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Hava T. Siegelmann - Publications

Affiliations: 
Computer Science University of Massachusetts, Amherst, Amherst, MA 
Area:
Computer Science, Technology of Education
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45/101 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
2020 van de Ven GM, Siegelmann HT, Tolias AS. Brain-inspired replay for continual learning with artificial neural networks. Nature Communications. 11: 4069. PMID 32792531 DOI: 10.1038/S41467-020-17866-2  0.35
2019 Patel D, Hazan H, Saunders DJ, Siegelmann HT, Kozma R. Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game. Neural Networks : the Official Journal of the International Neural Network Society. PMID 31500931 DOI: 10.1016/J.Neunet.2019.08.009  0.33
2019 Saunders DJ, Patel D, Hazan H, Siegelmann HT, Kozma R. Locally connected spiking neural networks for unsupervised feature learning. Neural Networks : the Official Journal of the International Neural Network Society. 119: 332-340. PMID 31499357 DOI: 10.1016/J.Neunet.2019.08.016  0.338
2019 Hazan H, Saunders DJ, Sanghavi DT, Siegelmann HT, Kozma R. Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data Annals of Mathematics and Artificial Intelligence. 1-24. DOI: 10.1007/S10472-019-09665-3  0.338
2018 Hazan H, Saunders DJ, Khan H, Patel D, Sanghavi DT, Siegelmann HT, Kozma R. BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python. Frontiers in Neuroinformatics. 12: 89. PMID 30631269 DOI: 10.3389/Fninf.2018.00089  0.358
2018 Kozma R, Ilin R, Siegelmann HT. Evolution of Abstraction Across Layers in Deep Learning Neural Networks Procedia Computer Science. 144: 203-213. DOI: 10.1016/J.Procs.2018.10.520  0.314
2017 Burroni J, Taylor P, Corey C, Vachnadze T, Siegelmann HT. Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks. Frontiers in Neuroscience. 11: 80. PMID 28289370 DOI: 10.3389/Fnins.2017.00080  0.313
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  0.32
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  0.367
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  0.691
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  0.334
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  0.661
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  0.338
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  0.653
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  0.333
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  0.301
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  0.356
2010 Siegelmann HT. Complex systems science and brain dynamics. Frontiers in Computational Neuroscience. 4. PMID 20877423 DOI: 10.3389/Fncom.2010.00007  0.367
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  0.684
2010 Olsen MM, Harrington KI, Siegelmann HT. Conspecific Emotional Cooperation Biases Population Dynamics International Journal of Natural Computing Research. 1: 51-65. DOI: 10.4018/Jncr.2010070104  0.689
2010 Olsen M, Sitaraman R, Siegelmann-Danieli N, Siegelmann H. Abstract 2006: Mathematical and computational models for cellular space in cancer growth Cancer Research. 70: 2006-2006. DOI: 10.1158/1538-7445.Am10-2006  0.672
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  0.303
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  0.657
2008 Olsen MM, Harrington K, Siegelmann HT. Emotions for strategic real-time systems Aaai Spring Symposium - Technical Report. 104-110.  0.663
2006 Sivan S, Filo O, Siegelmann H. Application of expert networks for predicting proteins secondary structure. Biomolecular Engineering. 24: 237-43. PMID 17236807 DOI: 10.1016/J.Bioeng.2006.12.001  0.316
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  0.331
2004 Ben-Hur A, Siegelmann HT. Computation in gene networks. Chaos (Woodbury, N.Y.). 14: 145-51. PMID 15003055 DOI: 10.1063/1.1633371  0.357
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  0.308
2003 Siegelmann HT. Neural and super-Turing computing Minds and Machines. 13: 103-114. DOI: 10.1023/A:1021376718708  0.335
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  0.312
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  0.301
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  0.306
1999 Gavaldà R, Siegelmann HT. Discontinuities in recurrent neural networks Neural Computation. 11: 715-745. PMID 10085427 DOI: 10.1162/089976699300016638  0.352
1999 Siegelmann HT, Ben-Hur A, Fishman S. Computational complexity for continuous time dynamics Physical Review Letters. 83: 1463-1466. DOI: 10.1103/Physrevlett.83.1463  0.323
