Back, T. and Schwefel, H. P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1):1-23.

Batali, J. (1994). Innate biases and critical periods: Combining evolution and learning in the acquisition of syntax. In Brooks, R. and Maes, P., editors, Proceedings of the Fourth Artificial Life Workshop, pages 160-171. MIT Press.

Batali, J. (1995). Small signaling systems can evolve in the absence of benefit to the information sender. Unpublished manuscript.

Batali, J. (1998). Computational simulations of the emergence of grammar. In Hurford, J. R., Knight, C., and Studdert-Kennedy, M., editors, Approaches to the Evolution of Language, pages 405-426. Cambridge University Press, Cambridge, England.

Batali, J. (in press). The negotiation and acquisition of recursive grammars as a result of competition among exemplars. In Briscoe, E. J., editor, Linguistic Evolution Through Language Acquisition: Formal and Computational Models. Cambridge University Press, Cambridge, UK.

Bengio, Y., Simard, P., and Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2):157-166.

Bickerton, D. (1983). Creole languages. Scientific American, 249(1):108-115.

Bickerton, D. (1990). Language and Species. University of Chicago Press.

Blair, A. D. and Pollack, J. B. (1997). Analysis of dynamical recognizers. Neural Computation, 9(5):1127-1142.

Blum, A. and Rivest, R. (1992). Training a 3-node neural network is NP-complete. Neural Networks, 5(1):117-128.

Boden, M., Wiles, J., Tonkes, B., and Blair, A. D. (1999). Learning to predict a context-free language: Analysis of dynamics in recurrent hidden units. In Proceedings of the Ninth International Conference on Artificial Neural Networks, pages 359-364. IEE.

Briscoe, E. J. (1998). Language as a complex adaptive system: Coevolution of language and of the language acquisition device. In Coppen, P.-A., van Halteren, H., and Teunissen, L., editors, Computation linguistics in the Netherlands 1997, pages 3-40, Amsterdam. Rodopi.

Briscoe, E. J. (2000). Macro and micro models of linguistic evolution. Submitted to volume arising from the Third (Paris) Evolution of Language Conference.

Cangelosi, A. (2001). Evolution of communication and language using signals, symbols and words. IEEE Transactions on Evolutionary Computation, 5(2):93-101.

Cangelosi, A. and Parisi, D. (1998). The emergence of a language in an evolving population of neural networks. Connection Science, 10(2):83-98.

Casey, M. P. (1996). The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction. Neural Computation, 8(6):1135-1178.

Chalmers, D. (1990). Why Fodor and Pylyshyn were wrong: The simplest refutation. In Proceedings of the 12th Annual Conference of the Cognitive Science Society, pages 340-347.

Chalup, S. and Blair, A. D. (1999). Hill climbing in recurrent neural networks for learning the a n b n c n language. In Gedeon, T., Wong, P., Halgamuge, S., Kasabov, N., Nauck, D., and Fukushima, K., editors, Proceedings of the Sixth International Conference on Neural Information Processing (ICONIP99), pages 508-513.

Chomsky, N. (1956). Three models for the description of language. IRE Transactions on Information Theory, 2(3):113-124.

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

Chomsky, N. (1965). Aspects of the Theory of Syntax. MIT Press.

Chomsky, N. (1981). Lectures on Government and Binding. Foris, Dordrecht.

Chomsky, N. (1986). Knowledge of Language. Praeger, New York.

Christiansen, M. H. (1995). Language as an organism -- implications for the evolution and acquisition of language. Unpublished manuscript.

Christiansen, M. H. and Chater, N. (1994). Generalization and connectionist languiage learning. Mind and Language, 9:273-287.

Christiansen, M. H. and Chater, N. (1999). Toward a connectionist model of recursion in human linguistic performance. Cognitive Science, 23:157-205.

Christiansen, M. H., Chater, N., and Seidenberg, M. S. (1999). Connectionist models of human language processing: Progress and prospects. Special issue of Cognitive Science, 23(4):415-634.

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

Cohen, D. (1981). On holy wars and a plea for peace. COMPUTER, 14(10):48-54.

Darwin, C. (1890). The Descent of Man and Selection in Relation to Sex. John Murray, Albermarle Street, London, second edition.

Deacon, T. W. (1997). The Symbolic Species: The Co-Evolution of Language and the Brain. W. W. Norton and Company, New York.

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

Elman, J. L. (1991). Distributed representations, simple recurrent networks and grammatical structure. Machine Learning, 7:195-224.

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

Elman, J. L. (1995). Language as a dynamical system. In Port, R. F. and van Gelder, T. J., editors, Mind as Motion: Explorations in the Dynamics of Cognition, chapter 8. MIT Press. Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., and

Plunkett, K. (1996). Rethinking Innateness: A Connectionist Perspective on Development. MIT Press, Boston.

Fodor, J. A. and Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, Special issue on Connections and Symbols:3-71.

Geman, S., Bienenstock, E., and Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4:1-58.

Gold, E. M. (1967). Language identification in the limit. Information and Control, 16:447-474.

