Neural Communication: Messages Between Modules

Chapter
First Online:
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 1)

Abstract

This chapter discusses and exemplifies the role of invariance in modular systems. We ask: What is a module? What is the kind of message exchanged between modules? What is the meaning of noise? What does it mean, in neural terms, to receive a message? The answers to these questions are tightly coupled, and rely on the simple observation that a message is interpreted by a receiver, and is only made meaningful therewith. Activity exchange between communicating brain areas admits a characterization in terms of meaning, which in turn admits a neat characterization in neurodynamics terms.

Keywords

Recurrent Neural Network Dynamical Modularity Modular Function Vertical Module Behavioral Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Abeles M (1994) Firing rates and well-timed events in the cerebral cortex. Models of neural networks II: Temporal aspects of coding and information processing in biological systems. Springer, BerlinGoogle Scholar
  2. 2.
    Arbib MA (2007) Modular models of brain function. Scholarpedia 2(3):1869. URL ttp://www.scholarpedia.org/article/Modular_models_of_brain_function Google Scholar
  3. 3.
    Beer RD (1995) A dynamical systems perspective on agent-environment interaction. Artif Intell (72):173–215CrossRefGoogle Scholar
  4. 4.
    Beer RD (2000) Dynamical approaches to cognitive science. Trends Cogn Sci 4(3):91–99PubMedCrossRefGoogle Scholar
  5. 5.
    Benavides-Piccione R, Hamzei-Sichani F, Ballesteros-Yanez I, DeFelipe J, Yuste R (2006) Dendritic size of pyramidal neurons differs among mouse cortical regions. Cereb Cortex 16(7):990–1001PubMedCrossRefGoogle Scholar
  6. 6.
    Braitenberg V, Braitenberg C (1979) Geometry of orientation columns in the visual cortex. Biol Cybern 33(3):179–186PubMedCrossRefGoogle Scholar
  7. 7.
    Braitenberg V, Schüz A (1998) Cortex: Statistics and geometry of neuronal connectivity. Springer, BerlinGoogle Scholar
  8. 8.
    Clark A (1999) An embodied cognitive science? Trends Cogn Sci 3(9):345–351PubMedCrossRefGoogle Scholar
  9. 9.
    Clark A, Chalmers D (1998) The extended mind. Analysis 58(1):7–19CrossRefGoogle Scholar
  10. 10.
    Dauce E, Quoy M, Cessac B, Doyon B, Samuelides M (1998) Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning. Neur Netw 11(3):521–533CrossRefGoogle Scholar
  11. 11.
    Edelman GM (1987) Neural darwinism. Basic Books, New YorkGoogle Scholar
  12. 12.
    Fodor J (1983) The modularity of mind. MIT, Cambridge, MAGoogle Scholar
  13. 13.
    Freeman W, Barrie J (2001) Chaotic oscillations and the genesis of meaning in cerebral cortex. Nonlinear Dynamics in the Life and Social SciencesGoogle Scholar
  14. 14.
    Freeman WJ (1995) Society of Brains: A study in the neuroscience of love and hate. Laurence Erlbaum Associates IncGoogle Scholar
  15. 15.
    Freeman WJ (2004) How and why brains create meaning from sensory information. International Journal of Bifurcation Theory and Chaos 14(2):515–530CrossRefGoogle Scholar
  16. 16.
    Gärdenfors P (1995) Cued and detached representations in animal cognition. Lund University Cognitive Studies 38Google Scholar
  17. 17.
    Grush R (2004) The emulation theory of representation: Motor control, imagery and perception. Behavioral and Brain Sciences 27:377–442PubMedGoogle Scholar
  18. 18.
    Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey C, Wedeen V, Sporns O (2008) Mapping the structural core of human cerebral cortex. PLoS Biology 6(7):e159PubMedCrossRefGoogle Scholar
  19. 19.
    Harvey I, Paolo ED, Wood R, Quinn M, Tuci E (2005) Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life 11(1-2):79–98, URL http://www.mitpressjournals.org/doi/abs/10.1162/1064546053278991 Google Scholar
  20. 20.
    Haugeland J (1995) Mind embodied and embedded. Acta Philosophica FennicaGoogle Scholar
  21. 21.
    Herrnstein RJ (1970) On the law of effect. Journal of the experimental analysis of behavior 13(2):243–266PubMedCrossRefGoogle Scholar
  22. 22.
