Nothing Special   »   [go: up one dir, main page]

Skip to main content

Functional Architectures and Hierarchies of Time Scales

  • Conference paper
Artificial Neural Networks – ICANN 2010 (ICANN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6353))

Included in the following conference series:

  • 1820 Accesses

Abstract

Dynamical system theory offers approaches towards cognitive modeling and computation inspired by self-organization and pattern formation in open systems operating far from thermodynamical equilibrium. In this spirit we propose a functional architecture for the emergence of complex functions such as sequential motor behaviors. We model elementary functions as Structured Flows on Manifolds (SFM) that provide an unambiguous deterministic description of the functional dynamics, while still remaining compatible with the intrinsically low dimensionality of elementary behaviors. Pattern competition processes (operating on a hierarchy of time scales) provide the means to compose complex functions out of simpler constituent ones. Our underlying hypothesis is that complex functions can be decomposed in functional modes (simpler building blocks). Simulations of generating cursive handwriting provide proof of concept and suggest exciting avenues towards extending the current framework to other human functions including learning and language.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Kelso, S.: Dynamic Patterns: The Self-Organization of Brain and Behavior. A Bradford Book, The MIT Press, Cambridge (1995)

    Google Scholar 

  2. Kozma, R., Freeman, W.: The KIV model of intentional dynamics. Neural Networks 22(3), 277–285 (2009)

    Article  MathSciNet  Google Scholar 

  3. Friston, K., Kilner, J., Harrison, L.: A free energy principle for the brain, pp. 70–87 (2006)

    Google Scholar 

  4. Harris, C., Wolpert, D.: Signal-dependent noise determines motor planning. Nature 394, 780–784 (1998)

    Article  Google Scholar 

  5. Newel, A.: Unified theories of cognition. Harvard University Press, Cambridge (1990)

    Google Scholar 

  6. Kremer, S.: Spatiotemporal connectionist networks: A taxonomy and review. Neural Computation 13, 249–306 (2001)

    Article  MATH  Google Scholar 

  7. Sun, R., Alexandre, F.: Connectionist-symbolic integration: From unified to hybrid approaches. Lawrence Erlbaum Associates, Mahwah (1997)

    Google Scholar 

  8. Rabinovich, M., Muezzinoglu, M.: Mutual emotion-cognition dynamics (September 2009), arXiv:0909.1144

    Google Scholar 

  9. Rabinovich, M., Huerta, R., Verona, P., Afraimovich, V.: Transient cognitive dynamics, metastability, and decision making. PLoS Comp. Biol. 4(5) (2008)

    Google Scholar 

  10. Kolen, J., Kremer, S.: A field guide to dynamical recurrent networks. Wiley-IEEE Press, New York (2001)

    Google Scholar 

  11. Carpenter, G., Grossberg, S.: Adaptive resonance theory. In: Arbib, M. (ed.) The handbook of brain theory and neural networks, 2nd edn., pp. 87–90. The MIT Press, Cambridge (2003)

    Google Scholar 

  12. Maass, W., Natschlager, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14, 2531–2560 (2002)

    Article  MATH  Google Scholar 

  13. Schmidt, R., Lee, T.: Motor control and learning: a behavioral emphasis, 4th edn. Human Kinetics, Leeds (2005)

    Google Scholar 

  14. Mussa-Ivaldi, F., Bizzi, E.: Motor learning through the combination of primitives. Phil. Trans. R. Soc. Lond. B 355, 1755–1769 (2000)

    Article  Google Scholar 

  15. Morasso, P., Mussa-Ivaldi, F.: Trajectory formation and handwriting: A computational model. Biological Cybernetics 45, 131–142 (1982)

    Article  Google Scholar 

  16. Tuller, B., Kelso, S.: The production and perception of syllable structure. Journal of Speech and Hearing Research 34, 501–508 (1991)

