Hypernetworks: A molecular evolutionary architecture for cognitive learning and memory

IEEE Computational Intelligence Magazine

Volumn 3, Issue 3, 2008, Pages 49-63

  Zhang, Byomg Tak ^a  

^a Seoul National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA STORAGE EQUIPMENT; EVOLUTIONARY ALGORITHMS; GAME THEORY; LEARNING SYSTEMS; LINGUISTICS; ROBOT LEARNING; SELF ASSEMBLY;

COGNITIVE LEARNING; COMPOSITIONALITY; COMPUTATIONAL INTELLIGENCE (CI); GAME ENVIRONMENT; GENERATIVE LEARNING; HIGHER ORDER; HUMAN LEARNING; HYPER GRAPH; HYPERNETWORKS; MACHINE-LEARNING; MOLECULAR SELF ASSEMBLIES; MULTI-MODAL; PROBABILISTIC RELATIONS; SELF-ORGANIZING PROCESSES; SYNTHETIC INTELLIGENCE; UNIFIED THEORY; VISUAL IMAGERY;

EDUCATION;

EID: 47849109461     PISSN: 1556603X     EISSN: None     Source Type: Journal    
DOI: 10.1109/MCI.2008.926615     Document Type: Article

References (98)

1. S.H. Barondes, Molecules and Mental Illness. Scientific American Library, 1993.
2. E. Baum, What Is Thought? MIT Press, 2004.
3. C. Berge, Graphs and Hypergraphs. North-Holland Publishing, 1973.
4. C.M. Bishop, Pattern Recogniiton and Machine Learning. Springer, 2006.
5. C.S. Calude, J. Casti, and M.J. Dinneen (Eds.), Unconventional Models of Computation. Springer-Verlag, 1998.
6. N.S. Cassimatis, "A cognitive substrate for achieving human-level intelligence," AI Magazine, pp. 45-56, Summer 2006.
7. R.G. Crowder, Principles of Learning and Memory. Lawrence Erlbaum, 1976.
8. W. Duch, R.J. Oentaryo, and M. Pasquier, "Cognitive architectures: Where do we go from here?," Proc. First Conf. on Artificial General Intelligence, University of Memphis, 2008.
9. R. Duda, P. Hart, and D. Stork, Pattern Classification. Wiley, 2000.
10. J. Eccles and D.N. Robinson, The Wonder of Being Human: Our Brain and Our Mind. Free Press, 1984.
11. G.M. Edelman, Wider Than the Sky. Yale University Press, 2004.
12. H. Eichenbaum, The Cognitive Neuroscience of Memory. Oxford University Press, 2002.
13. M. Eigen and R. Winkler, Laws of the Game: How the Principles of Nature Govern Chance. Princeton University Press, 1993.
14. J. Feldman and D. Ballard, "Connectionist models and their properties," Cognitive Science, vol. 6, no. 3, pp. 205-254, 1982.
15. J. Feldman, From Molecule to Metaphor: A Neural Theory of Language. MIT Press, 2006.
16. D. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, 1995.
17. K.D. Forbus and. T.R. Hinrichs, "Companion cognitive systems: A step toward human-level AI," AI Magazine, pp. 83-95, Summer 2006.
18. W.J. Freeman, Neurodynamics: An Exploration in Mesoscopic Brain Dynamics. Springer-Verlag, 2000.
19. J. Fuster, Cortex and Mind: Unifying Cognition. Oxford University Press, 2003.
20. L. Getoor, N. Friedman, D. Koller, A. Pfeffer, and B. Taskar, "Probabilistic relational models," In L. Getoor and B. Taskar (Eds.), Introduction to Statistical Relational Learning, pp. 129-174, 2007.
21. R.W. Gibbs Jr., Embodiment and Cognitive Science. Cambridge University Press, 2005.
22. B. Goertzel and C. Pennachin (Eds.), Artificial General Intelligence. Springer-Verlag, 2005.
23. S. Grillner and A.M. Graybiel (Eds.), Microcircuits: The Interface, between Neurons and Global Brain Function. MIT Press, 2006.
