Learning and encoding higher order rules in neural networks

Author:

Levine Daniel S.

Publisher

Springer Science and Business Media LLC

Subject

General Psychology,Psychology (miscellaneous),Experimental and Cognitive Psychology

Reference29 articles.

1. Anderson, J. A., Spoehr, K. T., &Bennett, D. J. (1994). A study in numerical perversity: Teaching arithmetic to a neural network. In D. S. Levine & M. Aparicio IV (Eds.),Neural networks for knowledge representation and inference (pp. 311–335). Hillsdale, NJ: Erlbaum.

2. Aparicio, M., IV, &Levine, D. S. (1994). Why are neural networks relevant to higher cognitive function? In D. S. Levine & M. Aparicio IV (Eds.),Neural networks for knowledge representation and inference (pp. 1–26). Hillsdale, NJ: Erlbaum.

3. Bapi, R. S., &Levine, D. S. (1994). Modeling the role of the frontal lobes in sequential task performance: I. Basic structure and primacy effects.Neural Networks,7, 1167–1180.

4. Bapi, R. S., &Levine, D. S. (1995).Modeling the role of the frontal lobes in sequential task performance: II. Classification of sequences. Manuscript submitted for publication.

5. Bremner, F. J., &Gotts, S. J. (1995). Shades of Braitenberg’s Vehicle 5.Behavior Research Methods, Instruments, & Computers,27, 169–172.

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