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Journal Publications (2014 and earlier)
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| Type | Author | Title | Publication Information | Date | Pages/Volume/Issue | Links |
|---|---|---|---|---|---|---|
| Jrn | H.T. Siegelmann and R. Freund | Bulletin of European Association for Theoretical Computer Science | EATCS | 2014 | number 114, pp. 265-269 | |
| Jrn | A. Tal, N. Peled, and H. T. Siegelmann | Biologically inspired load balancing mechanism in neocortical competitive learning | Frontiers in Neural Circuits | 2014 | doi: 10.3389/fncir.2014.00018 | |
| Jrn | J. Cabessa, H.T. Siegelmann | The Super-Turing Computational Power of Plastic Recurrent Neural Networks | International Journal of Neural Systems | 2014 | 24, 1450029 | |
| Jrn | D. Nowicki, P. Verga, H.T. Siegelmann | Modeling Reconsolidation in Kernel Associative Memory | PLOS One | 2013 | 8(8) e68189 | |
| Jrn | H. T. Siegelmann | Turing on Super-Turing and Adaptivity | Progress in Biophysics and Molecular Biology | 2013 | S0079-6107(13)00027-8 | |
| Jrn | E. Kagan, A. Rybalov, H. T. Siegelmann, and R. Yager | Probability-generated aggregators | International Journal of Intelligent Systems | 2013 | 28(7): 709-727 | - |
| Jrn | J. Cabessa and H. T. Siegelmann | The Computational Power of Interactive Recurrent Neural Networks | Neural Computation | 2012 | 24(4): 996-1019 | |
| Jrn | Frederick C. Harris, Jr., Jeffrey L. Krichmar, Hava Siegelmann, Hiroaki Wagatsuma | Biologically-Inspired Human-Robot Interactions – Developing More Natural Ways to Communicate with our Machines | IEEE Transactions on Autonomous Mental Development (editorial) | 2012 | 4(3),190-191 | - |
| Jrn | Jean-Philippe Thivierge, Ali Minai, Hava Siegelmann, Cesare Alippi, Michael Geourgiopoulos | A year of neural network research: Special Issue on the 2011 International Joint Conference on Neural Networks | Neural Networks (editorial) | 2012 | 32,1-2 | - |
| Jrn | H. T. Siegelmann | Addiction as a Dynamical Rationality Disorder | Frontiers of Electrical and Electronic Engineering (FEE) in China | 2011 | 1(6),151-158 | |
| Jrn | H. T. Siegelmann | Complex Systems Science and Brain Dynamics: A Special Topic | Frontiers in Computational Neuroscience | 2010 | 10.3389 | - |
| Jrn | L. Glass and H. T. Siegelmann | Logical and symbolic analysis of robust biological dynamics | Current Opinion in Genetics & Development 20 | 2010 | 644-649 | - |
| Jrn | H.T. Siegelmann and L.E. Holzman | Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference | Chaos 20 | 2010 | 037112 | |
| Jrn | D.V. Nowicki, H.T. Siegelmann | Flexible Kernel Memory | PLOS One 5(6) | 2010 | e10955 | |
| Jrn | 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) | 2010 | e10637 | |
| Jrn | K. Tu, D. Cooper, H.T. Siegelmann | Memory reconsolidation for natural language processing | Cogn Neurodyn 3 | 2009 | 365-372 | |
| Jrn | 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(2) | 2008 | 274-287 | |
| Jrn | 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 | 2008 | - | |
| Jrn | H.T. Siegelmann | Analog-Symbolic Memory that Tracks via Reconsoliation | Physica D 237(9) | 2008 | 1207-1214 | |
| Jrn | M.M. Olsen, N. Siegelmann-Danieli and H.T. Siegelmann | Robust Artificial Life Via Artificial Programmed Death | Artificial Intelligence 172(6-7) | 2008 | 884-898 | |
| Jrn | F. Roth, H.T. Siegelmann and 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 | |