24. J.-W. Ha, J.-H. Eom, S.-C. Kim, and B.-T. Zhang, "Evolutionary hypernetwork models for aptamer-based cardiovascular disease diagnosis," The. Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 2709-2716, 2007.
25. J. Hawkins and S. Blakeslee, On Intelligence. Times Books, 2004.
26. S. Haykin, Neural Networks: A Comprehensive Foundation. Prentice Hall, 1994.
27. D.O. Hebb, The Organization of Behavior: A Neuropsychological Theory. Wiley, 1949.
28. R. Hecht-Nielsen, "Cogent confabulation," Neural Networks, vol. 18, no. 2, pp. 111-115, 2005.
29. G.E. Hinton and J.A. Anderson (Eds.), Parallel Models of Associative. Memory. Erlbaum, 1989.
30. W. Hirstein, Brain Fiction: Self-Deception and the Riddle of Confabulation. MIT Press, 2004.
31. J. Holland, Emergence: From Chaos to Order. Perseus Books, 1998.
32. J. Holland, "Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems," in Michalski, R., Carbonell, J., & Mitchell, T (Eds.), Machine Learning: An Artificial Intelligente Approach, vol. 2, Morgan Kaufmann, 1986.
33. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities," Proc., Nat. Acad. Sci., USA, vol. 79, no. 8, 1982, pp. 2554-2558, 1982.
34. S. Janson, T. Luczak, and A. Rucinski, Random Graphs. Wiley, 2000.
35. T. Jebara, Machine Learning: Discriminative and Generative. Kluwer, 2004.
36. G.F. Jones, "Integrated intelligent knowledge management," In Achieving Human-Level Intelligence through Integrated Systems and Research: Papers from the AAAI 2004 Fall Symposium, 2004.
37. M.I. Jordan (Ed.), Learning in. Graphical Models. MIT Press, 1998.
38. E. Kandel, In Search of Memory: The Emergence of a New Science of Mind. Norton, 2006.
39. S. Kauffman, Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, 1993.
40. J.-K. Kim and B.-T. Zhang, "Evolving hypernetworks for pattern classification," IEEE Congress on Evolutionary Computation (CEC 2001), pp. 1856-1862, 2007.
41. J.-S. Kim, J.-W. Lee, Y.-K. Noh, J.-Y. Park, D.-Y. Lee, K.-A. Yang, Y.G. Chai, J.-C. Kim, and B.-T. Zhang, "An evolutionary Monte Carlo algorithm for predicting DNA hybridization," BioSystems, vol. 91, no. 1, pp. 69-75, 2008.
42. S. Kim, S.-J. Kim, and B.-T. Zhang, "Evolving hypernetwork classifiers for microRNA expression profile analysis," IEEE Congress on Evolutionary Computation (CEC 2007), pp. 313-319, 2007.
43. C. Koch and J.L. Davis (Eds.), Large-Scale Neuronal Theories of the Brain. MIT Press, 1994.
44. T. Kohonen, Content-Addressable Memories. Springer-Verlag, Berlin, 1980.
45. J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
46. P. Langley, "Cognitive architectures and general intelligent systems," AI Magazine, pp. 33-44, Summer 2006.
47. I.-H. Lee, K.-A. Yang, J.-H. Lee, J.-Y. Park, Y.-K. Chae, J.-H. Lee, B.-T. Zhang, "The use of gold nanoparticle aggregation for DNA computation and logic-based biomolecular detection," Nanotechnology, 2008 (submitted).
48. H.-W. Lim, J.-E. Yun, H.-M.Jang, Y.-G. Chai, S.-I. Yoo, and B.-T. Zhang, "Version space learning with DNA molecules," Proc. 2002 Int. Annual Meeting on DNA Comp. (DNA8), LNCS 2568: 2003, pp. 143-155, 2003.
49. D.J.C. MacKay, Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
50. D.J. Marchette, Random Graphs for Statistical Pattern Recognition. Wiley, 2004.
51. S. Maslov and K. Sneppen, "Specificity and stability in topology of protein networks," Science, vol. 296, pp. 910-913, 2002.