    Google Scholar 

  17. Poeppel, D., Idsardi, W., van Wassenhove, V.: Speech perception at the interface of neurobiology and linguistics. Phil. Trans. R. Soc. B 363(1493), 1071–1086 (2007)

    Article  Google Scholar 

  18. Lakoff, G.: Women, fire, and dangerous things: What categories reveal about the mind. University of Chicago Press, Chicago (1987)

    Google Scholar 

  19. Feldman, J.: From molecule to metaphor: A neural theory of language. A Bradford Book, The MIT Press, Cambridge (2006)

    Google Scholar 

  20. Jirsa, V., Mersmann, J.: Neuronal network structure and method to operate a neuronal network structure. International Patent Application WO 2009/037526 A1 (2009)

    Google Scholar 

  21. Pillai, A.: Structured Flows on Manifolds: Distributed functional architectures. PhD thesis, Florida Atlantic University (2008)

    Google Scholar 

  22. Haken, H.: Synergetics: introduction and advanced topics. Springer, Heidelberg (2004)

    Google Scholar 

  23. Kelso, S., Jirsa, V.: Coordination dynamics: issues and trends. Springer, Heidelberg (2004)

    Google Scholar 

  24. Guckenheimer, J., Holmes, P.: Nonlinear oscillations, dynamical systems, and bifurcations of vector fields. Springer, New York (2002)

    Google Scholar 

  25. Constantin, P., Foias, C., Nicolaenko, B., Teman, R.: Intergral manifolds and inertial manifolds for dissipative partial differential equations, 1st edn. Springer, New York (1989)

    Google Scholar 

  26. Jirsa, V., Kelso, S.: The excitator as a minimal model for the coordination dynamics of discrete and rhythmic movement generation. J. Mot. Behav. 37(1), 35–51 (2005)

    Article  MathSciNet  Google Scholar 

  27. Huys, R., Fernandez, L., Bootsma, R., Jirsa, V.: Fitts’ law is not continuous in reciprocal aiming, pp. 1179–1184 (2009)

    Google Scholar 

  28. Fink, P., Kelso, S., Jirsa, V.: Perturbation-induced false starts as a test of the Jirsa-Kelso excitator model, pp. 147–157 (2009)

    Google Scholar 

  29. Huys, R., Jirsa, V., Studenka, B., Rheaume, N., Zelaznik, H.: Human trajectory formation: Taxonomy of movement based on phase flow topology. In: Fuchs, A., Jirsa, V. (eds.) Coordination: neural, behavioral and social dynamics. Springer, Heidelberg (2007)

    Google Scholar 

  30. Perdikis, D., Jirsa, V.: How to control complex movements. Poster in Progress in Motor Control, Marseille (2009)

    Google Scholar 

  31. Friston, K.: Hierarchical models in the brain. PLoS Comput. Biol. 4(11) (2008)

    Google Scholar 

  32. Kiebel, S., von Kriegstein, K., Daunizeau, J., Friston, K.: Recognizing sequences of sequences. PLoS Comput. Biol. 5(8) (2009)

    Google Scholar 

  33. Haken, H.: Synergetic computers and cognition: a top-down approach to neural nets, 2nd edn. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  34. Grossberg, S.: Biological competition: Decision rules, pattern formation and oscillations. Proc. Natl. Acad. Sci. USA 77(4), 2338–2342 (1980)

    Article  MATH  Google Scholar 

  35. Bullock, D., Rhodes, B.: Competitive queuing for planning and serial performance. In: Arbib, M. (ed.) Handbook of brain theory and neural networks, 2nd edn., pp. 241–244. The MIT Press, Cambridge (2003)

    Google Scholar 

  36. Maturana, H., Varela, F.: The tree of knowledge: The biological roots of human understanding Revised edn. Shambhala, Boston (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Perdikis, D., Woodman, M., Jirsa, V. (2010). Functional Architectures and Hierarchies of Time Scales. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15822-3_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15821-6

  • Online ISBN: 978-3-642-15822-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics