Baldwin, J. M. (1896). A new factor in evolution. American Naturalist, 30, pp. 441--451.

Belew, R. K. (1989). When both individuals and populations search: Adding simple learning to the genetic algorithm. Proceedings of the 3rd International Conference on Genetic Algorithms, J. D. Schaefer, (ed.), Morgan Kaufman.

Belew, R. K., McInerney, J., and Schraudolph, N. N. (1991). Evolving networks: Using the genetic algorithm with connectionist learning. In Artificial Life II, SFI Studies in the Sciences of Complexity, Volume X. C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, (eds.), Addison-Wesley, pp. 511--547.

Chomsky, N. (1957). Syntactic Structures. The Hague: Mouton.

Chomsky, N. (1987). Knowledge of Language: Its Nature, Origin, and Use. New York: Praeger.

Christiansen, M. (1992). The (non)necessity of recursion in natural language processing. Proceedings of the Fourteenth Annual Meeting of the Cognitive Science Society, Indiana University, Bloomington.

Cleeremans, A., Servan-Schreiber, D. and McClelland, J. L. (1989). Finite state automata and simple recurrent networks. Neural Computation, 1, pp. 372--381.

Elman, J. L. (1990a). Finding structure in time. Cognitive Science, 14, pp. 179--211.

Elman, J. L. (1990b). Distributed representation, simple recurrent networks, and grammatical structure. Machine Learning, 7, pp. 195--225.

Elman, J.L. (1993). Learning and development in neural networks: The importance of starting small. Cognition, 48, pp. 71--99.

Fodor, J., & Pylyshyn, Z. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, pp. 3--71.

Giles, G. L., Sun, G. Z., Chen, H. H., Lee, Y. C., and Chen, D. (1990). ``Higher order recurrent networks & grammatical inference.'' In Advances in Neural Information Processing Systems, 2, D. S. Touretzky (ed.), San Mateo CA: Morgan Kaufman.

Gold, E. M. (1967). Language identification in the limit. Information and Control, 16, pp. 447--475.

Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley.

Hinton, G., & Nowlan, S. J. (1987). How learning can guide evolution. Complex Systems, 1, pp. 495--502.

Hopcroft, J. E. & Ullman, J. D., (1979). Introduction to Automata Theory, Languages, and Computation. Addison-Wesley.

Jordan, M. (1986). Serial Order: a Parallel Distributed Processing Approach. ICS Report No. 8604. Institute for Cognitive Science; University of California at San Diego.

Kolen, J. F., & Pollack, J. B. (1990). Back propagation is sensitive to initial conditions. Complex Systems 4, pp. 269--280.

Lennenberg, E. H. (1967). Biological Foundations of Language. New York: John Wiley & Sons, Inc.

Marchman, V. A. (1993). Constraints on plasticity in a connectionist model of the English past tense. Journal of Cognitive Neuroscience, 5, pp. 215--234.

Marcus, M. (1980). A Theory of Syntactic Recognition for Natural Language. Cambridge, MA: MIT Press.

Marler, P. (1987). The instict to learn. In The Epigenesis of Mind: Essays on Biology and Cognition. Susan Carey & Rochel Gelman (eds.), Hillsdale, New Jersey: Lawrence Erlbaum Associates. pp. 37--66.

Newport, E. (1990). Maturational constraints on language learning. Cognitive Science, 14, pp. 11--28.

Nolfi, S., Elman, J. L., and Parisi, D. (1990). Learning and Evolution in Neural Networks. CRL Technical Report 9019. Center for Research on Language. University of California at San Diego.

Pinker, S. (1989). Learnability and Cognition. Cambridge, MA: MIT Press.

Pollack, J. B. (1987). On Connectionist Models of Natural Language Processing. PhD Thesis, Computer Science Department, University of Illinois, Urbana. Available as Memorandum MCCS-87-100, Computing Research Laboratory, New Mexico State University.

Pollack, J. B. (1990). Language acquisition via strange automata. In Proceedings of the Twelfth Annual Conference of the Cognitive Science Society.

Pollack, J. B. (1991). The induction of dynamical recognizers. Machine Learning, 7, pp. 227--252.

Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations. David E. Rumelhart, James L. McClelland and the PDP Research Group. Cambridge, MA: MIT Press, pp. 318--362.

Servan-Schreiber, D., Cleeremans, A., and McClelland, J. L. (1988). Encoding sequential structure in simple recurrent networks. CMU Technical Report CMU-CS335-87). Carnegie-Mellon University, Department of Computer Science.

Siegelmann, H. T. (1993) Foundations of Recurrent Neural Networks. PhD Dissertation. Rutgers University, Graduate Program in Computer Science.

Sun, G. Z., Chen, H. H., Giles, G. L., Lee, Y. C., and Chen, D. (1990). Connectionist pushdown automata that learn context-free grammars. Proceedings of the International Joint Conference on Neural Networks, Vol. 1. M. Caudill (ed.) Hillsdale, NJ: Lawrence Erlbaum.

Weckerly, J., & Elman, J.L. (1992). A PDP approach to processing center-embedded sentences. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum.

Wexler, K., & Culicover, P. (1980). Formal Principles of Language Acquisition. Cambridge, MA: MIT Press.