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Concerning the Neuronal Code

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The central problem with understanding brain and mind is the neural code issue: understanding the matter of our brain as basis for the phenomena of our mind. The richness with which our mind represents our environment, the parsimony of genetic data, the tremendous efficiency with which the brain learns from scant sensory input and the creativity with which our mind constructs mental worlds all speak in favor of mind as an emergent phenomenon. This raises the further issue of how the neural code supports these processes of organization. The central point of this communication is that the neural code has the form of structured net fragments that are formed by network self-organization, activate and de-activate on the functional time scale, and spontaneously combine to form larger nets with the same basic structure.

Abstract

The central problem with understanding brain and mind is the neural code issue: understanding the matter of our brain as basis for the phenomena of our mind. The richness with which our mind represents our environment, the parsimony of genetic data, the tremendous efficiency with which the brain learns from scant sensory input and the creativity with which our mind constructs mental worlds all speak in favor of mind as an emergent phenomenon. This raises the further issue of how the neural code supports these processes of organization. The central point of this communication is that the neural code has the form of structured net fragments that are formed by network self-organization, activate and de-activate on the functional time scale, and spontaneously combine to form larger nets with the same basic structure.

참고문헌 (51)

  1. M. Abeles, "Corticonics: Neronal Circuits of the Cerebral Cortex" (1991), Cambridge University Press
  2. S. Amari, "Dynamics of pattern formation in lateral-inhibitory type neural fields", Biol. Cybern, 27, pp.77~87 (1977)
  3. C. H. Anderson, "Shifter circuits: a computational strategy for dynamic aspects of visual processing", PNAS, 84(17), pp.6297~6301 (1987)
  4. D. W. Arathorn, "Map-Seeking circuits in Visual Cognition-A Computational Mechanism for Biological and Machine Vision" (2002), Standford Univ. Press
  5. M. Arbib, "The Handbook of Brain Theory and Neural Networks", pp.1427~1443 (1992), MIT Press
  6. F. C. Bartlett, "Remembering, A Study in Experimental and Social Psychology" (1932), Cambridge University Press
  7. U. Bergmann, "Self-organization of topographic bilinear networks for invariant recognition", Neural Computation, 23, pp.2770~2797 (2011)
  8. E. Bienenstock, "A neural network for invariant pattern recognition", Europhys. Lett, 4, pp.121~126 (1987)
  9. R. A. Block, "Cognitive models of psychological time" (2014), Psychology Press
  10. Thomas Burwick, "The binding problem", Wiley Interdisciplinary Reviews: Cognitive Science, 5(3), pp.305~315 (2014), Wiley[DOI]
  11. C. A. Curcio, "Topography of ganglion cells in human retina", J. Comp. Neurol, 300, pp.5~25 (1990)
  12. Jerome Feldman, "The neural binding problem(s)", Cognitive Neurodynamics, 7(1), pp.1~11 (2013), Springer Nature[DOI]
  13. Tomas Fernandes, "Self-Organization of Control Circuits for Invariant Fiber Projections", Neural Computation, 27(5), pp.1005~1032 (2015), MIT Press - Journals[DOI]
  14. M. R. Garey, "Computers and Intractability" (1979), W.H. Freeman and Co
  15. G. J. Goodhill, "Contributions of theoetical modeling to the understanding of neural map development", Neuron, 56, pp.301~311 (2007)
  16. A. F. Häussler, "Development of retinotopic projections: An analytical treatment", J. Theoretical Neurobiology, 2, pp.47~73 (1983)
  17. Makoto Hirahara, "Associative memory with a sparse encoding mechanism for storing correlated patterns", Neural Networks, 10(9), pp.1627~1636 (1997), Elsevier BV[DOI]
  18. E. Husserl, "Allgemeine Einführung in die reine Phänomenologie" (1980), Max Niemeyer Verlag
  19. J. Jitsev, "Off-line memory reprocessing following on-line unsupervised learning strongly improves recognition performance in a hierarchical visual memory", International Joint Conference on Neural Networks (IJCNN), Special Session on Organic Computing, IEEE World Congress on Computational Intelligence (WCCI), pp.3123~3130 (2010)
