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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