Theory<\/strong><\/em> 43(4), July 1997: 1175-1183.

    <\/li>\n
  102. J. P. Neto, H.T. Siegelmann, and J.F. Costa, \u201cImplementation of Programming Languages with Neural Nets,\u201d International Journal of Computing Anticipatory Systems<\/em><\/strong> 1, 1997: 201-208

    <\/li>\n
  103. H. T. Siegelmann, B.G. Horne, and C.L.Giles, \u201cComputational Capabilities of Recurrent NARX Neural Networks,\u201d IEEE Transaction on Systems, Man and Cybernetics<\/em><\/strong> \u2013 part B: Cybernetics<\/em> 27(2), 1997: 208-215.

    <\/li>\n
  104. E. Nissan, H.T. Siegelmann, A. Galperin, and S. Kimhi, \u201cUpgrading Automation for Nuclear Fuel In-Core Management: From the Symbolic Generation of Configurations, to the Neural Adaptation of Heuristics,\u201d Engineering with Computers<\/strong><\/em> 13(1), 1997: 1-19.

    <\/li>\n
  105. O. Frieder and H.T. Siegelmann, \u201cDocument Allocation: A Genetic Algorithm Approach,\u201d IEEE Transactions on Knowledge and Data Engineering<\/em><\/strong> 9(4), 1997: 640-642. (Work described in American Scientist)

    <\/em><\/li>\n
  106. H. T. Siegelmann and C.L. Giles, \u201cThe Complexity of Language Recognition by Neural Networks,\u201d Journal of Neurocomputing,<\/em> special Issue on Recurrent Networks for Sequence Processing, <\/strong>Editors: M. Gori, M. Mozer, A.H. Tsoi, W. Watrous, 15(3-4), 1997: 327-345.

    <\/li>\n
  107. H. T. Siegelmann, \u201cOn NIL: The Software Constructor of Neural Networks,\u201d Parallel Processing Letters<\/strong><\/em> 6(4), 1996: 575-582.

    <\/li>\n
  108. H. T. Siegelmann, \u201cThe Simple Dynamics of Super Turing Theories,\u201d Theoretical Computer Science<\/strong><\/em> (special issue on UMC) 168(2), 1996: 461-472.

    <\/li>\n
  109. H. T. Siegelmann, \u201cRecurrent Neural Networks and Finite Automata,\u201d Journal of Computational Intelligence<\/strong><\/em> 12(4), 1996: 567-574.

    <\/li>\n
  110. J. Kilian and H.T. Siegelmann, \u201cThe Dynamic Universality of Sigmoidal Neural Networks,\u201d Information and Computation<\/em><\/strong> 128(1), 1996: 45-56.

    <\/li>\n
  111. H. T. Siegelmann, \u201cAnalog Computational Power, Technical comment,\u201d Science<\/em><\/strong> 271(19), January 1996: 373.

    <\/li>\n
  112. B. DasGupta, H.T. Siegelmann and E. Sontag, \u201cOn the Complexity of Training Neural Networks with Continuous Activation Functions,\u201d IEEE Transactions on Neural Networks<\/strong><\/em> 6(6), 1995: 1490-1504.

    <\/li>\n
  113. H. T. Siegelmann, \u201cComputation Beyond the Turing Limit,\u201d Science<\/em><\/strong> 238(28), April 1995: 632-637. (Work received wide media attention, and was mentioned as founding the field of HyperComputation.<\/em>)

    <\/li>\n
  114. H. T. Siegelmann and E.D. Sontag, \u201cComputational Power of Neural Networks,\u201d Journal of Computer System Science<\/em>s<\/strong> 50(1), 1995: 132-150. (Work described as most fundamental theorem about neural networks in Simon Haykin\u2019s book of Neural Networks.)

    <\/em><\/li>\n
  115. H. T. Siegelmann and E.D. Sontag, \u201cAnalog Computation via Neural Networks,\u201d Theoretical Computer Science<\/strong> 131, <\/em>1994: 331-360. (Work described as the fundamental theorem differentiating neural networks from classical computers in Simon Haykin\u2019s book of Neural Networks; is cited in the field and in the media.)

    <\/em><\/li>\n
  116. H. T. Siegelmann and E.D. Sontag, \u201cTuring Computability with Neural Networks,\u201d Applied Mathematics Letters<\/em><\/strong> 4(6), 1991: 77-80.<\/li>\n<\/ol>\n

    Book Chapters<\/h2>\n
      \n
    1. J.\u00a0 Hammelman, H.Siegelmann, S. Manicka, M. Levin, “Towards Modeling Regeneratopm via Adaptable Echo State Networks,” in “From Parallel to Emergent Computing<\/a>,” 2019, (18 page) ebook isbn: 9781315167084

      <\/li>\n
    2. H.T. Siegelmann and R. Kozma, \u201cAssociative Learning,\u201d UNESCO Encyclopedia of Life Support Systems (EOLSS), Vol. Computational Intelligence, (Eds) H. Ishibuchi, UNESCO EOLSS Press, New York, 2015.