Hadley, R. F. and Hayward, M. (1995). Strong semantic systematicity from unsupervised connectionist learning. In Moore, J. D. and Lehman, J. F., editors, Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society, pages 358-363.

Hare, M. and Elman, J. L. (1995). Learning and morphological change. Cognition, 56:61-98.

Hashimoto, T. and Ikegami, T. (1995). Communication network of symbolic grammar systems. In Y. Aizawa et al, editor, Proceedings of the International Conference on Dynamical Systems and Chaos, volume 2, pages 595-598, Singapore. World Scientific.

Hashimoto, T. and Ikegami, T. (1996). Emergence of net-grammar in communicating agents. BioSystems, 38:1-14.

Hurford, J. R. (1989). Biological evolution of the Saussurean sign as a component of the language acquisistion device. Lingua, 77:187-222.

Hurford, J. R., Studdert-Kennedy, M., and Knight, C. (1998). Approaches to the Evolution of Language: Social and Cognitive Bases. Cambridge University Press, Cambridge, England. Volume arising from the first Evolution of Language conference.

Hutchins, E. and Hazlehurst, B. (1995). How to invent a lexicon: The development of shared symbols in interaction. In Gilbert, N. and Conte, R., editors, Artificial Societies: The computer simulation of social life. UCL Press.

Jackendoff, R. S. (1977). X-bar syntax: A study of phrase structure. MIT Press.

Jagota, A., Plate, T., Shastri, L., and Sun, R. (1999). Connectionist symbol processing: Dead or alive? Neural Computing Surveys, 2:1-40.

Kirby, S. (1998). Fitness and the selective adaptation of language. In Hurford, J. R., Knight, C., and Studdert-Kennedy, M., editors, Approaches to the Evolution of Language. Cambridge University Press, Cambridge, England.

Kirby, S. (1999a). Function, Selection, and Innateness. Oxford University Press.

Kirby, S. (2000). Syntax without natural selection: How compositionality emerges from vocabulary in a population of learners. In Knight, C., Hurford, J. R., and Studdert-Kennedy, M., editors, The Evolutionary Emergence of Language: Social function and the origins of linguistic form, pages 303-323. Cambridge University Press, Cambridge, England.

Kirby, S. (2001). Spontaneous evolution of linguistic structure { an iterated learning model of the emergence of regularity and irregularity. IEEE Transactions on Evolutionary Computation, 5(2):102-110.

Kirby, S. (in press 1999b). Learning, bottlenecks and the evolution of recursive syntax. In Briscoe, E. J., editor, Linguistic Evolution through Language Acquisition: Formal and Computational Models. Cambridge University Press.

Knight, C., Hurford, J. R., and Studdert-Kennedy, M. (2000). The Evolutionary Emergence of Language: Social Function and the Origins of Linguistic Form. Cambridge University Press, Cambridge, England. Volume arising from the second Evolution of Language conference.

Kolen, J. F. and Pollack, J. B. (1990). Back-propagation is sensitive to initial conditions. Complex Systems, 4(3):269-280.

Lewis, D. K. (1969). Convention: A Philosophical Study. Harvard University Press.

Maass, W. and Orponen, P. (1998). On the effect of analog noise in discrete-time analog computations. Neural Computation, 10:1071-1095.

Maass, W. and Sontag, E. D. (1999). Analog neural nets with gaussian or other common noise distributions cannot recognize arbitrary regular languages. Neural Computation, 10(5):771-782.

MacLennan, B. J. and Burghardt, G. M. (1994). Synthetic ecology and the evolution of cooperative communication. Adaptive Behavior, 2(2):161-188.

McClelland, J. L. and Rumelhart, D. E. (1986). Parallel distributed processing: Explorations in the Microstructure of Cognition, Vol 2: Psychological and biological models. MIT Press, Cambridge, MA.

Moore, C. (1998). Dynamical recognizers: Real-time language recognition by analog computers. Theoretical Computer Science, 201(1-2):99-136.

Newmeyer, F. J. (1986). Linguistic Theory in America. Academic Press, second edition.

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

Niyogi, P. and Berwick, R. C. (1995a). A dynamical systems model for language change. Technical Report A.I. Memo No. 1515 and C.B.C.L. Paper No. 114, Massachusetts Institute of Technology Artificial Intelligence Laboratory and Center for Biological and Computational Learning, Department of Brain and Cognitive Sciences.

Niyogi, P. and Berwick, R. C. (1995b). The logical problem of language change. Technical Report A.I. Memo No. 1516 and C.B.C.L. Paper No. 115, Massachusetts Institute of Technology Artificial Intelligence Laboratory and Center for Biological and Computational Learning, Department of Brain and Cognitive Sciences.

Noble, W. and Davidson, I. (1996). Human Evolution, Language and Mind: A Psychological and Archaeological Inquiry. Cambridge University Press, Cambridge, England.

Nowak, M. A., Plotkin, J. B., and Jansen, V. A. A. (2000). The evolution of syntactic communication. Nature, 404:495-498.

Oliphant, M. (1999). The learning barrier: Moving from innate to learned systems of communication. Adaptive Behavior, 7(3-4):371-384.