1999 Siegelmann HT. Stochastic analog networks and computational complexity Journal of Complexity. 15: 451-475. DOI: 10.1006/Jcom.1999.0505  0.364
1998 Siegelmann HT, Fishman S. Analog computation with dynamical systems Physica D: Nonlinear Phenomena. 120: 214-235. DOI: 10.1016/S0167-2789(98)00057-8  0.317
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  0.311
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  0.335
1996 Siegelmann HT. On nil: The software constructor of neural networks Parallel Processing Letters. 6: 575-582. DOI: 10.1142/S0129626496000510  0.358
1996 Siegelmann HT. The simple dynamics of super Turing theories Theoretical Computer Science. 168: 461-472. DOI: 10.1016/S0304-3975(96)00087-4  0.342
1996 Kilian J, Siegelmann HT. The Dynamic Universality of Sigmoidal Neural Networks Information and Computation. 128: 48-56. DOI: 10.1006/Inco.1996.0062  0.359
1995 Siegelmann HT. Computation beyond the turing limit Science. 268: 545-548. DOI: 10.1126/Science.268.5210.545  0.336
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  0.366
1994 Siegelmann HT, Sontag ED. Analog computation via neural networks Theoretical Computer Science. 131: 331-360. DOI: 10.1016/0304-3975(94)90178-3  0.354
1991 Siegelmann HT, Sontag ED. Turing computability with neural nets Applied Mathematics Letters. 4: 77-80. DOI: 10.1016/0893-9659(91)90080-F  0.332
Low-probability matches (unlikely to be authored by this person)
1997 Nissan E, Siegelmann H, Galperin A, Kimhi S. Upgrading automation for nuclear fuel in-core management: From the symbolic generation of configurations, to the neural adaptation of heuristics Engineering With Computers. 13: 1-19. DOI: 10.1007/Bf01201857  0.298
2012 Thivierge JP, Minai A, Siegelmann H, Alippi C, Geourgiopoulos M. A year of neural network research: special issue on the 2011 International Joint Conference on Neural Networks. Neural Networks : the Official Journal of the International Neural Network Society. 32: 1-2. PMID 22551620 DOI: 10.1016/J.Neunet.2012.03.010  0.298
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  0.295
2003 Neto JP, Siegelmann HT, Costa JF. Symbolic processing in neural networks Journal of the Brazilian Computer Society. 8: 58-70. DOI: 10.1590/S0104-65002003000100005  0.294
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  0.292
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  0.283
2010 Nowicki D, Siegelmann H. Flexible kernel memory. Plos One. 5: e10955. PMID 20552013 DOI: 10.1371/Journal.Pone.0010955  0.281
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  0.278
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  0.276
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  0.272
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  0.269
2007 Leise T, Siegelmann H. Dynamics of a multistage circadian system. Journal of Biological Rhythms. 21: 314-23. PMID 16864651 DOI: 10.1177/0748730406287281  0.269
2013 Nowicki D, Verga P, Siegelmann H. Modeling reconsolidation in kernel associative memory. Plos One. 8: e68189. PMID 23936300 DOI: 10.1371/Journal.Pone.0068189  0.264
2015 Taylor P, He Z, Bilgrien N, Siegelmann HT. Human Strategies for Multitasking, Search, and Control Improved via Real-Time Memory Aid for Gaze Location Frontiers in Ict. 2. DOI: 10.3389/Fict.2015.00015  0.256
2020 Shifrin M, Siegelmann H. Near-optimal Insulin Treatment for Diabetes Patients: A machine learning approach Artificial Intelligence in Medicine. 107: 101917. PMID 32828456 DOI: 10.1016/J.Artmed.2020.101917  0.256
2000 Lipson H, Siegelmann HT. Clustering irregular shapes using high-order neurons. Neural Computation. 12: 2331-53. PMID 11032037 DOI: 10.1162/089976600300014962  0.254
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  0.253
2013 Kagan E, Rybalov A, Siegelmann H, Yager R. Probability-generated aggregators International Journal of Intelligent Systems. 28: 709-727. DOI: 10.1002/Int.21598  0.247
2005 Loureiro O, Siegelmann H. Introducing an active cluster-based information retrieval paradigm Journal of the American Society For Information Science and Technology. 56: 1024-1030. DOI: 10.1002/Asi.20193  0.24
2000 Siegelmann HT, Roitershtein A, Ben-Hur A. Noisy neural networks and generalizations Advances in Neural Information Processing Systems. 335-341.  0.235
2017 McGuire SH, Rietman EA, Siegelmann H, Tuszynski JA. Gibbs free energy as a measure of complexity correlates with time within C. elegans embryonic development. Journal of Biological Physics. PMID 28929407 DOI: 10.1007/S10867-017-9469-0  0.232
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  0.227