      <\/li>\n
    3. E. Kagan, A. Rybalov, A. Sela, H. Siegelmann, J. Steshenko, \u201cProbabilistic control and swarm dynamics in mobile robots and ants,\u201d in Biologically-Inspired Techniques for Knowledge Discovery and Data Mining,<\/em><\/strong> S. A. Burki, G. Dobbie and Y. S. Koh (eds.), pp.11-47, 2014 http:\/\/www.igi-global.com\/chapter\/probabilistic-control-and-swarm-dynamics-in-mobile-robots-and-ants\/110453

      <\/li>\n
    4. H. T. Siegelmann, \u201cSuper Turing As a Cognitive Reality,\u201d (Chapter 21) in Consciousness: Its Nature and Functions<\/em><\/strong>,<\/em> Shulamith Kreitler and Oded Maimon (eds), Nova Publishers, 2012, Hauppauge, NY: 401-410.

      <\/li>\n
    5. K. I. Harrington and H. T. Siegelmann, \u201cAdaptive Multi-modal Sensors,\u201d in 50 years of Artificial Intelligence<\/em><\/strong>, Lungarella, F. Iida, J. Bongard, R. Pfeifer (eds.) Springer 2007: 264-173.

      <\/li>\n
    6. Bhaskar DasGupta, Derong Liu and Hava Siegelmann, \u201cNeural Networks,\u201d in Handbook on Approximation Algorithms and Metaheuristics<\/em><\/strong>, Teofilo F. Gonzalez (editor), Chapman & Hall\/CRC\u00a0 (Computer & Information Science Series, series editor:\u00a0 Sartaj Sahni) 2007: 22-1\u201422-14.

      <\/li>\n
    7. H. T. Siegelmann, \u201cNeural Computing,\u201d in Current Trends in Theoretical Computer Science: The Challenge of the New Century<\/em><\/strong>, G. Paun, G. Rozenberg, A. Salomaa (eds), 2004.

      <\/li>\n
    8. H.T. Siegelmann, \u201cNeural Automata and Computational Complexity,\u201d in Handbook of Brain Theory and Neural Networks<\/em><\/strong>, M.A. Arbib (ed.), Birkhauser Boston, 2002.

      <\/li>\n
    9. H.T. Siegelmann, \u201cUniversal Computation and Super-Turing Capabilities,\u201d in Field Guide to Dynamical Recurrent Networks<\/em><\/strong>, J.F. Kolen\u00a0 and S.C. Kremer (eds.), IEEE Press, 2001:143-151.

      <\/li>\n
    10. A. Ben-Hur and H.T. Siegelmann, \u201cComputation in gene networks,\u201d in Machines, Computations and Universality (MCU), Lecture Notes in Computer Science<\/strong>, M. Margenstern and Y. Rogozhin (Eds.) 2055, 2001: 11-24.

      <\/li>\n
    11. H.T. Siegelmann, \u201cFinite vs. Infinite Descriptive Length in Neural Networks and the Associated Computational Complexity,\u201d in Finite vs. Infinite: Contributions to an Eternal Dilemma<\/em>, <\/strong>C. Calude and G. Paun (eds.), Springer Verlag, 2000.\u00a0

      <\/li>\n
    12. H.T. Siegelmann, \u201cNeural Automata and Computational Complexity,\u201d in Handbook of Brain Theory and Neural Networks<\/em><\/strong>, M.A. Arbib (ed.), 2000.

      <\/li>\n
    13. H. Lipson and H.T. Siegelmann, \u201cHigh Order Eigentensors as Symbolic Rules in Competitive Learning,\u201d in Lecture Notes in Computer Science <\/em><\/strong>1778, Hybrid Neural Systems<\/strong><\/em>, Springer-Verlag, 1998: 286-297.

      <\/li>\n
    14. H.T. Siegelmann, \u201cNeural Dynamics with Stochasticity,\u201d in Adaptive Processing of Sequences and Data Structures<\/em><\/strong>, C.L. Giles and M. Gori (eds.), Springer, 1998: 346-369.

      <\/li>\n
    15. H.T. Siegelmann, \u201cComputability with Neural Networks,\u201d in Lectures in Applied Mathematics<\/em><\/strong> 32, J. Reneger, M. Shub, and S. Smale (eds.), American Mathematical Society, 1996: 733-747.

      <\/li>\n
    16. H.T. Siegelmann, \u201cNeural Automata,\u201d in Shape, Structures and Pattern Recognition<\/em><\/strong>, D. Dori and F. Bruckstein (eds.), World Scientific, 1995:241-250.

