Hava T. Siegelmann - Publications

Computer Science University of Massachusetts, Amherst, Amherst, MA 
Computer Science, Technology of Education

68 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2015 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. Scientific Reports. 5: 18112. PMID 26669858 DOI: 10.1038/srep18112  1
2014 Cabessa J, Siegelmann HT. The super-Turing computational power of plastic recurrent neural networks. International Journal of Neural Systems. 24: 1450029. PMID 25354762 DOI: 10.1142/S0129065714500294  1
2014 Tal A, Peled N, Siegelmann HT. Biologically inspired load balancing mechanism in neocortical competitive learning. Frontiers in Neural Circuits. 8: 18. PMID 24653679 DOI: 10.3389/fncir.2014.00018  1
2013 Siegelmann HT. Turing on Super-Turing and adaptivity. Progress in Biophysics and Molecular Biology. 113: 117-26. PMID 23583352 DOI: 10.1016/j.pbiomolbio.2013.03.013  1
2013 Olsen MM, Siegelmann HT. Multiscale agent-based model of tumor angiogenesis Procedia Computer Science. 18: 1016-1025. DOI: 10.1016/j.procs.2013.05.267  1
2012 Cabessa J, Siegelmann HT. The computational power of interactive recurrent neural networks. Neural Computation. 24: 996-1019. PMID 22295978 DOI: 10.1162/NECO_a_00263  1
2012 Harris F, Krichmar J, Siegelmann H, Wagatsuma H. Guest editorial: Biologically inspired human-robot interactionsdeveloping more natural ways to communicate with our machines Ieee Transactions On Autonomous Mental Development. 4: 190-191. DOI: 10.1109/TAMD.2012.2216703  1
2012 Siegelmann HT. Super turing as a cognitive reality Consciousness: Its Nature and Functions. 401-409.  1
2011 Harrington KI, Olsen MM, Siegelmann HT. Communicated somatic markers benefit both the individual and the species Proceedings of the International Joint Conference On Neural Networks. 3272-3278. DOI: 10.1109/IJCNN.2011.6033655  1
2011 Cabessa J, Siegelmann HT. Evolving recurrent neural networks are super-Turing Proceedings of the International Joint Conference On Neural Networks. 3200-3206. DOI: 10.1109/IJCNN.2011.6033645  1
2011 Siegelmann HT. Addiction as a dynamical rationality disorder Frontiers of Electrical and Electronic Engineering in China. 6: 151-158. DOI: 10.1007/s11460-011-0134-2  1
2010 Glass L, Siegelmann HT. Logical and symbolic analysis of robust biological dynamics. Current Opinion in Genetics & Development. 20: 644-9. PMID 20961750 DOI: 10.1016/j.gde.2010.09.005  1
2010 Siegelmann HT, Holzman LE. Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference. Chaos (Woodbury, N.Y.). 20: 037112. PMID 20887078 DOI: 10.1063/1.3491237  1
2010 Siegelmann HT. Complex systems science and brain dynamics. Frontiers in Computational Neuroscience. 4. PMID 20877423 DOI: 10.3389/fncom.2010.00007  1
2010 Olsen M, Siegelmann-Danieli N, Siegelmann HT. Dynamic computational model suggests that cellular citizenship is fundamental for selective tumor apoptosis. Plos One. 5: e10637. PMID 20498709 DOI: 10.1371/journal.pone.0010637  1
2010 Levy YZ, Levy D, Meyer JS, Siegelmann HT. Identification and control of intrinsic bias in a multiscale computational model of drug addiction Proceedings of the Acm Symposium On Applied Computing. 2389-2393. DOI: 10.1145/1774088.1774584  1
2009 Tu K, Cooper DG, Siegelmann HT. Memory reconsolidation for natural language processing. Cognitive Neurodynamics. 3: 365-72. PMID 19862641 DOI: 10.1007/s11571-009-9097-x  1
2009 Levy YZ, Levy D, Meyer JS, Siegelmann HT. Drug addiction as a non-monotonic process: A multiscale computational model Ifmbe Proceedings. 23: 1688-1691. DOI: 10.1007/978-3-540-92841-6_419  1
2009 Levy YZ, Levy D, Meyer JS, Siegelmann HT. DRUG addiction: A computational multiscale model combining neuropsychology, cognition and behavior Biosignals 2009 - Proceedings of the 2nd International Conference On Bio-Inspired Systems and Signal Processing. 87-94.  1
