Summary

Proceedings of the 2012 International Symposium on Nonlinear Theory and its Applications

2012

Session Number:B1L-B

Session:

Number:300

Learning and memory phenomena in a complex sensory environment: a neuroheuristic approach

J. Cabessa,  Y. Asai,  J. Iglesias,  P. Dutoit,  A. Lintas,  A.E.P. Villa,  

pp.300-303

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.1.300

PDF download (326.2KB)

Summary:
The concept of interdependent communications systems and Wiener's assertion that a machine that changes its responses based on feedback is a machine that learns, defines the brain as a cybernetic machine. Systems theory has traditionally focused on the structure of systems and their models, whereas cybernetics has focused on how systems function, how they control their actions, how they communicate with other systems or with their own components. However, structure and function of a system cannot be understood in separation and cybernetics and systems theory should be viewed as two facets of a single approach, defined as the neuroheuristic approach.

References:

[1] N. Wiener, Cybernetics Or Control And Communication In The Animal And The Machine. John Wiley & Sons Inc., 1948.

[2] W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysic, vol. 5, pp. 115-133, 1943.

[3] S. C. Kleene, “Representation of events in nerve nets and finite automata,” in Automata Studies, vol. 34 of Annals of Mathematics Studies, pp. 3-42, Princeton, N. J.: Princeton University Press, 1956.

[4] J. von Neumann, The computer and the brain. New Haven, CT, USA: Yale University Press, 1958.

[5] M. L. Minsky, Computation: finite and infinite machines. Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1967.

[6] A. M. Turing, “On computable numbers, with an application to the Entscheidungsproblem,” Proc. London Math. Soc., vol. 2, no. 42, pp. 230-265, 1936.

[7] D. Goldin, S. A. Smolka, and P. Wegner, Interactive Computation: The New Paradigm. Secaucus, NJ, USA: Springer-Verlag, 2006.

[8] P. Wegner, “Interactive foundations of computing,” Theor. Comput. Sci., vol. 192, pp. 315-351, February 1998.

[9] J. van Leeuwen and J. Wiedermann, “Beyond the Turing limit: Evolving interactive systems,” in SOFSEM 2001: Theory and Practice of Informatics (L. Pacholski and P. Ruzicka, eds.), vol. 2234 of LNCS, pp. 90-109, Springer-Verlag, 2001.

[10] H. T. Siegelmann and E. D. Sontag, “On the computational power of neural nets,” J. Comput. Syst. Sci., vol. 50, no. 1, pp. 132-150, 1995.

[11] J. Cabessa and H. T. Siegelmann, “The computational power of interactive recurrent neural networks,” Neural Comput., vol. 24, no. 4, pp. 996-1019, 2012.

[12] J. Kilian and H. T. Siegelmann, “The dynamic universality of sigmoidal neural networks,” Inf. Comput., vol. 128, no. 1, pp. 48-56, 1996.

[13] H. T. Siegelmann, “Computation beyond the Turing limit,” Science, vol. 268, no. 5210, pp. 545-548, 1995.

[14] J. Cabessa, “Interactive evolving recurrent neural networks are super-Turing,” in ICAART 2012: Proceedings of the 4th International Conference on Agents and Artificial Intelligence 2012 (J. Filipe and A. Fred, eds.), vol. 1, pp. 328-333, SciTePress, 2012.

[15] J. Cabessa and A. E. Villa, “The expressive power of analog recurrent neural networks on infinite input streams,” Theor. Comput. Sci., vol. 436, pp. 23-34, 2012.

[16] J. Iglesias and A. E. P. Villa, “Emergence of preferred firing sequences in large spiking neural networks during simulated neuronal development,” Int J Neural Syst, vol. 18, pp. 267-277, Aug 2008.

[17] D. O. Hebb, The organization of behavior : a neuropsycho-logical theory. John Wiley & Sons Inc., 1949.

[18] A. E. P. Villa, I. V. Tetko, B. Hyland, and A. Najem, “Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task,” Proc Natl Acad Sci U S A, vol. 96, pp. 1106-1111, Feb 1999.

[19] T. Shmiel, R. Drori, O. Shmiel, Y. Ben-Shaul, Z. Nadasdy, M. Shemesh, M. Teicher, and M. Abeles, “Neurons of the cerebral cortex exhibit precise interspike timing in correspondence to behavior,” Proc Natl Acad Sci U S A, vol. 102, pp. 18655-18657, Dec 2005.

[20] Y. Asai and A. E. P. Villa, “Integration and transmission of distributed deterministic neural activity in feed-forward networks,” Brain Res, vol. 1434, pp. 17-33, Jan 2012.

[21] I. Tsuda, “Chaotic itinerancy as a dynamical basis of hermeneutics of brain and mind,” World Futures, vol. 32, pp. 167-185, 1991.

[22] S. Smale, “Differentiable dynamical systems,” Bull. Amer. Math. Soc., vol. 73, pp. 747-817, 1967.

[23] J. G. Taylor and A. E. P. Villa, “The ”Conscious I”: A Neuroheuristic Approach to the Mind,” in Frontiers of Life (D. Baltimore, R. Dulbecco, F. Jacob, and R. Levi Montalcini, eds.), vol. III, pp. 349-270, Academic Press, 2001. ISBN: 0-12-077340-6.

[24] R. Thom, Structural stability and morphogenesis. W.A. Benjamin, Reading, MA, 1975.

[25] A. E. P. Villa, “Neural Coding in the Neuroheuristic Perspective,” in The Codes of Life: The Rules of Macroevolution. (M. Barbieri, ed.), vol. 1 of Biosemiotics, ch. 16, pp. 357-377, Berlin, Germany: Springer, 2008.

[26] A. E. Villa, V. M. Bajo Lorenzana, and G. Vantini, “Nerve growth factor modulates information processing in the auditory thalamus,” Brain Res Bull, vol. 39, no. 3, pp. 139-147, 1996.

[27] J. P. Segundo, “Mind and matter, matter and mind?,” J Theor Neurobiol, vol. 4, pp. 47-58, 1985.