      <\/li>\n
    17. H.T. Siegelmann, \u201cTowards a Neural Programming Language,\u201d in Shape, Structures and Pattern Recognition<\/em>,<\/strong> D. Dori and F. Bruckstein (eds.), World Scientific, 1995.

      <\/li>\n
    18. H.T. Siegelmann, \u201cWelcoming the Super-Turing theories,\u201d in Lecture Notes in Computer Science<\/em><\/strong> 1012, M. Bartosek, J. Staudek, J. Wiedermann (eds.), Springer Verlag, 1995: 83-94.

      <\/li>\n
    19. H.T. Siegelmann, \u201cRecurrent Neural Networks,\u201d in The 1000th Volume of Lecture Notes in Computer Science: Computer Science Today<\/em><\/strong>, J. Van Leeuwen (ed.), Springer Verlag, 1995: 29-45.

      <\/li>\n
    20. H.T. Siegelmann, B.G. Horne, and C.L. Giles, \u201cWhat NARX Networks Can Compute,\u201d in Lecture Notes in Computer Science: Theory and Practice of Informatics<\/em><\/strong> Vol. 1012, M. Bartosek, J. Staudek, J. Wiedermann (eds.), Springer Verlag, 1995: 95-102.

      <\/li>\n
    21. DasGupta, H.T. Siegelmann, and E. Sontag, \u201cOn the Intractability of Loading Neural Networks,\u201d in Theoretical Advances in Neural Computation and Learning<\/em><\/strong>, V.P. Roychowdhury, K.Y. Siu, and A. Orlitsky (eds.), Kluwer Academic Publishers, 1994: 357-389.

      <\/li>\n
    22. H.T. Siegelmann, \u201cOn the Computational Power of Probabilistic and Faulty Neural Networks,\u201d in Lecture Notes in Computer Science<\/em><\/strong> 820: Automata, Languages and Programming, S. Abiteboul and E. Shamir (eds.), Springer Verlag, 1994: 20-34.

      <\/li>\n
    23. H.T. Siegelmann and O. Frieder, \u201cDocument Allocation in Multiprocessor Information Retrieval Systems,\u201d in Lecture Notes in Computer Science<\/em><\/strong> 759: Advanced Database Concepts and Research Issues<\/strong>, N.R. Adam and B. Bhargava (eds.), Springer Verlag, November 1993: 289-310.

      <\/li>\n
    24. H.T. Siegelmann, E.D. Sontag, and C.L. Giles, \u201cThe Complexity of Language Recognition by Neural Networks,\u201d Algorithms, Software, Architecture<\/em><\/strong> (J. Van Leeuwen, ed.), North Holland, Amsterdam, 1992: 329-335.<\/li>\n<\/ol>\n

      In Proceedings<\/h2>\n
        \n
      1. Devdhar Patel and Hava Siegelmann, \u201cOvercoming Slow Decision Frequencies in Continuous Control Model-Based Sequence Reinforcement Learning for Model-Free Control\u201d, In The Thirteenth International Conference on Learning Representations (ICLR) 2025.

        <\/li>\n
      2. Russell, J., Gavier, I., Patel, D., Rietman, E., & Siegelmann, H. Optimizing Neural Network Representations of Boolean Networks.\u00a0In The Thirteenth International Conference on Learning Representations (ICLR) 2025.

        <\/li>\n
      3. Prithviraj Tarale, Ed Rietman and Hava T. Siegelmann, \u201cDistributed Multi-Agent Lifelong Learning,\u201d accepted ICONIP 2024.

        <\/li>\n
      4. Karuvally, T. Sejnowski, and H. T. Siegelmann, \u201cHidden traveling waves bind working memory variables in recurrent neural networks,\u201d in Proceedings of the 41st International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, J. Scarlett, and F. Berkenkamp, Eds., vol. 235. PMLR, 21\u201327 Jul 2024, pp. 23 266\u201323 290. [Online]. Available: https:\/\/proceedings.mlr.press\/v235\/karuvally24a.html<\/a>

        <\/li>\n
      5. Kaleab B. Belay, Dezhen Song and Hava T. Siegelmann. \u201cComputational Model for error correction in the Head-Direction System,\u201d 5th International Convention on the Mathematics of Neuroscience and AI. May 28-31, 2024, Rome Italy.

        <\/li>\n
      6. Devdhar Patel, Joshua Russell, Francesca Walsh, Tauhidur Rahman, Terrence Sejnowski, and Hava Siegelmann. 2023. Temporally Layered Architecture for Adaptive, Distributed and Continuous Control. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (AAMAS ’23). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2830\u20132832. May 29-June 2, 2023.

