Free Access
Issue
ESAIM: PS
Volume 13, January 2009
Page(s) 343 - 362
DOI https://doi.org/10.1051/ps:2008016
Published online 21 July 2009
  1. B. Aazhang and M.K. Varanasi, Multistage detection in asynchronous code division multiple acces communications. IEEE Trans. Commun. 38 (1990) 509–519. [CrossRef]
  2. S. Amariand and H.-F. Yanai, Auto-associative memory with two-stage dynamics of nonmonotonic neurons. IEEE Trans. Neural Networks 7 (1996) 803–815. [CrossRef]
  3. R.K. Bahr and J.S. Sadowski, Direct-sequence spread-spectrum multiple-access communications with random signature sequences: A large deviations analysis. IEEE Trans. Inform. Theory 37 (1991) 514–527. [CrossRef]
  4. A. Ben-Israel and A. Charnes, Contribution to the theory of generalized inverses. J. SIAM 11 (1963) 667–699.
  5. A. Bovier, Sharp upper bounds for perfect retrieval in the Hopfield model. J. Appl. Probab. 36 (1999) 941–950. [CrossRef] [MathSciNet]
  6. A. Bovier, Statistical mechanics of disordered system: A mathematical perspective. Cambridge Series in Statistical and Probabilistic Mathematics 18. Cambridge University Press (2006).
  7. A. Bovier and V. Gayrard, Hopfield models as a generalized mean field model, preprint. In Mathematics of spin glasses and neural networks, A. Bovier and P. Picco (Eds.). Progress in Probability, Birkhäuser (1998).
  8. R.M. Buehrer and B.D. Woerner, Analysis of adaptive multistage interference cancellation for CDMA using an improved Gaussian approximation. IEEE Trans. Commun. 44 (1996) 1308–1329. [CrossRef]
  9. R.M. Buehrer, A. Kaul, S. Striglis and B.D. Woerner, Analysis of DS-CDMA parallel interference cancellation with phase and timing errors. IEEE JSAC 14 (1996) 1522–1535.
  10. B. Crespi, Storage capacity of non-monotonic neurons. Neural Networks 12 (1999) 1377–1389. [CrossRef]
  11. P. de Jong, A Central Limit Theorem for Generalized Multilinear Forms. J. Multiv. Anal. 34 (1990) 275–289. [CrossRef]
  12. G. Dreyfus, I. Guyon and L. Personnaz, Information storage and retrieval in spin-glass like neural networks. J. Phys. Lett. 46 (1985) L359–L365. [CrossRef]
  13. G. Dreyfus, I. Guyon and L. Personnaz, Collective computational properties of neural networks: New learning mechanisms. Phys. Rev. A 34 (1986) 4217–4228. [CrossRef] [MathSciNet] [PubMed]
  14. P. Eichelsbacher and M. Löwe, A large deviation principle for m-variate von Mises-statistics and U-statistics. J. Theoret. Probab. 8 (1995) 807–824. [CrossRef] [MathSciNet]
  15. P. Eichelsbacher and M. Löwe, Moderate deviations for i.i.d. random variables. ESAIM: PS 7 (2003) 209–218. [CrossRef] [EDP Sciences]
  16. J.M. Holtzman, A simple, accurate method to calculate spread spectrum multiple-access error probabilities. IEEE Trans. Commun. 40 (1992) 461–464. [CrossRef]
  17. J.J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79 (1982) 2554–2558. [NASA ADS] [CrossRef]
  18. M. Juntti, Multiuser demodulation for DS-CDMA systems in fading channels, Ph.D. thesis, University of Oulu, Finland, 1998.
  19. I. Kanter and H. Sampolinski, Associative recall of memory without errors. Phys. Rev. A 35 (1987) 380–392. [CrossRef] [PubMed]
  20. M.J. Klok, G. Hooghiemstra, T. Ojanperä and R. Prasad, A novel technique for DS-CDMA system performance evaluation. VTC'99 spring, Houston, USA (1999).