    Hülse M (2006) Multifunktionalität rekurrenter neuronaler netze – synthese und analyse nichtlinearer kontrolle autonomer roboter. PhD thesis, Universität OsnabrückGoogle Scholar
  23. 23.
    Ikegami T, Tani J (2002) Chaotic itinerancy needs embodied cognition to explain memory dynamics. Behavioral and Brain Sciences 24(05):818–819Google Scholar
  24. 24.
    Ito M (2008) Control of mental activities by internal models in the cerebellum. Nature Reviews NeuroscienceGoogle Scholar
  25. 25.
    Izhikevich EM (2006) Polychronization: Computation with spikes. Neural Computation 18:245–282PubMedCrossRefGoogle Scholar
  26. 26.
    Jaegger H, Maas W, Markram H (2007) special issue: Echo state networks and liquid state machines. Neural Networks 20(3):290–297CrossRefGoogle Scholar
  27. 27.
    Kaneko K, Tsuda I (2003) Chaotic itinerancy. Chaos: An Interdisciplinary Journal of Nonlinear Science 13(3):926–936CrossRefGoogle Scholar
  28. 28.
    Krichmar JL, Edelman GM (2003) Brain-based devices: Intelligent systems based in principles of the nervous system. In: Proceedings of International Conference on Intelligent Robots and SystemsGoogle Scholar
  29. 29.
    Leicht EA, Newman ME (2008) Community structure in directed networks. Physical Review Letters 100:118703PubMedCrossRefGoogle Scholar
  30. 30.
    Menzel R, Giurfa M (2001) Cognitive architecture of a mini-brain: the honeybee. Trends in Cognitive Science 5(2):62–71CrossRefGoogle Scholar
  31. 31.
    Merleau-Ponty M (1963 (translation), 1942) The Structure of Behavior. Duquesne University PressGoogle Scholar
  32. 32.
    Mountcastle V (1997) The columnar organization of the neocortex. Brain 120(701-722)PubMedCrossRefGoogle Scholar
  33. 33.
    Pfeiffer K, Homberg U (2007) Coding of Azimuthal Directions via Time-Compensated Combination of Celestial Compass Cues. Current Biology 17(11):960–965PubMedCrossRefGoogle Scholar
  34. 34.
    Simon H (1969) The Sciences of the Artificial. MIT Press (Cambridge)Google Scholar
  35. 35.
    Sporns O (2002) Graph theory methods for the analysis of neural connectivity patterns. Neuroscience Databases A Practical Guide pp 169–83Google Scholar
  36. 36.
    Sporns O, Kotter R (2004) Motifs in brain networks. PLOS Biology 2:1910–1918CrossRefGoogle Scholar
  37. 37.
    Sporns O, Honey C, Kötter R (2007) Identification and classification of hubs in brain networks. PLoS ONE 2(10)CrossRefGoogle Scholar
  38. 38.
    Strausfeld N (2009) Brain organization and the origin of insects: an assessment. Proceedings of the Royal Society B: Biological SciencesGoogle Scholar
  39. 39.
    Tani J (1998) An interpretation of the ‘self’ from the dynamical systems perspective: A constructivist approach. Journal of Consciousness Studies 5(5-6):516–542Google Scholar
  40. 40.
    Thompson E, Varela F (2001) Radical embodiment: neural dynamics and consciousness. Trends in cognitive sciences 5(10):418–425PubMedCrossRefGoogle Scholar
  41. 41.
    Ton R, Hackett J (1984) Neural Mechanisms of Startle Behavior, Springer, chap The Role of the Mauthner Cell in Fast Starts Involving Escape in Teleost FishesGoogle Scholar
  42. 42.
    Tononi G, Sporns O, Edelman G (1994) A Measure for Brain Complexity: Relating Functional Segregation and Integration in the Nervous System. Proceedings of the National Academy of Sciences 91(11):5033–5037CrossRefGoogle Scholar
  43. 43.
    Varela F, Rorty E, Thompson E (1991) The Embodied Mind. MIT PressGoogle Scholar
  44. 44.
    Varela F, Lachaux J, Rodriguez E, Martinerie J, et al (2001) The brainweb: phase synchronization and large-scale integration. Nature Reviews Neuroscience 2(4):229–239PubMedCrossRefGoogle Scholar
  45. 45.
    Watson RA, Pollack JB (2005) Modular interdependency in complex dynamical systems. Artificial Life 11(4)CrossRefGoogle Scholar
  46. 46.
    Wolpert DM, Doya K, Kawato M (2003) A unifying computational framework for motor control and social interaction. The Royal Society JournalGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Okinawa Institute of Science and TechnologyOkinawaJapan

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