        <\/li>\n
      7. Karuvally, A., Sejnowski, T. and Siegelmann, H.T. (2023). General Sequential Episodic Memory Model. Proceedings of the 40th International Conference on Machine Learning, (ICML) in Proceedings of Machine Learning Research 202:15900-15910 Available from https:\/\/proceedings.mlr.press\/v202\/karuvally23a.html<\/a>. July 23-29 2023.

        <\/li>\n
      8. Karuvally, A., Delmastro, P., & Siegelmann, H.T., (2023). Episodic Memory Theory of Recurrent Neural Networks: Insights into Long-Term Information Storage and Manipulation. 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML) at the 40th International Conference on Machine Learning, (ICML) Honolulu, Hawaii, USA., July 23-29 2023.

        <\/li>\n
      9. Karuvally, A., Delmastro, P., & Siegelmann, H.T., (2023). Episodic Memory Theory of Recurrent Neural Networks: Insights into Long-Term Information Storage and Manipulation. 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML) at the 40th International Conference on Machine Learning, (ICML) Honolulu, Hawaii, USA., July 23-29 2023.

        <\/li>\n
      10. Karuvally, A., Delmastro, P., & Siegelmann, H.T., (2023). Episodic Memory Theory of Recurrent Neural Networks: Insights into Long-Term Information Storage and Manipulation. 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML) at the 40th International Conference on Machine Learning, (ICML) Honolulu, Hawaii, USA., July 23-29 2023.<\/li>\n
      11. M.C. Chung and H. T. Siegelmann, \u201cTuring Completeness of Finite Precision Neural Networks,\u201d 35th Conference on Neural Information Processing Systems (35th Conference on Neural Information Processing Systems (NeurIPS 2021).) Dec 6-14 2021

        <\/li>\n
      12. Gain, H. Siegelmann, \u201cAbstraction Mechanisms Predict Generalization in Deep Neural Networks,\u201d International conference on Machine learning (ICML), May 2020

        <\/li>\n
      13. G. M. van de Ven, H. T. Siegelmann, A.S. Tolias \u201cBrain-like replay for continual learning with artificial neural networks,\u201d International Conference on Learning Representations (ICLR) workshop “Bridging AI and Cognitive Science,” April 2020. Selected for 15-min oral (6% acceptance rate). URL: https:\/\/baicsworkshop.github.io\/program\/baics_8.html<\/a>

        <\/li>\n
      14. A. Gain, P. Kaushik, H. Siegelmann, \u201cAdaptive Neural Connections for Sparsity Learning,\u201d The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 3188-3193

        <\/li>\n
      15. A. Gain, H. Siegelmann, \u201cUtilizing full neuronal states for adversarial robustness,\u201d Proceedings Volume 11197, SPIE Future Sensing Technologies; 1119712 (2019), Tokyo, Japan https:\/\/doi.org\/10.1117\/12.2542804<\/a>

        <\/li>\n
      16. R. Kozma, R. Noack, H.T. Siegelmann (2019) Models of Situated Intelligence Inspired by the Energy Management of Brains, Proc. IEEE Inf. Conf. Systems, Man, and Cybernetics, SMC2019<\/em>, October 5-9, 2019, Bari, Italy, IEEE Press.

        <\/li>\n
      17. H. Hazan, D. Saunders, D. Sanghavi, H. T. Siegelmann and K. Robert, \u201cUnsupervised Learning with Self-Organizing Spiking Neural Networks,\u201d IEEE\/INNS International Joint Conference on Neural Networks, Brazil, July 2018.

        <\/li>\n
      18. D. Saunders, H. T. Siegelmann, R. Kozma and M. Ruszinko, \u201cSTDP Learning of Image Features with Spiking Neural Networks,\u201d IEEE\/INNS International Joint Conference on Neural Networks, Brazil, July 2018.

        <\/li>\n
      19. R. Kozma, R. Ilin, and H. T. Siegelmann, \u201cEvolution of Abstraction Across layers in deep learning neural networks,\u201d INNS Big Data Deep Learning Conference (BDDL2018), Bali Indonesia, April 17-19 2018.<\/li>\n
      20. R. Noack, C. Manjesh, M. Ruszinko, H. Siegelmann, and R. Kozma, \u201cResting State Neural Networks and Energy Metabolism,\u201d IEEE\/INNS International Joint Conference on Neural Networks, Anchorage Alaska, May 14-19 2017.

        <\/li>\n
      21. J. Nick Hobbs and H.T. Siegelmann, \u201cImplementation of Universal Computation via Small Recurrent Finite Precision Neural Networks\u201d IEEE\/INNS International Joint Conference on Neural Networks, Ireland, July 2015.