52. J.L. McGaugh, Memory & Emotion: The Making of Lasting Memories. Columbia. University Press, 2003.
53. R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach. Tioga Publishing, 1983.
54. T. Mitchell, Machine Learning. McGraw-Hill, 1997.
55. M. Minsky, The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon & Schuster, 2006.
56. N.J. Nilsson, "Human-level artificial intelligence? Be serious!," AI Magazine, pp. 68-75, 2005.
57. D. Norman, Things That Make Us Smart: Defending Human Attributes in the Age. of the Machine. Addison-Wesley, 1993.
58. R.C O'Reilly, "Biologically based computational models of high-level cognition," Science, vol. 314, pp. 91-94, October 2006.
59. G. Paun (Ed.), Computing with Bio-Molecules: Theory and Experiments. Springer-Verlag, 1998.
60. M.A. Peterson and G. Rhodes (Eds.), Perception of Faces, Objects, and Scenes: Analytic and Holistic Processes. Oxford University Press, 2003.
61. T. Poggio, V. Torre, and C. Koch, "Computational vision and regularization theory," Nature, vol. 317, pp. 314-319, 1985.
62. R.F. Port and T. Van Gelder, (Eds.), Mind as Motion: Explorations in the Dynamics of Cognition. MIT Press, 1995.
63. F. Pulvermüller, The Neuroscience of Language: On Brain Circuits of Words and Serial Order. Cambridge University Press, 2003.
64. V.S. Ramachandran, The Emerging Mind. BBC/Profile Books, 2003.
65. D.E. Rumelhart and D.A. Norman, "Accretion, tuning and restructuring: Three modes of learning," In J. W. Cotton and R. Klatzky (Eds.), Semantic Factors in Cognition, Lawrence Erlbaum, 1976.
66. D.E. Rumelhart, J. MaClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructures of Cognition. 2 vols., MIT Press, 1986.
67. M. Scheutz (Ed.), Computationalism: Nm Directions. MIT Press, 2002.
68. F.O. Schmitt, D.M. Schneider, and D.M. Crothers (Eds.), Functional Linkage in Biomolecular Systems. Raven Press, 1975.
69. B. Schoelkopf, C Burges, A. Smola (Eds.), Advances in Kernel Methods: Support Vector Learning. MIT Press, 1999.
70. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
71. A.M. Sengupta, M. Djordjevic, and B.I. Shraiman, "Specificity and robustness in transcription control networks," Proc. Natl. Acad. Sci. USA, vol. 99, no. 4, 2002, pp. 2072-2077, 2002.
72. S.-Y. Shin, I.-H. Lee, D. Kim, and B.-T. Zhang, "Multi-objective evolutionary optimization of DNA sequences for reliable DNA computing," IEEE Trans. Evol. Comp., vol. 9, no. 2, pp. 143-158, 2005.
73. L.R. Squire and. E.R. Kandel, Memory: From Mind to Molecules. Scientific American Library, 1999.
74. R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction. MIT Press, 1998.
75. J.D. Sweatt, Mechanisms of Memory. Elsevier, 2003.
76. T. Thrun and L.Y. Pratt (Eds.), Learning To Learn. Kluwer, 1998.
77. K.J. Thurber and L.D. Wald, "Associative and parallel processors," ACM Computing Surveys, vol. 7, no. 4, pp. 215-225, 1975.
78. D. Tse et al., "Schemas and memory consolidation," Science, vol. 316, pp. 76-82, Apr. 2007.
79. L.G. Valiant, "A theory of the learnable," Communications of the ACM, vol. 27, no. 11, pp. 1134-1142, 1984.
80. L.M. Ward, Dynamical Cognitive Science. MIT Press, 2002.
81. B. Widrow, "Cognitive memory and its applications," Invited talk at The IEEE World Congress on Computational Intelligence (WCCI-2006), Vancouver, 2006.