Oliphant, M. and Batali, J. (1996). Learning and the emergence of coordinated communication. Submitted.

Pinker, S. (1994). The Language Instinct. William Morrow, New York.

Pinker, S. and Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences, 13:707-784.

Pollack, J. B. (1987). On connectionist models of natural language processing. PhD thesis, Computer Science Department, University of Illinois, Urbana, IL.

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

Rodriguez, P., Wiles, J., and Elman, J. L. (1999). A recurrent network that learns to count. Connection Science, 11(1):5-40.

Rohde, D. L. T. and Plaut, D. C. (1997). Simple recurrent networks and natural language: How important is starting small? In Proceedings of the 19th Annual Conference of the Cognitive Science Society, pages 656-661, Hillsdale, NJ. Erlbaum.

Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning internal representations by error propagation. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the microstructure of cognition, chapter 8, pages 318-361. MIT Press.

Rumelhart, D. E. and McClelland, J. L. (1986a). On learning the past tenses of English verbs, pages 216-271. In McClelland and Rumelhart (1986).

Rumelhart, D. E. and McClelland, J. L. (1986b). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol 1: Foundations. MIT Press, Cambridge, MA.

Saunders, G. and Pollack, J. B. (1996). The evolution of communication schemes over continuous channels. In Maes, P., Mataric, M. J., Meyer, J.-A., Pollack, J., and Wilson, S. W., editors, From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 580-589. MIT Press.

Savage-Rumbaugh, E. S. and Lewin, R. (1994). Kanzi: The Ape at the Brink of the Human Mind. John Wiley, New York.

Seidenberg, M. S. (1997). Language acquisition and use: Learning and applying probablistic constraints. Science, 275(5306):1599-1603.

Sejnowski, T. J. and Rosenberg, C. R. (1990). NETtalk: A parallel network that learns to read aloud. Cognitive Science, 14:179-211.

Senghas, A. (1995). Children's contribution to the birth of Nicaraguan Sign Language. PhD thesis, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.

Siegelmann, H. T. (1993). Foundations of Recurrent Neural Networks. PhD thesis, New Brunswick Rutgers, The State of New Jersey.

Steels, L. (1996). Emergent adaptive lexicons. In Maes, P., Mataric, M. J., Meyer, J.-A., Pollack, J., and Wilson, S. W., editors, From Animals to Animats 4: Proceedings of the Fourth International Conference on Sumulation of Adaptive Behaviour, pages 562-567.

Steels, L. (1997a). The origins of syntax in visually grounded robotic agents. In Pollack, M., editor, Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pages 1632-1641, Los Angeles. Morgan Kauffman.

Steels, L. (1997b). The synthetic modeling of language origins. Evolution of Communication, 1(1):1-34.

Sun, G. Z., Chen, H. H., Giles, C. L., Lee, Y. C., and Chen, D. (1990). Neural networks with external memory stack that learn context-free grammars from examples. In Proceedings of the Conference on Information Science and Systems, volume II, page 649, Princeton, NJ.

Tino, P., Horne, B. G., and Giles, C. L. (1995). Fixed points in two-neuron discrete time recurrent networks: Stability and bifurcation considerations. Technical Report UMIACS-TR-95-51 and CS-TR-3461, Institute for Advanced Computer Studies, University of Maryland.

Tonkes, B., Blair, A. D., and Wiles, J. (1998). Inductive bias in context-free language learning. In Downs, T., Frean, M., and Gallagher, M., editors, Proceedings of the Ninth Australian Conference on Neural Networks, pages 52-56.

Tonkes, B., Blair, A. D., and Wiles, J. (1999). A paradox of neural encoders and decoders, or, why don't we talk backwards? In McKay, B., Yao, X., Newton, C. S., Kim, J. H., and Furuhashi, T., editors, Simulated Evolution and Learning, volume 1585 of Lecture Notes in Artificial Intelligence, pages 357-364. Springer.

Tonkes, B., Blair, A. D., and Wiles, J. (2000). Evolving learnable languages. In Solla, S. A., Leen, T. K., and Muller, K.-R., editors, Advances in Neural Information Processing Systems 12, pages 66-72. MIT Press.

Tonkes, B. and Wiles, J. (in press). Methodological issues in simulating the emergence of language. In Wray, A., editor, The Transition to Language. Oxford University Press.

Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27(11):1134-1142.

van Gelder, T. J. (1990). Compositionality: A connectionist variation on a classical theme. Cognitive Science, 14:355-384.

van Gelder, T. J. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21:1-14.

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

Werner, G. M. and Dyer, M. G. (1991). Evolution of communication in artificial organisms. In Langton, C. G., Taylor, C., Farmer, J. D., and Rasmussen, S., editors, Artifical Life II, pages 659-687. Addison-Wesley.

Wiles, J. and Elman, J. L. (1995). Learning to count without a counter: A case study of dynamics and activation landscapes in recurrent networks. In Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society, pages 482-487, Cambridge, MA. MIT Press.

Williams, R. J. and Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1(2):270-280.

Wray, A. (1998). Protolanguage as a holistic system for social interaction. Language and Communication, 18:47-67.