        <\/li>\n
      22. \u00a0A.S. Younger, E. Redd, H. Siegelmann \u201cDevelopment of Physical Super-Turing Hardware.\u201d O.H. Ibarra et al. (Eds.) UCNC 2014 (Unconventional computation and Natural computation) Ontario, Canada, June, LNCS 8553 (2014): 379-391.

        <\/li>\n
      23. A. Tal and H.T. Siegelmann, \u201cConscience mechanism in neocortical competitive learning,\u201d ICCN2013 (International Conference on Cognitive Neurodynamics), Sigtuna, Sweden, June 2013.

        <\/li>\n
      24. M. M. Olsen and H.T. Siegelmann, \u201cMultiscale Agent-Based Model for Tumor Angiogenesis,\u201d International Conference on Computational Science ICCS, June 2013: 1016-1025.

        <\/li>\n
      25. J. Cabessa and H.T. Siegelmann, \u201cEvolving Recurrent Neural Networks are Super-Turing,\u201d Proceedings of International Joint Conference on Neural Networks; 2012 July 31 \u2013 August 5; San Jose, California, USA: 3200-3206.

        <\/li>\n
      26. Harrington, K. I., M. Olsen, and H. Siegelmann, “Computational Neuroecology of Communicated Somatic Markers”. In Proceedings of Artificial Life XIII, July 2012: 555-556.

        <\/li>\n
      27. K.I. Harrington, M.M. Olsen and H.T. Siegelmann, \u201cCommunicated Somatic Markers Benefit Both the Individual and the Species,\u201c Proceedings of International Joint Conference on Neural Networks; 2012 July 31 \u2013 August 5; San Jose, California, USA: 3272-3278.

        <\/li>\n
      28. K. Tu, M. Olsen, H. Siegelmann. \u201cCIM for Improved Language Understanding,\u201d Proceedings of the Tenth International Symposium on Logical Formalization on Commonsense Reasoning. March 2011.

        <\/li>\n
      29. Y. Z. Levy, D. Levy, J.S. Meyer, H.T. Siegelmann, \u201cIdentification and Control of Intrinsic Bias in a Multiscale Computational Model of Drug Addiction,\u201d Proceedings of the 2010 Symposium on Applied Computing (ACM SAC 2010<\/a>), Sierre, Switzerland, March 2010: 2389-2393.

        <\/li>\n
      30. K. Tu and H.T. Siegelmann, \u201cText-based Reasoning with Symbolic Memory Model,\u201d Proceedings of the Fifth International Workshop on Neural-Symbolic Learning and Reasoning (NeSy’09), Pasadena, USA, July 11, 2009: 16-21.

        <\/li>\n
      31. M. Olsen, N. Siegelmann-Danieli, H. Siegelmann. Computational Modeling Reveals the Crucial Role of Cellular Citizenship in Selective Tumor Apoptosis. Systems Biology of Human Disease. June 2009.

        <\/li>\n
      32. Y.Z. Levy, D. Levy, J.S. Meyer and H.T. Siegelmann, \u201dDrug Addiction: a computational multiscale model combining neuropsychology, cognition, and behavior,\u201d Intl. Conf. on Bio-inspired Systems and Signal Processing (BIOSIGNALS), Portugal, 2009: 87-94.

        <\/li>\n
      33. Y.Z. Levy, D. Levy, J.S. Meyer and H.T. Siegelmann, \u201cDrug Addiction as a Non-monotonic Process: a Multiscale Computational Model,\u201d 13th Intl. Conf. on Biomedical Engineering (ICBME), Singapore, December 2008. 4 pages.\u00a0

        <\/li>\n
      34. M. Olsen and H. Siegelmann, \u201cMulti-Agent System that Attains Longevity via Death,\u201d Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI), India, Jan 2007: 1428-1433.

        <\/li>\n
      35. M. Olsen, H. Siegelmann. Artifical Death for Attaining System Longevity. Proceedings of the 50th Anniversary Summit of Artificial Intelligence Summit. pp. 217-218. July 2006.

        <\/li>\n
      36. Y. Guo and H. Siegelmann, \u201cTime-Warped Longest Common Subsequence Algorithm for Music Retrieval,” International Conference on Music Information Retrieval (ISMIR), Spain, October 2004: 258-261.

        <\/li>\n
      37. T. Jaakkola and H. Siegelmann, \u201cActive information retrieval,\u201d
        Advances in Neural Information Processing Systems (NIPS), Denver Colorado, 2001: 777-784.

        <\/li>\n
      38. P. Rodrigues, J. F\u00e9lix Costa, H. T. Siegelmann, \u201cVerifying Properties of Neural Networks,\u201d International Work Conference on Artificial Neural Networks (IWANN), Granada Spain, June 2001: 158-165.

        <\/li>\n
      39. D. Horn, I. Opher, M. Epstein and H. T. Siegelmann, \u201dClustering of Documents using Latent Semantic Analysis,\u201d Proceedings of the Document Analysis Systems (DAS), Rio de Janeiro, 2000.

