Selected citations from 1995 to mid 2000 for: ``On the computational power of neural nets,'' J. Comp. Syst. Sci. (1995): 132-150. (about 30+) Lawrence S, Giles CL, Fong S Natural language grammatical inference with recurrent neural networks IEEE T KNOWL DATA EN 12: (1) 126-140 JAN-FEB 2000 Hammer B On the approximation capability of recurrent neural networks NEUROCOMPUTING 31: (1-4) 107-123 MAR 2000 Nancovska I, Todorovski L, Jeglic A, et al. Deterministic predictive models for DC voltage reference source control IEEE T IND ELECTRON 47: (1) 186-192 FEB 2000 Siegelmann HT Stochastic analog networks and computational complexity J COMPLEXITY 15: (4) 451-475 DEC 1999 Freivalds R, Kinber E, Smith CH The functions of finite support: a canonical learning problem J EXP THEOR ARTIF IN 11: (4) 543-552 OCT-DEC 1999 Berent I, Pinker S, Shimron J Default nominal inflection in Hebrew: evidence for mental variables COGNITION 72: (1) 1-44 AUG 25 1999 Apolloni B, Zoppis I Subsymbolically managing pieces of symbolical functions for sorting IEEE T NEURAL NETWOR 10: (5) 1099-1122 SEP 1999 Giles CL, Omlin CW, Thornber KK Equivalence in knowledge representation: Automata, recurrent neural networks, and dynamical fuzzy systems P IEEE 87: (9) 1623-1640 SEP 1999 Siegelmann HT, Margenstern M Nine switch-affine neurons suffice for Turing universality NEURAL NETWORKS 12: (4-5) 593-600 JUN 1999 Gavalda R, Siegelmann HT Discontinuities in recurrent neural networks NEURAL COMPUT 11: (3) 715-745 APR 1 1999 Blondel VD, Tsitsiklis JN Complexity of stability and controllability of elementary hybrid systems AUTOMATICA 35: (3) 479-489 MAR 1999 Sperduti A Neural networks for processing data structures LECT NOTES ARTIF INT 1387: 121-144 1998 Sun GZ, Giles CL, Chen HH The neural network pushdown automaton: Architecture, dynamics and training LECT NOTES ARTIF INT 1387: 296-345 1998 Siegelmann HT Neural dynamics with stochasticity LECT NOTES ARTIF INT 1387: 346-369 1998 Nancovska I, Kranjec P, Jeglic A, et al. Case study of the predictive models used for stability improvement of the DC voltage reference source IEEE T INSTRUM MEAS 47: (6) 1487-1491 DEC 1998 Lin T, Horne BG, Giles CL How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies NEURAL NETWORKS 11: (5) 861-868 JUL 1998 Frasconi P, Gori M, Sperduti A A general framework for adaptive processing of data structures IEEE T NEURAL NETWOR 9: (5) 768-786 SEP 1998 Siegelmann HT, Fishman S Analog computation with dynamical systems PHYSICA D 120: (1-2) 214-235 SEP 1 1998 Koiran P, Sontag ED Vapnik-Chervonenkis dimension of recurrent neural networks DISCRETE APPL MATH 86: (1) 63-79 AUG 18 1998 Omlin CW, Thornber KK, Giles CL Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks IEEE T FUZZY SYST 6: (1) 76-89 FEB 1998 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 ADV ENG SOFTW 28: (9) 581-592 DEC 1997 Stiles BW, Sandberg IW, Ghosh J Complete memory structures for approximating nonlinear discrete-time mappings IEEE T NEURAL NETWOR 8: (6) 1397-1409 NOV 1997 Sontag E, Sussmann H Complete controllability of continuous-time recurrent neural networks SYST CONTROL LETT 30: (4) 177-183 MAY 1997 Balcazar JL, Gavalda R, Siegelmann HT Computational power of neural networks: A characterization in terms of Kolmogorov complexity IEEE T INFORM THEORY 43: (4) 1175-1183 JUL 1997 Sperduti A On the computational power of recurrent neural networks for structures NEURAL NETWORKS 10: (3) 395-400 APR 1997 Siegelmann HT, Horne BG, Giles CL Computational capabilities of recurrent NARX neural networks IEEE T SYST MAN CY B 27: (2) 208-215 APR 1997 Turan G, Vatan F On the computation of Boolean functions by analog circuits of bounded fan-in J COMPUT SYST SCI 54: (1) 199-212 FEB 1997 Siegelmann HT The simple dynamics of super Turing theories THEOR COMPUT SCI 168: (2) 461-472 NOV 20 1996 Koiran P A family of universal recurrent networks THEOR COMPUT SCI 168: (2) 473-480 NOV 20 1996 Siegelmann HT Recurrent neural networks and finite automata COMPUT INTELL 12: (4) 567-574 NOV 1996 Lin TN, Horne BG, Tino P, et al. Learning long-term dependencies in NARX recurrent neural networks IEEE T NEURAL NETWOR 7: (6) 1329-1338 NOV 1996 Siegelmann HT Recurrent neural networks LECT NOTES COMPUT SC 1000: 29-45 1995 Kilian J, Siegelmann HT The dynamic universality of sigmoidal neural networks INFORM COMPUT 128: (1) 48-56 JUL 10 1996 Sloman SA The empirical case for two systems of reasoning PSYCHOL BULL 119: (1) 3-22 JAN 1996