2008 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. 59: 274-87. PMID 18667155 DOI: 10.1016/j.neuron.2008.05.032  1
2008 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. 149: 4809-20. PMID 18556351 DOI: 10.1210/en.2008-0035  1
2008 Siegelmann HT. Analog-symbolic memory that tracks via reconsolidation Physica D: Nonlinear Phenomena. 237: 1207-1214. DOI: 10.1016/j.physd.2008.03.038  1
2008 Olsen MM, Siegelmann-Danieli N, Siegelmann HT. Robust artificial life via artificial programmed death Artificial Intelligence. 172: 884-898. DOI: 10.1016/j.artint.2007.10.015  1
2008 Cooper DG, Katz D, Siegelmann HT. Emotional robotics: Tug of war Aaai Spring Symposium - Technical Report. 23-29.  1
2008 Olsen MM, Harrington K, Siegelmann HT. Emotions for strategic real-time systems Aaai Spring Symposium - Technical Report. 104-110.  1
2007 Roth F, Siegelmann H, Douglas RJ. The self-construction and -repair of a foraging organism by explicitly specified development from a single cell. Artificial Life. 13: 347-68. PMID 17716016 DOI: 10.1162/artl.2007.13.4.347  1
2007 Harrington KI, Siegelmann HT. Adaptive multi-modal sensors Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4850: 164-173.  1
2005 Glass L, Perkins TJ, Mason J, Siegelmann HT, Edwards R. Chaotic dynamics in an electronic model of a genetic network Journal of Statistical Physics. 121: 989-994. DOI: 10.1007/s10955-005-7009-y  1
2004 Ben-Hur A, Siegelmann HT. Computation in gene networks. Chaos (Woodbury, N.Y.). 14: 145-51. PMID 15003055 DOI: 10.1063/1.1633371  1
2004 Ben-Hur A, Roitershtein A, Siegelmann HT. On probabilistic analog automata Theoretical Computer Science. 320: 449-464. DOI: 10.1016/j.tcs.2004.03.003  1
2004 Ben-Hur A, Feinberg J, Fishman S, Siegelmann HT. Random matrix theory for the analysis of the performance of an analog computer: A scaling theory Physics Letters, Section a: General, Atomic and Solid State Physics. 323: 204-209. DOI: 10.1016/j.physleta.2004.01.069  1
2003 Siegelmann HT. Neural and super-Turing computing Minds and Machines. 13: 103-114. DOI: 10.1023/A:1021376718708  1
2003 Ben-Hur A, Feinberg J, Fishman S, Siegelmann HT. Probabilistic analysis of a differential equation for linear programming Journal of Complexity. 19: 474-510. DOI: 10.1016/S0885-064X(03)00032-3  1
2003 Neto JP, Siegelmann HT, Costa JF. Symbolic processing in neural networks Journal of the Brazilian Computer Society. 8: 58-70.  1
2002 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 Journal of Surgery. 26: 79-85. PMID 11898038 DOI: 10.1007/s00268-001-0185-2  1
2002 Ben-Hur A, Siegelmann HT, Fishman S. A theory of complexity for continuous time systems Journal of Complexity. 18: 51-86. DOI: 10.1006/jcom.2001.0581  1
2001 Edwards R, Siegelmann HT, Aziza K, Glass L. Symbolic dynamics and computation in model gene networks Chaos. 11: 160-169. DOI: 10.1063/1.1336498  1
2001 Ben-Hur A, Horn D, Siegelmann HT, Vapnik V. A support vector method for clustering Advances in Neural Information Processing Systems 1
2001 Rodrigues P, Costa JF, Siegelmann HT. Verifying properties of neural networks Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2084: 158-165.  1
2000 Lipson H, Siegelmann HT. Clustering irregular shapes using high-order neurons. Neural Computation. 12: 2331-53. PMID 11032037  1
2000 Lange DH, Siegelmann HT, Pratt H, Inbar GF. Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials. Ieee Transactions On Bio-Medical Engineering. 47: 822-6. PMID 10833858 DOI: 10.1109/10.844236  1
2000 Karniely H, Siegelmann HT. Sensor registration using neural networks Ieee Transactions On Aerospace and Electronic Systems. 36: 85-101. DOI: 10.1109/7.826314  1
2000 Lipson H, Siegelmann HT. High order eigentensors as symbolic rules in competitive learning Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 1778: 286-297.  1
2000 Ben-Hur A, Horn D, Siegelmann HT, Vapnik V. A support vector clustering method Proceedings - International Conference On Pattern Recognition. 15: 724-727.  1