        <\/li>\n
      40. A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik, \u201cA Support Vector Method for Hierarchical Clustering,\u201d Fourteenth Annual Conference on Neural Information Processing Systems (NIPS), Denver Colorado, 2001: 367-373.

        <\/li>\n
      41. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik, \u201cA Support Vector Clustering Method,\u201d Proceedings of the 15th International Conference on Pattern Recognition (ICPR), Barcelona Spain, September 2000: 728-731.

        <\/li>\n
      42. H.T. Siegelmann, A. Roitershtein and A. Ben-Hur, \u201cNoisy Neural Networks and Generalizations,\u201d Proceedings of Thirteenth Annual Conference on Neural Information Processing Systems (NIPS), Denver Colorado, December 1999: 335-341.

        <\/li>\n
      43. H.T. Siegelmann and S. Fishman, \u201cAttractor Systems and Analog Computation,\u201d Proceedings of the Second International Conference on Knowledge-Based Intelligent Electronic Systems (KES\u201998), Adelaide Australia, 21-23 April 1998.

        <\/li>\n
      44. H. Lipson, Y. Hod, and H.T. Siegelmann, \u201cHigh-Order Clustering Metrics for Competitive Learning Neural Networks,\u201d Proceedings of the Israel-Korea Bi-National Conference on New Themes in Computer Aided Geometric Modeling, Tel-Aviv Israel, February 1998: 181-188.

        <\/li>\n
      45. J.P. Neto, H.T. Siegelmann, and J.F. Costa, \u201cTuring Universality of Neural Nets Revisited,\u201d Proceedings of the Sixth International Conference on Computer Aided Systems Technology (EUROCAST\u201997).\u00a0 In Franz Pichler and Roberto Moreno-Diaz (eds.), Lecture Notes in Computer Science (LNCS) 1333, 1997: 3651-366.

        <\/li>\n
      46. D.H. Lange, H.T. Siegelmann, H. Pratt, and G.F. Inbar, \u201cA Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure,\u201d Proceedings of the International Conference on Neural Information Proceeding (NIPS), Denver Colorado, December 1997: 901-907.

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      47. Y. Finkelstein and H.T. Siegelmann, \u201cA Stochastic Model to Study Degenerative Disorders in the Central Nervous System,\u201d The Israel Neurological Association Annual Meeting, Zichron-Yaakov, November 1997.

        <\/li>\n
      48. H.T. Siegelmann and S. Fishman, \u201cComputation in Dynamical Systems,\u201d Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, October 1997.

        <\/li>\n
      49. H.T. Siegelmann, A. Ofri, and H. Guterman, \u201cApplying Modular Networks and Fuzzy Logic Controllers to Nonlinear Flexible Structures,\u201d Fuzzy Information Processing Society, Annual Meeting of the North American, September 1997: 96-101.

        <\/li>\n
      50. G. Arieli and H.T. Siegelmann, \u201cANN Approach vs. the Symbolic Approach in AI,\u201d Proceedings of the Thirteenth Israeli Conference on Artificial Intelligence and Computer Vision (IAICV\u201997), Tel-Aviv, February 1997.

        <\/li>\n
      51. J. Utans, J. Moody, S. Rehfuss, and H. T. Siegelmann, \u201cSelecting Input Variables via Sensitivity Analysis: Application to Predicting the U.S. Business Cycle,\u201d Proceedings of Computational Intelligence in Financial Engineering, IEEE Press, New York, April 1995: 118-122.

        <\/li>\n
      52. H.T. Siegelmann, \u201cRecurrent Neural Networks and Finite Automata\u201d Proceedings of the Twelfth International Conference on Pattern Recognition, Jerusalem, October 1994.

        <\/li>\n
      53. E. Nissan, H.T. Siegelmann, and A. Galperin, \u201cAn Integrated Symbolic and Neural Network Architecture for Machine Learning in the Domain of Nuclear Engineering,\u201d Proceedings of the Twelfth International Conference on Pattern Recognition, Jerusalem, October 1994: 494-496.

        <\/li>\n
      54. E. Nissan, H.T. Siegelmann, A. Galperin, and S. Kimhi, \u201cTowards Full Atomization of the Discovery of Heuristics in a Nuclear Engineering Project: Integration with a Neural Information Language,\u201d Proceedings of the Eight International Symposium on Methodologies for Intelligent Systems, Charlotte, North Carolina, October 1994 (869): 427-436.

        <\/li>\n
      55. H.T. Siegelmann, \u201cNeural Programming Language,\u201d Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI-94, July\u2013August 1994, Seattle Washington, AAAI Press\/The MIT Press, 1994, Vol. 2: 877-882.

