Coupled Dynamics in Host-Guest Complex Systems Duplicates Emergent Behavior in the Brain
The ability of the brain to organize information and generate the functional structures we use to act, think and communicate, is a common and easily observable natural phenomenon. In object-oriented analysis, these structures are represented by objects. Objects have been extensively studied and documented, but the process that creates them is not understood. In this work, a new class of discrete, deterministic, dissipative, host-guest dynamical systems is introduced. The new systems have extraordinary self-organizing properties. They can host information representing other physical systems and generate the same functional structures as the brain does. A simple mathematical model is proposed. The new systems are easy to simulate by computer, and measurements needed to confirm the assumptions are abundant and readily available. Experimental results presented here confirm the findings. Applications are many, but among the most immediate are object-oriented engineering, image and voice recognition, search engines, and Neuroscience.
[1] M. Prokopenko, F. Boschetti, and A. J. Ryan, "An Information-Theoretic
Primer on Complexity, Self-Organization, and Emergence." Complexity,
vol. 15, 9. 11-28, (2009).
[2] D. Miner, M. Pickett, and M. desJardins, "Understanding the Brains
Emergent Properties." Proc. Second Conference on Artificial General
Intelligence, Arlington, VA (2009).
[3] S. Pissanetzky, "The matrix model of computation," Proc. 12th SCI
Conference, Orlando, FL, 2008, vol. IV, pp. 184-189.
[4] S. Pissanetzky, Sparse Matrix Technology. London: Academic Press,
1984. Russian translation: Moscow: MIR, 1988. Electronic Edition (in
English), 2008.
[5] S. Pissanetzky, "A relational virtual machine for program evolution," in
Proc. 2007 Int. Conf. on Software Engineering Research and Practice,
Las Vegas, vol. I, pp. 144-150. In this publication, the model was
introduced with the name Relational Model of Computation, but was
later renamed as the Matrix Model of Computation because of a name
conflict.
[6] S. Pissanetzky, "A new universal model of computation and its
contribution to learning, intelligence, parallelism, ontologies, refactoring,
and the sharing of resources." Int. J. Computational Intelligence,
vol. 5, nbr.2, pp.143-173, August 22, 2009. Available on-line:
http://www.waset.org/journals/ijci/v5.php
[7] L. Lin, R. Osan, and J. Z. Tsien. "Organizing principles of real-time
memory encoding: neural clique assemblies and universal neural codes."
Trends in Neurosciences, Vol. 29, No. 1, pp. 48-57 (2006).
[8] J. Perotti et al. "Emergent Self-Organized Complex Network Topology
out of Stability Constraints." Phys. Rev. Letters, 103, 108701 (2009).
[9] S. Pissanetzky. "The New Theory of Objects and the Automatic Generation
of Intelligent Agents." Workshop on Automation and Robotics.
NASA Gilruth Center, Johnson Space Center, Houston, Texas. Sept. 25,
2009.
[10] S. Pissanetzky. "The Matrix Theory of Objects. An Update." AIAA
Houston Section. Annual Technical Symposium 2010. NASA/JSC
Gilruth Center. Houston, Texas. April 30, 2010.
[11] S. Pissanetzky, "A new type of structured artificial neural networks based
on the matrix model of computation," Proc. 2008 Int. Conf. on Artificial
Intelligence, Las Vegas, vol. I, pp. 251-257.
[12] S. Pissanetzky, "Applications of the matrix model of computation." Proc.
12th SCI Conference, Orlando, FL, 2008, vol. IV, pp. 190-195.
[13] M. Schmidt and H. Lipson, "Distilling free-form natural laws from
experimental data." Science, 324, pp. 81-85 (April 2009).
[14] S. Demeyer et al., "The LAN-simulation: a refactoring teaching example,"
Proc. 8th. Int. Workshop on Principles of Software. Evolution,
Lisbon, 2005, pp. 123-134.
[15] W. F. Opdyke, "Refactoring object-oriented frameworks," Ph.D. thesis,
Dep. Comp. Sc., Univ. of Illinois, Urbana-Champaign, 1992.
[16] H. Korn and P. Faure, "Is there chaos in the brain? II. Experimental
evidence and related models." Comptes Rendus Bilogies, 326, pp.787-
840 (2003).
[17] W. Chen, X. Li, J. Pu, and Q. Luo, "Spatial-temporal dynamics of
chaotic behavior in cultured hippocampal networks." Phys. Rev. E, 81,
061903 (2010).
[1] M. Prokopenko, F. Boschetti, and A. J. Ryan, "An Information-Theoretic
Primer on Complexity, Self-Organization, and Emergence." Complexity,
vol. 15, 9. 11-28, (2009).