2000 Siegelmann HT, Roitershtein A, Ben-Hur A. Noisy neural networks and generalizations Advances in Neural Information Processing Systems. 335-341.  1
1999 Gavaldà R, Siegelmann HT. Discontinuities in recurrent neural networks Neural Computation. 11: 715-745. PMID 10085427  1
1999 Siegelmann HT, Margenstern M. Nine switch-affine neurons suffice for Turing universality Neural Networks. 12: 593-600. DOI: 10.1016/S0893-6080(99)00025-8  1
1999 Siegelmann HT. Stochastic analog networks and computational complexity Journal of Complexity. 15: 451-475. DOI: 10.1006/jcom.1999.0505  1
1999 Siegelmann HT, Ben-Hur A, Fishman S. Computational complexity for continuous time dynamics Physical Review Letters. 83: 1463-1466.  1
1998 Siegelmann HT, Fishman S. Analog computation with dynamical systems Physica D: Nonlinear Phenomena. 120: 214-235.  1
1998 Galperin A, Kimhi Y, Nissan E, Siegelmann HT. FUELCON'S heuristics, their rationale, and their representations New Review of Applied Expert Systems and Emerging Technologies. 4: 163-176.  1
1998 Lange DH, Siegelmann HT, Pratt H, Inbar GF. A generic approach for identification of event related brain potentials via a competitive neural network structure Advances in Neural Information Processing Systems. 901-907.  1
1997 Frieder O, Siegelmann HT. Multiprocessor document allocation: A genetic algorithm approach Ieee Transactions On Knowledge and Data Engineering. 9: 640-642. DOI: 10.1109/69.617055  1
1997 Siegelmann HT, Horne BG, Giles CL. Computational capabilities of recurrent NARX neural networks Ieee Transactions On Systems, Man, and Cybernetics, Part B: Cybernetics. 27: 208-215. DOI: 10.1109/3477.558801  1
1997 Balcázar JL, Gavaldà R, Siegelmann HT. Computational power of neural networks: A characterization in terms of Kolmogorov complexity Ieee Transactions On Information Theory. 43: 1175-1183. DOI: 10.1109/18.605580  1
1997 Siegelmann HT, Giles CL. The complexity of language recognition by neural networks Neurocomputing. 15: 327-345. DOI: 10.1016/S0925-2312(97)00015-5  1
1997 Neto JP, Siegelmann HT, Costa JF, Araujo CPS. Turing universality of neural nets (revisited) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1333: 361-366. DOI: 10.1007/BFb0025058  1
1997 Siegelmann HT, Nissan E, Galperin A. A novel neural/symbolic hybrid approach to heuristically optimized fuel allocation and automated revision of heuristics in nuclear engineering Advances in Engineering Software. 28: 581-592.  1
1996 Siegelmann HT. The simple dynamics of super Turing theories Theoretical Computer Science. 168: 461-472. DOI: 10.1016/S0304-3975(96)00087-4  1
1996 Kilian J, Siegelmann HT. The Dynamic Universality of Sigmoidal Neural Networks Information and Computation. 128: 48-56. DOI: 10.1006/inco.1996.0062  1
1996 Siegelmann HT. Recurrent neural networks and finite automata Computational Intelligence. 12: 566-574.  1
1996 Siegelmann HT. On nil: The software constructor of neural networks Parallel Processing Letters. 6: 575-582.  1
1995 DasGupta B, Siegelmann HT, Sontag E. On the Complexity of Training Neural Networks with Continuous Activation Functions Ieee Transactions On Neural Networks. 6: 1490-1504. DOI: 10.1109/72.471360  1
1995 Siegelmann HT. Computation beyond the turing limit Science. 268: 545-548.  1
1995 Siegelmann HT. Welcoming the super turing theories Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1012: 83-94.  1
1995 Horne BG, Siegelmann HT, Lee Giles C. What NARX networks can compute Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1012: 95-102.  1
1994 Siegelmann HT, Sontag ED. Analog computation via neural networks Theoretical Computer Science. 131: 331-360. DOI: 10.1016/0304-3975(94)90178-3  1
1991 Siegelmann HT, Sontag ED. Turing computability with neural nets Applied Mathematics Letters. 4: 77-80. DOI: 10.1016/0893-9659(91)90080-F  1
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