82. X. Yao and M.M. Islam, "Evolving artificial neural network ensembles," IEEE Computational Intelligence. Magazine, vol. 3, no. 1, pp. 31-42, February 2008.
83. B.-T. Zhang, "Cognitive learning and the multimodal memory game: Toward human-level machine learning," IEEE World Congress on Computational Intelligence: Special Session on Cognitive Architectures for Human-Level. Intelligence (WCCI-2008), 2008 (in press).
84. B.-T. Zhang, "Random hypergraph models of learning and memory in biomolecular networks: Shorter-term adaptability vs. longer-term persistency," The First IEEE Symposium on Foundations of Computational Intelligence (FOCI'01), pp. 344-349, 2007.
85. B.-T. Zhang, "Hyperinteractionism: Computational investigations into artificial minds beyond symbolism, connectionism, and dynamicism," Natural Science, pp. 144-165, Summer 2005.
86. B.-T. Zhang, "Molecular nanobiointelligence computers: Computer science meets biotechnology, nanotechnology, and cognitive brain science," Communications Kor. Info. Sci. Soc., vol. 23, no. 5, pp. 41-56, 2005.
87. B.-T. Zhang, "Accelerated learning by active example selection," International Journal of Neural Systems, vol. 5, no. 1, pp. 67-75, 1994.
88. B.-T. Zhang, "Self-development learning: Constructing optimal size neural networks via incremental data, selection," Arbeitspapiere der GMD, no. 768, German National Research Center for Computer Science, St. Augustin/Bonn, 1993.
89. B.-T. Zhang and H.-Y. Jang, "A Bayesian algorithm for in vitro molecular evolution of pattern classifiers," Proc. 2004 Int. Annual Meeting on DNA Computing (DNA10), LNCS 3384: pp. 458-467, 2005.
90. B.-T. Zhang and H.-Y. Jang, "Molecular programming: Evolving genetic programs in a test tube," The Genetic, and Evolutionary Computation Conf. (GECCO 2005), pp. 1761-1768, 2005.
91. B.-T. Zhang and. J.-K. Kim, "DNA hypernetworks for information storage and retrieval," Proc. 2006 Int. Annual Meeting on DNA Computing (DNA12), LNCS 4287: pp. 298-307, 2006.
92. B.-T. Zhang and H. Mühlenbein, "Balancing accuracy and parsimony in genetic programming," Evolutionary Computation, vol. 3, no. 1, pp. 17-38, 1995.
93. B.-T. Zhang and. H. Mühlenbein, "Evolving optimal neural networks using genetic algorithms with Occam's razor," Complex. Systems, vol. 7, no. 3, pp. 199-220, 1993.
94. B.-T. Zhang, P. Ohm, and H. Mühlenbein, "Evolutionary induction of sparse neural trees," Evolutionary Computation, vol. 5, no. 2, pp. 213-236, 1997.
95. B.-T. Zhang and C.-H. Park, "Self-assembling hypernetworks for cognitive learning of linguistic memory," Proc. World Acad. Sci. Eng. Tech. (WASET), pp. 134-138, 2008.
96. B.-T. Zhang and G. Veenker, "Neural networks that teach themselves through genetic discovery of novel examples," Proceedings of the IEEE Int. Joint Conf Neural Networks (IJCNN'91), pp. 690-695, 1991.
97. B.-T. Zhang, J. Yang, and S.-W. CM, "Self-organizing latent lattice models for temporal gene expression profiling," Machine Learning, vol. 52, no. 1/2, pp. 67-89, 2003.
98. H.Z. Zimmer, A. Mecklinger, and U. Lindenberger, "Levels of binding: Types, mechanisms, and functions of binding in memory," In Zimmer, H. Z., Mecklinger, A., and. Lindenberger, U. (Eds.), The Handbook of Binding and Memory, Oxford University Press, 2006.