2021 Hayes TL, Krishnan GP, Bazhenov M, Siegelmann HT, Sejnowski TJ, Kanan C. Replay in Deep Learning: Current Approaches and Missing Biological Elements. Neural Computation. 1-44. PMID 34474476 DOI: 10.1162/neco_a_01433  0.224
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  0.222
2020 Rietman EA, Taylor S, Siegelmann HT, Deriu MA, Cavaglia M, Tuszynski JA. Using the Gibbs Function as a Measure of Human Brain Development Trends from Fetal Stage to Advanced Age. International Journal of Molecular Sciences. 21. PMID 32046179 DOI: 10.3390/Ijms21031116  0.221
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.  0.218
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.  0.212
2020 Tsuda B, Tye KM, Siegelmann HT, Sejnowski TJ. A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex. Proceedings of the National Academy of Sciences of the United States of America. PMID 33154155 DOI: 10.1073/pnas.2009591117  0.21
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.  0.209
1996 Siegelmann HT. Recurrent neural networks and finite automata Computational Intelligence. 12: 566-574.  0.207
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  0.202
2023 Kohan A, Rietman EA, Siegelmann HT. Signal Propagation: The Framework for Learning and Inference in a Forward Pass. Ieee Transactions On Neural Networks and Learning Systems. PMID 37022224 DOI: 10.1109/TNNLS.2022.3230914  0.2
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  0.196
2021 Amgalan A, Taylor P, Mujica-Parodi LR, Siegelmann HT. Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters. Scientific Reports. 11: 5331. PMID 33674620 DOI: 10.1038/s41598-021-82461-4  0.191
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  0.189
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.  0.178
2008 Cooper DG, Katz D, Siegelmann HT. Emotional robotics: Tug of war Aaai Spring Symposium - Technical Report. 23-29.  0.174
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.  0.172
2014 Siegelmann H, Kagan E, Ben-Gal I. Honest signaling in the cooperative search 2014 Ieee 28th Convention of Electrical and Electronics Engineers in Israel, Ieeei 2014. DOI: 10.1109/EEEI.2014.7005774  0.16
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.  0.151
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.  0.15
2023 Rietman EA, Siegelmann HT, Klement GL, Tuszynski JA. Gibbs Energy and Gene Expression Combined as a New Technique for Selecting Drug Targets for Inhibiting Specific Protein-Protein Interactions. International Journal of Molecular Sciences. 24. PMID 37834096 DOI: 10.3390/ijms241914648  0.142
2014 Kagan E, Rybalov A, Sela A, Siegelmann H, Steshenko J. Probabilistic control and swarm dynamics in mobile robots and ants Biologically-Inspired Techniques For Knowledge Discovery and Data Mining. 11-47. DOI: 10.4018/978-1-4666-6078-6.ch002  0.14
2007 Bush WS, Siegelmann HT. Circadian synchrony in networks of protein rhythm driven neurons Complexity. 12: 46-46. DOI: 10.1002/cplx.20190  0.137
1995 Siegelmann H, Sontag E. On the Computational Power of Neural Nets Journal of Computer and System Sciences. 50: 132-150. DOI: 10.1006/jcss.1995.1013  0.128
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  0.108
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  0.108
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  0.103
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.  0.089
2023 Abookasis D, Shemesh D, Litwin A, Siegelmann HT, Didkovsky E, Ad-El DD. Single probe light reflectance spectroscopy and parameter spectrum feature extraction in experimental skin cancer detection and classification. Journal of Biophotonics. e202300001. PMID 37078262 DOI: 10.1002/jbio.202300001  0.075
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.  0.062
2001 Ben-Hur A, Horn D, Siegelmann HT, Vapnik V. A support vector method for clustering Advances in Neural Information Processing Systems.  0.056
2000 Ben-Hur A, Horn D, Siegelmann HT, Vapnik V. A support vector clustering method Proceedings - International Conference On Pattern Recognition. 15: 724-727.  0.056
2023 Mead EA, Wang Y, Patel S, Thekkumthala AP, Kepich R, Benn-Hirsch E, Lee V, Basaly A, Bergeson S, Siegelmann HT, Pietrzykowski AZ. miR-9 utilizes precursor pathways in adaptation to alcohol in mouse striatal neurons. Advances in Drug and Alcohol Research. 3. PMID 38116240 DOI: 10.3389/adar.2023.11323  0.03
2012 Siegelmann HT. Super turing as a cognitive reality Consciousness: Its Nature and Functions. 401-409.  0.012
2016 Siegelmann HT. Preface Theoretical Computer Science. 633: 2-3. DOI: 10.1016/j.tcs.2016.05.012  0.01
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