        <\/li>\n
      56. DasGupta, H.T. Siegelmann, and E. Sontag, \u201cOn a Learnability Question Associated to Neural Networks with Continuous Activations,\u201d Proceedings of the Sixth ACM Workshop on Computational Learning (COLT), New Brunswick NJ, July 1994: 47-56.

        <\/li>\n
      57. H.T. Siegelmann, \u201cOn the Computational Power of Probabilistic and Faulty Neural Networks,\u201d Proceedings of the International Colloquium on Automata, Languages, and Programming (ICALP), Jerusalem, July 1994: 23-34.

        <\/li>\n
      58. J. Kilian and H.T. Siegelmann, \u201cComputability with the Classical Sigmoid,\u201d Proceedings of the Fifth ACM Workshop on Computational Learning (COLT), Santa Cruz, July 1993: 137-143.

        <\/li>\n
      59. H.T. Siegelmann and O. Frieder, \u201cDocument Allocation In Multiprocessor Information Retrieval Systems,\u201d Advanced Database Systems, 1993: 289-310.

        <\/li>\n
      60. H.T. Siegelmann and E.D. Sontag, \u201cAnalog Computation via Neural Networks,\u201d Proceedings of the Second Israel Symposium on Theory of Computing and Systems (ISTCS), Natanya Israel, June 1993: 98-107.

        <\/li>\n
      61. J.L. Balc\u00e1zar, R. Gavalda, H.T. Siegelmann, and E.D. Sontag, \u201cSome Structural Complexity Aspects of Neural Computation,\u201d Proceedings of the IEEE Conference on Structure in Complexity Theory, San Diego, California, May 1993: 253-265.

        <\/li>\n
      62. H.T. Siegelmann and E.D. Sontag, \u201cSome Recent Results on Computing with \u2018Neural Nets\u2019,\u201d Proceedings of the IEEE Conference on Decision and Control, Tucson Arizona, December 1992: 1476-1481. Best Student Paper Award.

        <\/em><\/strong><\/li>\n
      63. H.T. Siegelmann and E.D. Sontag, \u201cOn the Computational Power of Neural Networks,\u201d Proceedings of the Fifth ACM Workshop on Computational Learning Theory (COLT), Pittsburgh Penn, July 1992: 440-449.

        <\/li>\n
      64. H.T. Siegelmann and O. Frieder, \u201cThe Allocation of Documents in Multiprocessor Information Retrieval Systems: An Application of Genetic Algorithms,\u201d Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, Charlottesville Virginia, October 1991 (1): 645-650.

        <\/li>\n
      65. O. Frieder and H.T. Siegelmann, \u201cOn the Allocation of Documents in Information Retrieval Systems,\u201d Proceedings of the ACM Fourteenth Conference on Information Retrieval (SIGIR), Chicago Illinois, October 1991: 230-239.

        <\/li>\n
      66. H.T. Siegelmann and B.R. Badrinath, \u201cIntegrating Implicit Answers with Object-Oriented Queries,\u201d Proceedings of the Conference on Very Large Data Bases, Barcelona Spain, September 1991: 15-24.<\/li>\n<\/ol>\n

        Abstracts and Short Papers<\/h2>\n
          \n
        1. A. Gain and H. Siegelmann, Relating information complexity and training in deep neural networks,\u201d SPIE Defense + Commercial Sensing, 2019, Baltimore, Maryland, United States

          <\/li>\n
        2. K. Tu, H. T. Siegelmann, \u201cMemory Model for Text Reasoning,\u201d Northeast Student Conference on Artificial Intelligence (NESCAI) 2010.

          <\/li>\n
        3. M. Olsen, R. Sitaraman, N. Siegelmann-Danieli, H. Siegelmann, \u201cMathematical and computational models for cellular space in cancer growth,\u201d Proceedings of the American Association for Cancer Research. April 2010.

          <\/li>\n
        4. M. M. Olsen, N. Siegelmann-Danieli and H.T. Siegelmann, \u201cMathematical and computational models for cellular space in cancer growth,\u201d American Association for Cancer Research (AACR) 101th Annual meeting, Washington D.C., April 2010.

          <\/li>\n
        5. Y.Z. Levy, D. Levy, J.S. Meyer, H.T. Siegelmann (2009). \u201cCeasing the use of narcotics without treatments in the context of a multiscale computational model of addiction,\u201d 6th annual meeting of the Society for Autonomous Neurodynamics, Principles of Autonomous Neurodynamics 2009 (SAND), La Jolla, CA, USA, July 2009.

          <\/li>\n
        6. Y.Z. Levy, D. Levy, J.S. Meyer, H.T. Siegelmann. Neuropsychology, cognition, and behavior of drug addiction: A non-monotonic multiscale computational model. 13th International Conference on Cognitive and Neural Systems (ICCNS), Boston, MA, USA, May 2009.

