Connection and coordination: The interplay between architecture and dynamics in evolved model pattern generators

Neural Computation

Volumn 18, Issue 3, 2006, Pages 729-747

  Psujek, Sean ^a   Ames, Jeffrey ^b   Beer, Randall D ^a  

^a CASE WESTERN RESERVE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Case Western Reserve University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33645727303     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2006.18.3.729     Document Type: Article

References (31)

1. Albert, R., Jeong, H., & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378-382.
2. Ames, J. C. (2003). Design methods for pattern generation circuits. Master's thesis, Case Western Reserve University.
3. Angeline, P. J., Saunders, G. M., & Pollack, J. B. (1994). An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans. Neural Networks, 5, 54-65.
4. Bäck, T. (1996). Evolutionary algorithms in theory and practice. New York: Oxford University Press.
5. Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509-512.
6. Beer, R. D. (1995). On the dynamics of small continuous-time recurrent neural networks. Adaptive Behavior, 3, 469-509.
7. Beer, R. D., Chiel, H. J., & Gallagher, J. C. (1999). Evolution and analysis of model CPGs for walking II. General principles and individual variability. J. Computational Neuroscience, 7, 119-147.
8. Beer, R. D., & Gallagher, J. C. (1992). Evolving dynamical neural networks for adaptive behavior Adaptive Behavior, 1, 91-122.
9. Braitenberg, V. (2001). Brain size and number of neurons: An exercise in synthetic neuroanatomy. J. Computational Neuroscience, 10, 71-77.
10. Chiel, H. J., Beer, R. D., & Gallagher, J. C. (1999). Evolution and analysis of model CPGs for walking I. Dynamical modules. J. Computational Neuroscience, 7, 99-118.
11. Garlaschelli, D., Caldarelli, G., & Pietronero, L. (2003). Universal scaling relations in food webs. Nature, 423, 165-168.
12. Getting, P. (1989). Emerging principles governing the operation of neural networks. Annual Review of Neuroscience, 12, 185-204.
13. Gruau, F. (1995). Automatic definition of modular neural networks. Adaptive Behavior, 3, 151-183.
14. Guelzim, N., Bottani, S., Bourgine, P., & Képès, F. (2002). Topological and causal structure of the yeast transcriptional regulatory network. Nature Genetics, 31, 60-63.
15. Harary, F. (1972). Graph theory. Reading, MA: Addison-Wesley.
16. Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000). The large-scale organization of metabolic networks. Nature, 407, 651-654.
17. Kodjabachian, J., & Meyer, J.-A. (1998). Evolution and development of neural controllers for locomotion, gradient-following, and obstacle avoidance in artificial insects. IEEE Trans. Neural Networks, 9, 796-812.
18. Marder, E., & Calabrese, R. L. (1996). Principles of rhythmic motor pattern generation. Physiological Reviews, 76, 687-717.
19. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298, 824-827.
20. Newman, M. E. J. (2003). The structure and function of complex networks. SLAM Review, 45, 167-256.
21. Pikovsky, A., Rosenblum, M., & Kurths, J. (2001). Synchronization: A universal concept in nonlinear sciences. Cambridge: Cambridge University Press.
22. Roberts, P. D. (1998). Classification of temporal patterns in dynamic biological networks. Neural Computation, 10, 1831-1846.
23. Sporns, O., Tononi, G., & Edelman, G. M. (2000). Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex, 10, 127-141.
24. Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10, 99-127.
25. Stelling, J. Klamt, S., Bettenbrock K., Schuster, S., & Gilles, E. D. (2002). Metabolic network structure determines key aspects of functionality and regulation. Nature, 420, 190-193.
26. Stevens, C. F. (1989). How cortical interconnectedness varies with network size. Neural Computation, 1, 473-479.
27. Stewart, I., Golubitsky, M., & Pivato, M. (2003). Symmetry groupoids and patterns of synchrony in coupled cell networks. SIAM J. Applied Dynamical Systems, 2, 609-646.
28. Strogatz, S. H. (2001). Exploring complex networks. Nature, 510, 268-276.
29. van Essen, D. C., Anderson, C. H., & Felleman, D. J. (1992). Information processing in the primate visual system: An integrated systems perspective. Science, 255, 419-123.
30. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of "small-world" networks. Nature, 393, 440-442.
31. Yao, X. (1999). Evolving artificial neural networks. Proc. of the IEEE, 87, 1423-1447.