  20. H. H. Kornhuber, "Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale", Pflügers Arch, 284, pp.1~17 (1965)[DOI]
  21. G. Leuba, "Changes in volume, surface estimate, three-dimensional shape and total number of neurons of the human primary visual cortex from midgestation until old age", Anatomy and Embryology, 190(4) (1994), Springer Nature[DOI]
  22. B. Libet, "Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential)", Brain, 106(3), pp.623~642 (1983)
  23. M. D. Lieberman, "Social – Why Our Brains Are Wired to Connect" (2013), Broadway Books
  24. H. S. Loos, "Biologically Motivated Computer Vision 2002 (BMCV 2002), volume 2525 of Lecture Notes in Computer Science", pp.377~386 (2002), Springer Verlag
  25. A framework for representing knowledge, "M. Minsky", MIT AI Laboratory, ~, pp.Technical Report 306 (1974)
  26. J. Müller, "Handbuch der Physiologie des Menschen für Vorlesungen" (1840), Verlag von J. Hölscher
  27. A. Peters, "Neuronal organization in area 17 of cat visual cortex", Cerebral Cortex, 3, pp.49~68 (1993)
  28. H. Poincaré, "Creativity", pp.77~90 (1973), Penguin Books
  29. A. Polsky, "Computational subunits in thin dendrites of pyramidal cells", Nature Neuroscience, 7, pp.621~627 (2004)
  30. M. C. Potter, "Recognition memory for a rapid sequence of pictures", Journal of experimental psychology, 81(1), pp.10~15 (1969)
  31. D. Rumelhart, "Parallel Distributed Processing Vol. 1" (1987), MIT Press
  32. D. E. Rumelhart, "Parallel Distributed Processes - Explorations in the Microstructure of Cognition, Volume 2 Psychological and Biological Models", pp.7~57 (1987), MIT Press
  33. R. Schank, "Scripts, plans, goals and understanding: An inquiry into human knowledge structures" (1977), Erlbaum
  34. Benjamin Siddoway, "Molecular mechanisms of homeostatic synaptic downscaling", Neuropharmacology, 78, pp.38~44 (2014), Elsevier BV[DOI]
  35. Xiangyu Tang, "X. Tang and Figure-ground separation by cue integration", Neural Computation, 20, pp.1452~1472 (2008)
  36. S. Thorpe, "Speed of processing in the human visual system", Nature, 381, pp.520~522 (1996)
  37. M. Tomasello, "A natural history of human thinking" (2014), Harvard University Press
  38. J. Triesch, "Synergies between intrinsic and synaptic plasticity mechanisms", Neural Computation, 19, pp.885~909 (2007)
  39. C. von der Malsburg, "Self-organization of orientation sensitive cells in the striate cortex", Kybernetik, 14, pp.85~100 (1973)
  40. C. von der Malsburg, "Models of Neural Networks II, chapter 2", pp.95~119 (1994), Springer
  41. C. von der Malsburg, "Cognition, Computation and Consciousness", pp.193~204 (1997), Oxford University Press
  42. C. von der Malsburg, "A neural network for the retrieval of superimposed connection patterns", Europhys. Lett., 3, pp.1243~1249 (1987)
  43. J. G. White, "The structure of the nervous system of the nematode Caenorhabditis elegans", Philos. Trans. R. Soc. Lond., 314, pp.1~340 (1986)
  44. J. Wieghardt, "Pose-independent object representation by 2-d views", IEEE International Workshop on Biologically Motivated Computer Vision (2000)
  45. D. J. Willshaw, "How patterned neural connections can be set up by self-organization", Proceedings of the Royal Society of London, Series B, 194, pp.431~445 (1976)
  46. H. R. Wilson, "Excitatory and inhibitory interactions in localized populations of model neurons", Biophys. J., 12, pp.1~24 (1972)
  47. Procedures as a representation for data in a computer program for understanding natural language, "T. Winograd", MIT AI, ~, pp.Technical Report 235 (1971)
  48. Philipp Wolfrum, "A recurrent dynamic model for correspondence-based face recognition", Journal of Vision, 8(7), pp.34~ (2008), Association for Research in Vision and Ophthalmology (ARVO)[DOI]
  49. P. Wolfrum, "What is the optimal architecture for visual information routing?", Neural Computation, 19(12), pp.3293~3309 (2007)
  50. J. Zhu, "Self-organization of steerable topographic mappings as basis for translation invariance. In ICANN, Part II", LNCS, pp.6353~ (2010)
  51. Junmei Zhu, "Maplets for correspondence-based object recognition", Neural Networks, 17, pp.1311~1326 (2004)

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