| Jrn | S. Sivan, O. Filo and H. Siegelman | Application of Expert Networks for Predicting Proteins Secondary Structure | Biomolecular Engineering, Volume 24, Issue 2 | 2007 | 237-243 | |
| Jrn | W. Bush and H.T. Siegelmann | Circadian Synchrony in Networks of Protein Rhythm Driven Neurons | Complexity Volume 12, Issue 1 | 2006 | 67-72 | |
| Jrn | T. Leise and H Siegelmann | Dynamics of a multistage circadian system | Journal of Biological Rhythms, 21:4 | 2006 | 314-323 | |
| Jrn | L. Glass, T. J. Perkins, J. Mason, H. T. Siegelmann and R. Edwards | Chaotic Dynamics in an Electronic Model of a Genetic Network | Journal of Statistical Physics Volume 121 Numbers 5/6 | 2005 | 969-994 | |
| Jrn | Loureiro, O. and Siegelmann, H. | Introducing an Active Cluster-Based Information Retrieval Paradigm | Journal of the American Society for Information Science and Technology, vl 56, n. 10 | 2005 | 1024-1030 | |
| Jrn | A. Roitershtein, A. Ben-Hur and H.T. Siegelmann | On Probabilistic Analog Automata | Theoretical Computer Science, 320(2-3) | 2004 | 449-464 | |
| Jrn | A. Ben-Hur, H.T. Siegelmann | Computation in Gene Networks | Chaos: An Interdisciplinary Journal of Nonlinear Science, 14(1) | 2004 | 145-151 | |
| Jrn | A. Ben-Hur, J. Feinberg, S. Fishman and H. T. Siegelmann | Random matrix theory for the analysis of the performance of an analog computer: a scaling theory | Phys. Lett. A. 323(3-4) | 2004 | 204-209 | |
| Jrn | A. Ben-Hur, J. Feinberg, S. Fishman and H. T. Siegelmann | Probabilistic analysis of a differential equation for linear programming | Journal of Complexity, 19(4) | 2003 | 474-510 | |
| Jrn | J. P. Neto, H. T. Siegelmann, and J. F. Costa | Symbolic processing in neural networks | Journal of the Brazilian Computer Society, 8(3) | 2003 | - | |
| Jrn | 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) | 2002 | 79-85 | |
| Jrn | A. Ben-Hur, H.T. Siegelmann and S. Fishman. | A Theory of Complexity for Continuous Time Systems. | Journal of Complexity 18(1) | 2002 | 51-86 | |
| Jrn | H.T. Siegelmann | Neural and Super-Turing Computing | Minds and Machines 13(1) | 2003 | 103-114 | |
| Jrn | A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik | Support Vector Clustering | Journal of Machine Learning Research 2 | 2001 | 125-137 | |
| Jrn | H.T. Siegelmann A., Ben-Hur, S. Fishman | Comments on Attractor Computation | International Journal of Computing Anticipatory Systems, D.M. Dubois, ed. | 2000 | - | |
| Jrn | R. Edwards, H.T. Siegelmann, K. Aziza and L. Glass | Symbolic dynamics and computation in model gene networks | Chaos 11(1) | 2001 | 160-169 | |
| Jrn | H. Lipson and H.T. Siegelmann | Clustering Irregular Shapes Using High-Order Neurons | Neural Computation 12(10) | 2000 | 2331-2353 | |
| Jrn | 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) | 2000 | 822-826 | |
| Jrn | H. Karniely and H.T. Siegelmann | Sensor Registration Using Neural Networks | IEEE Transactions on Aerospace and Electronic Systems, 36(1) | 2000 | 85-98 | |
| Jrn | H.T. Siegelmann, | Stochastic Analog Networks and Computational Complexity | Journal of Complexity, 15(4) | 1999 | 451-475 | |
| Jrn | H.T. Siegelmann, A. Ben-Hur and S. Fishman | Computational Complexity for Continuous Time Dynamics | Physical Review Letters, 83(7) | 1999 | 1463-1466 | |
| Jrn | H.T. Siegelmann and M. Margenstern | Nine Switch-Affine Neurons Suffice for Turing Universality | Neural Networks, 12 | 1999 | 593-600 | |