  21. K. Kobayashi, On the capacity of a neuron with a nonmonotone output function. Network 2 (1991) 237–243. [CrossRef] [MathSciNet]
  22. W. König and P. Mörters, Brownian intersection local times: Upper tail asymptotics and thick points. Ann. Probab. 30 (2002) 1605–1656. [CrossRef] [MathSciNet]
  23. M. Latva-aho, Advanced receivers for wideband CDMA systems, Ph.D. thesis, University of Oulu, Finland, 1999.
  24. J.S. Lehnert and M.B. Pursley, Error probabilities for binary direct sequence spread-spectrum communications with random signature sequences. IEEE Trans. Commun. COM-35 (1987) 87–98.
  25. J.S. Lehnert and R.K. Morrow, Bit-to-bit-error dependence in slotted DS/SSMA packet systems with random signature sequences. IEEE Trans. Commun. COM-37 (1989) 1052–1061.
  26. M. Löwe, On the storage capacity of Hopfield models with weakly correlated patterns. Ann. Appl. Probab. 8 (1999) 1216–1250.
  27. M. Löwe and F. Vermet, The storage capacity of the Hopfield model and moderate deviations. Statist. Probab. Lett. 75 (2005) 237–248. [CrossRef] [MathSciNet]
  28. M. Löwe and F. Vermet, The Capacity of q-state Potts neural networks with parallel retrieval dynamics. Statist. Probab. Lett. 77 (2007) 1505–1514. [CrossRef] [MathSciNet]
  29. Mathematical aspects of spin glasses and neural networks, in A. Bovier and P. Picco (Eds.). Progress in Probability, Birkhäuser, Boston (1998).
  30. R. McEliece, E. Posner, E. Rodemich and S. Venkatesh, The capacity of the Hopfield associative memory. IEEE Trans. Inform. Theory 33 (1987) 461–482. [CrossRef] [MathSciNet]
  31. S.K. Mitra and C.R. Rao, Generalized inverse of matrices and its applications. Wiley, New York (1971).
  32. M. Morita, Associative memory with nonmonotone dynamics. Neural Networks 6 (1993) 115–126. [CrossRef]
  33. M. Morita, S. Yoshizawa and K. Nakano, Analysis and improvement of the dynamics of autocorrelation associative memory. Trans. Inst. Electron. Inform. Commun. Eng. Jpn J73-D-II (1990) 232–242.
  34. N. Nishimori and I. Opris, Retrieval process of an associative memory with nonmonotonic input-output function. IEEE Int. Conf. Neural Networks 1 (1993) 353–358. [CrossRef]
  35. G. Palm, Memory capacities of local rules for synaptic modification. Concepts Neurosci. 2 (1991) 97–128.
  36. L.A. Pastur and A.L. Figotin, Exactly soluble model of a spin-glas. Sov. J. Low Temp. Phys. 3 (1977) 378–383.
  37. D. Petritis, Thermodynamic formalism of neural computing; Nonlinear Phenomena of Complex Systems, volume 2, pp. 86–146. Kluwer Acad. Publ., Dordrecht (1996).
  38. P. Picco, Artificial neural networks. A review from Physical and Mathematical point of view. Ann. Inst. H. Poincaré, Section A 64 (1996) 289–307.
  39. R. Prasad, CDMA for wireless personal communications. Artech House (1996).
  40. E. Rio, Théorie asymptotique des processus aléatoires faiblement dépendants. Springer (Ed.), Paris (2000).
  41. M.O. Sunay and P.J. Mclane, Calculating error probabilities for DS CDMA systems: When not to use the Gaussian approximation. IEEE Globecom 3 (1996) 1744–1749.
  42. R. van der Hofstad and M.J. Klok, Improving the performance of third-generation wireless communication systems. Adv. Appl. Probab. 36 (2004) 1046–1084. [CrossRef]
  43. R. van der Hofstad, G. Hooghiemstra and M.J. Klok, Large deviations for code division multiple access systems. SIAM J. Appl. Math. 62 (2002) 1044–1065. [CrossRef]
  44. R. van der Hofstad, M. Löwe and F. Vermet, The effect of system load on the existence of bit-errors in CDMA with and without parallel interference cancelation. IEEE Trans. Inform. Theory 52 (2006) 4733–4741. [CrossRef] [MathSciNet]
  45. F. Vermet, Étude asymptotique d'un réseau neuronal : le modèle de mémoire associative de Hopfield, Ph.D. thesis, University of Rennes 1, France, 1994.

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