          <\/li>\n
        7. D. Nowicki and H.T. Siegelmann, \u201cThe Secret Life of Kernels: Reconsolidation in Flexible memories,\u201d Computational and Systems Neuroscience (COSYNE), February 2009. doi: 10.3389\/conf.neuro.06.2009.03.271

          <\/li>\n
        8. H.T. Siegelmann, M. M. Olsen and N. Siegelmann-Danieli, \u201cRescue Selective Apoptosis Relies on Cell Communication and Citizenship Commitments: A Computational Approach,\u201d American Association for Cancer Research (AACR) 99th Annual Meeting, Dan Diego, April 2008.

          <\/li>\n
        9. M. M. Olsen, K. Harrington and H.T. Siegelmann, \u201cEmotions for Strategic Real-Time Systems,\u201d AAAI Spring Symposium on Emotion, Personality and Social Behavior, Technical Report (SS-08-04), March 2008: 104-110.

          <\/li>\n
        10. D. G. Cooper, D. Katz and H.T. Siegelmann, \u201cEmotional Robotics: Tug of War,\u201d AAAI Spring Symposium on Emotion, Personality and Social Behavior, Technical Report (SS-08-04), March 2008: 23-29.

          <\/li>\n
        11. L. E. Holtzman and H.T. Siegelmann, \u201cInput driven dynamic attractors,\u201d Computational and Systems Neuroscience (COSYNE), Salt Lake City, February 2007: 101.

          <\/li>\n
        12. M. M. Olsen, H.T. Siegelmann, \u201cArtificial Death for Attaining System Longevity,\u201d Proceedings of the 50th Anniversary Summit of Artificial Intelligence, Switzerland, July 2006: 217-218.

          <\/li>\n
        13. K. Harrington and H.T. Siegelmann \u201cAdaptive Multi-Modal Sensors,\u201d Proceedings of the 50th Anniversary Summit of Artificial Intelligence, Switzerland, July 2006: 163-164.

          <\/li>\n
        14. W. Bush and H.T. Siegelmann, \u201cGenetic based neurons,\u201d Computational and Systems Neuroscience (COSYNE), Salt Lake City, 2005: 69.

          <\/li>\n
        15. E. Bittman, Y. Chait, C.V. Hollot, M. Harrington and H. Siegelmann, \u201cIs the Mammalian Circadian Clock a Resonant-Circuit Oscillator?\u201d Society for Research on Biological Rhythms, Whistler, BC, 2004.

          <\/li>\n
        16. Y. Tong and H. Siegelmann, \u201cSimulation mammalian molecular circadian oscillators by dynamic gene network,\u201d Eighth Annual International Conference on Research in Computational Molecular Biology (RECOMB), San Diego CA, March 2004.

          <\/li>\n
        17. S. Lu, A. Guo, K. Becker, H. Siegelmann, P. Sebastiani, K. MacBeth, J. Jerry, \u201cMicroarray Analysis of Global Gene Expression in the Mammary Gland Following Estrogen and Progesterone Treatment of Ovariectomized Mice,\u201d Second Annual AACR International Conference on Frontiers in Cancer Prevention Research, Phoenix, Arizona, October 2003.<\/li>\n<\/ol>\n

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          <\/div>\n","protected":false},"excerpt":{"rendered":"

          Journal Publications   Book Chapters    In Proceedings   Abstracts and Short Papers Books Neural Networks and Analog Computation: Beyond the Turing Limit H.T. SiegelmannNeural Networks and Analog Computation:Beyond the Turing Limit, BirkhauserBoston, December 1998 Journal Publications Prithviraj Tarale, Edward Rietman and Hava T Siegelmann, \u201cDistributed Multi-Agent Lifelong Learning,\u201d TMLR (Transactions on Machine Learning Research), 21 January … Continue reading “Publications”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-8","page","type-page","status-publish","hentry","hfeed"],"_links":{"self":[{"href":"https:\/\/groups.cs.umass.edu\/binds\/wp-json\/wp\/v2\/pages\/8","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/groups.cs.umass.edu\/binds\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/groups.cs.umass.edu\/binds\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/binds\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/binds\/wp-json\/wp\/v2\/comments?post=8"}],"version-history":[{"count":58,"href":"https:\/\/groups.cs.umass.edu\/binds\/wp-json\/wp\/v2\/pages\/8\/revisions"}],"predecessor-version":[{"id":555,"href":"https:\/\/groups.cs.umass.edu\/binds\/wp-json\/wp\/v2\/pages\/8\/revisions\/555"}],"wp:attachment":[{"href":"https:\/\/groups.cs.umass.edu\/binds\/wp-json\/wp\/v2\/media?parent=8"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}