| Jrn | R. Gavaldà and H.T. Siegelmann | Discontinuities in Recurrent Neural Networks | Neural Computation, 11(3) | 1999 | 715-745 | |
| Jrn | H.T. Siegelmann and S. Fishman | Analog Computation with Dynamical Systems | Physica D, 120 | 1998 | 120--214 | |
| Jrn | A. Galperin, Y. Kimhi, E. Nissan, and H.T. Siegelmann | FUELCON's Heuristics, their Rationale, and their Representations | The New Review of Applied Expert Systems, 4 | 1998 | 163-176 | abstract |
| Jrn | Joachim Utans, John Moody, Steve Rehfuss, and Hava Siegelmann | Input Variable Selection for Neural Networks: Application to Predicting the U.S. Business Cycle | Proceedings of Computational Intelligence in Financial Engineering, IEEE Press | 1995 | 118-122 | |
| Jrn | 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 | |
| Jrn | J.L. Balcázar, R. Gavaldà, and H.T. Siegelmann | Computational Power of Neural Networks: A Characterization in Terms of Kolmogorov Complexity | IEEE Transactions on Information Theory, 43(4) | 1997 | 1175-1183 | |
| Jrn | H.T. Siegelmann, B.G. Horne, and C.L.Giles | Computational Capabilities of Recurrent NARX Neural Networks | IEEE Transaction on Systems, Man and Cybernetics-part B: Cybernetics, 27(2) | 1997 | 208-215 | |
| Jrn | E. Nissan, H.T. Siegelmann, A. Galperin, and S. Kimhi, | Upgrading Automation for Nuclear Fuel In-Core Management: From the Symbolic Generation of Configurations, to the Neural Adaptation of Heuristics, | Engineering with Computers, 13(1) | 1997 | 1-19 | |
| Jrn | O. Frieder and H.T. Siegelmann | Multiprocessor Document Allocation: A Genetic Algorithm Approach | IEEE Transactions on Knowledge and Data Engineering, 9(4) | 1997 | 640-642 | |
| Jrn | H.T. Siegelmann and C.L. Giles | The Complexity of Language Recognition by Neural Networks | Journal of Neurocomputing; Special Issue Recurrent Networks for Sequence Processing, Editors: M. Gori, M. Mozer, A.H. Tsoi, W. Watrous, 15(3-4) | 1997 | 327-345 | |
| Jrn | H.T. Siegelmann, | On NIL: The Software Constructor of Neural Networks | Parallel Processing Letters, 6(4) | 1996 | 575-582 | abstract |
| Jrn | H.T. Siegelmann | The Simple Dynamics of Super Turing Theories | Theoretical Computer Science, 168(2)(special issue on UMC) | 1996 | 461-472 | |
| Jrn | H.T. Siegelmann | Recurrent Neural Networks and Finite Automata | Journal of Computational Intelligence, 12(4) | 1996 | 567-574 | |
| Jrn | J. Kilian and H.T. Siegelmann | The Dynamic Universality of Sigmoidal Neural Networks | Information and Computation, 128(1) | 1996 | 45-56 | |
| Jrn | H.T. Siegelmann | Analog Computational Power | Science, 271(19) | 1996 | - | |
| Jrn | B. DasGupta, H.T. Siegelmann and E. Sontag | On the Complexity of Training Neural Networks with Continuous Activation Functions | IEEE Transactions on Neural Networks, 6(6) | 1995 | 1490-1504 | |
| Jrn | H.T. Siegelmann | Computation Beyond the Turing Limit | Science, 238(28) | 1995 | - | |
| Jrn | H.T. Siegelmann and E.D. Sontag | On the Computational Power of Neural Nets, | Journal of Computer System Sciences, 50(1) | 1995 | 132-150 | |
| Jrn | H.T. Siegelmann and E.D. Sontag | Analog Computation via Neural Networks | Theoretical Computer Science, 131 | 1994 | 331-360 | |
| Jrn | H.T. Siegelmann and E.D. Sontag | Turing Computability with Neural Nets | Applied Mathematics Letters, 4(6) | 1991 | 77-80 |
© 2007-2008 BINDS Lab, Computer Science Department, UMass Amherst