[2] D. Miner, M. Pickett, and M. desJardins, "Understanding the Brains
Emergent Properties." Proc. Second Conference on Artificial General
Intelligence, Arlington, VA (2009).
[3] S. Pissanetzky, "The matrix model of computation," Proc. 12th SCI
Conference, Orlando, FL, 2008, vol. IV, pp. 184-189.
[4] S. Pissanetzky, Sparse Matrix Technology. London: Academic Press,
1984. Russian translation: Moscow: MIR, 1988. Electronic Edition (in
English), 2008.
[5] S. Pissanetzky, "A relational virtual machine for program evolution," in
Proc. 2007 Int. Conf. on Software Engineering Research and Practice,
Las Vegas, vol. I, pp. 144-150. In this publication, the model was
introduced with the name Relational Model of Computation, but was
later renamed as the Matrix Model of Computation because of a name
conflict.
[6] S. Pissanetzky, "A new universal model of computation and its
contribution to learning, intelligence, parallelism, ontologies, refactoring,
and the sharing of resources." Int. J. Computational Intelligence,
vol. 5, nbr.2, pp.143-173, August 22, 2009. Available on-line:
http://www.waset.org/journals/ijci/v5.php
[7] L. Lin, R. Osan, and J. Z. Tsien. "Organizing principles of real-time
memory encoding: neural clique assemblies and universal neural codes."
Trends in Neurosciences, Vol. 29, No. 1, pp. 48-57 (2006).
[8] J. Perotti et al. "Emergent Self-Organized Complex Network Topology
out of Stability Constraints." Phys. Rev. Letters, 103, 108701 (2009).
[9] S. Pissanetzky. "The New Theory of Objects and the Automatic Generation
of Intelligent Agents." Workshop on Automation and Robotics.
NASA Gilruth Center, Johnson Space Center, Houston, Texas. Sept. 25,
2009.
[10] S. Pissanetzky. "The Matrix Theory of Objects. An Update." AIAA
Houston Section. Annual Technical Symposium 2010. NASA/JSC
Gilruth Center. Houston, Texas. April 30, 2010.
[11] S. Pissanetzky, "A new type of structured artificial neural networks based
on the matrix model of computation," Proc. 2008 Int. Conf. on Artificial
Intelligence, Las Vegas, vol. I, pp. 251-257.
[12] S. Pissanetzky, "Applications of the matrix model of computation." Proc.
12th SCI Conference, Orlando, FL, 2008, vol. IV, pp. 190-195.
[13] M. Schmidt and H. Lipson, "Distilling free-form natural laws from
experimental data." Science, 324, pp. 81-85 (April 2009).
[14] S. Demeyer et al., "The LAN-simulation: a refactoring teaching example,"
Proc. 8th. Int. Workshop on Principles of Software. Evolution,
Lisbon, 2005, pp. 123-134.
[15] W. F. Opdyke, "Refactoring object-oriented frameworks," Ph.D. thesis,
Dep. Comp. Sc., Univ. of Illinois, Urbana-Champaign, 1992.
[16] H. Korn and P. Faure, "Is there chaos in the brain? II. Experimental
evidence and related models." Comptes Rendus Bilogies, 326, pp.787-
840 (2003).
[17] W. Chen, X. Li, J. Pu, and Q. Luo, "Spatial-temporal dynamics of
chaotic behavior in cultured hippocampal networks." Phys. Rev. E, 81,
061903 (2010).
@article{"International Journal of Engineering, Mathematical and Physical Sciences:57639", author = "Sergio Pissanetzky", title = "Coupled Dynamics in Host-Guest Complex Systems Duplicates Emergent Behavior in the Brain", abstract = "The ability of the brain to organize information and generate the functional structures we use to act, think and communicate, is a common and easily observable natural phenomenon. In object-oriented analysis, these structures are represented by objects. Objects have been extensively studied and documented, but the process that creates them is not understood. In this work, a new class of discrete, deterministic, dissipative, host-guest dynamical systems is introduced. The new systems have extraordinary self-organizing properties. They can host information representing other physical systems and generate the same functional structures as the brain does. A simple mathematical model is proposed. The new systems are easy to simulate by computer, and measurements needed to confirm the assumptions are abundant and readily available. Experimental results presented here confirm the findings. Applications are many, but among the most immediate are object-oriented engineering, image and voice recognition, search engines, and Neuroscience.
", keywords = "AI, artificial intelligence, complex system, object oriented, OO, refactoring.", volume = "4", number = "8", pages = "1157-9", }
