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- Jan-Alexander F. Heimel, A. C. C. Coolen
- Physical review. E, Statistical, nonlinear, andâ€¦
- 2001

We study the dynamics of the batch minority game, with random external information, using generating functional techniques introduced by De Dominicis. The relevant control parameter in this model isâ€¦ (More)

We study the dynamics of on-line learning in large (N â†’ âˆž) perceptrons, for the case of training sets with a structural O(N0) bias of the input vectors, by deriving exact and closed macroscopicâ€¦ (More)

- Anthony C. C. Coolen, Jan-Alexander F. Heimel, D. Sherrington
- Physical review. E, Statistical, nonlinear, andâ€¦
- 2002

We study the dynamics of a version of the batch minority game, with random external information and with different types of inhomogeneous decision noise (additive and multiplicative), usingâ€¦ (More)

We study the dynamics of on-line learning in large (N â†’ âˆž) perceptrons, for the case of training sets with a structural O(N 0) bias of the input vectors, by deriving exact and closed macroscopicâ€¦ (More)

We solve the dynamics of the on-line minority game (MG), with general types of decision noise, using generating functional techniques a la De Dominicis and the temporal regularization procedure ofâ€¦ (More)

We study the dynamics of supervised on-line learning of realizable tasks in feed-forward neural networks. We focus on the regime where the number of examples used for training is proportional to theâ€¦ (More)

- Jan-Alexander F. Heimel, Haim Sompolinksy
- Neurocomputing
- 2001

We study the behaviour of the Ben-Yishai hypercolumn model (Ben-Yishai et al., Proc. Natl. Acad. Sci. USA 92 (1995) 3844) under presentation of oriented stimuli, having extended this model byâ€¦ (More)

- BiasH, . C. Rae, Jan-Alexander F. Heimel, C. A.C., CoolenDepartment
- 2000

We study the dynamics of on-line learning in large (N ! 1) perceptrons, for the case of training sets with a structural O(N 0) bias of the input vectors, by deriving exact and closed macroscopicâ€¦ (More)

We study the dynamics of supervised on-line learning of realizable tasks in feed-forward neural networks. We focus on the regime where the number of examples used for training is proportional to theâ€¦ (More)

We study the dynamics of supervised on-line learning of realizable tasks in feed-forward neural networks. We focus on the regime where the number of examples used for training is proportional to theâ€¦ (More)