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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 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
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