'Neuro References' - eine Literaturübersicht zur KI-Forschung

Kopie aus: www.gfai.de/www_open/perspg/g_heinz/veroef/neurolit.htm

By the way: What means 'Neural Network'?

  • for the evolution in science: statistical methods to learn pattern.
  • for me: a field closed coupled to biolology. A field with tormenting questions away from statistics, error backpropagation or overfitting.
The difference between neural and neural is to find in a slow flow of pulses. While interference nets generate mirrored projections, node-basing neural nets can not solve this task, anyway. More: They can not generate or detect time coded pulses used as bursts, or it is not able to project an excitement map over a few axons.
So we have to differ between
  • neural nets* (McCulloch/Pitts 1943, a wire has one potential and is following called a node; neglection of time flow through wires)
  • interference nets** (Heinz 1992, nets with wiring delay proportional to distances, pulses flow observable slow)
  • on intermediate position stand neural nets for time code detection, working like digital filters step by step.
  • last species are nerve models to understand single nerve behaviour, ratios, properties and parameters (Hodgkin/Huxley 1952), see biological or nerve nets.

* Statistical methods and theories of neural networks in all directions see more in ftp://ftp.sas.com/pub/neural/FAQ.html
** The terminus interference network comes in sight when I found that neural networks can not create mirrored projections - so they are far away from informational processing of nerves. It was not possible, to call the new network class 'neural'. So I decided to call the class 'interference networks'. Now interference networks appear to be more neural as neural networks - my sympathy offer to all students! Apropos: The terminus 'neuron' was created by H. W. G. von Waldeyer, 1891, see Florey/Breidbach, p.73 and 97. Before, Virchow said 'Ganglions'. First measurments of nerval transmission velocities had done Hermann von Helmholtz 1851 at frog's legs. Termini:
  • neural means learning
  • interference means neural

Neuro References - A Colored List
with Standard Books, Interesting and Some Forgotten Ideas

  1. Aarts, E., Korst, J.: Simulated Annealing and Boltzmann Machines. A Stochastic Approach to Combinatorial Optimization and Neural Computing. J. Wiley & Sons, New York, 1989
  2. Advances in Neural Information Processing Systems: Bd. 1: Edited by D.S. TouretzkyBd. 2: Edited by D.S. TouretzkyBd. 3: Edited by R.P. Lippmann, J.E. Moody, D.S. TouretzkyBd. 4: Edited by J.E. Moody, S.J. Hanson, R.P. Lippmann. Morgan Kaufmann Publishers, 2929 Campus Drive Suite 260, San Mateo, CA 94403.
  3. Aleksander, I.: Neural Computing Architectures: The Design of Brainlike Machines. MIT- Press Cambridge MA, 1989
  4. Anderson J.A., Rosenfeld, E.: Neurocomputing: Foundations of Research. MIT- Press Cambridge MA, 1988
  5. Anderson, J.A., Pellionisz, A., Rosenfeld, E.: Neurocomputing 2: Directions of Research. MIT- Press Cambridge MA, 1990
  6. Ardenne, M.v., Reitnauer, P.G.: Handbuch medizinischer Elektronik. Teil I.: Tabellen, Verlag Technik Berlin, 1962
  7. Baur, Erwin: Einführung in die Radartechnik. B. G. Teubner Stuttgart, 1985
  8. Beer, R.D.: Intelligence as Adaptive Behaviour. Academic Press, Boston, 1990
  9. Birbaumer, N., Schmidt, R.F.: Biologische Psychologie. Springer- Verlag Berlin, 1989
  10. Delgado- Frias, J.G., Moore, W.R.: VLSI for Artifical Intelligence. Kluwer Acad. Publ., Boston, USA, 1989
  11. Dobesch, H.: Laplace-Transformation von Abtastfunktionen. Verlag Technik Berlin, 1970
  12. Eckmiller, R.; v.d.Malsburg, C. (Editors): Neural Computers. Springer, 1988.
  13. Esche, P.G.: Ernst Abbe. B.G. Teubner Verlagsgesellschaft, Leipzig 1963
  14. Florey, E., Breidbach, O. (Herausgeber): Das Gehirn - Organ der Seele? Zur Ideengeschichte der Neurobiologie. Akademie Verlag Berlin, 1993
  15. Freeman, J. A.: Simulating Neural Networks with Mathematica. Addison-Wesley, Reading MA, 1994
  16. Grossberg, Stephen: Neural Networks and Natural Intelligence. MIT- Press, Cambridge MA, 1988.
  17. Grauel, A.: Neuronale Netze. BI Wissenschaftsverlag, Mannheim, 1992
  18. Harmuth, H.F.: Transmission of Information by Orthogonal Functions. Springer-Verlag, 2. Aufl. 1972
  19. Hebb, D.O.: The Organization of Behaviour. J. Willey & Sons, New York 1949 (Hebb's Rule)
  20. Heiligenberg, W.E.: Neural Nets in Electric Fish. MIT/Bradford, 1991
  21. Henderson, C.J., Butler, S.R., Glass, A.: The Localization of Equivalent Dipoles of EEG Sources by the Application of Electrical Field Theory. Electroencephalography and Clinical Neurophysiology, Elsevier Sc. Publ. Comp. Amsterdam, 1975,39 pp. 117-130
  22. Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiology (1952) 117, 500-544
  23. Hopfield, J.J.: Neural Networks and Physical Systems with Emergent Collective Computational Abilities. PNAS USA, Vol. 79, 1982, pp. 2554-2558 (Spinglas- Modell, Rückkopplung)
  24. Hurst, S.L.: Schwellwertlogik. Dr. Alfred Hüthig Verlag, UTB 262, 1974
  25. James, W.: Psychology. 1890. Quelle: Schöneburg/Hansen/Gawelczyk: Neuronale Netzwerke, Markt & Technik V., 1990, S. 68.
  26. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science, vol.220, 1983, pp. 671-680 (Simulated Annealing)
  27. Klar,H.: JESSI- Project: Advanced Neural Systems and Networks on Silicon. TU Berlin, Institut für Mikroelektronik, 1990
  28. Klix, F.: Information und Verhalten. Deutscher Verlag Wissenschaft Berlin, 1973
  29. Klimasauskas, C.C.: The 1989 Neuro-Computing Bibliography. MIT Press, Cambridge MA, London England. Second Edition 1989.
  30. Koch,C.: Nichtlineare Informationsverarbeitung in dendritischen Bäumen beliebiger Geometrie. Dissertation. Eberhard-Karls-Universität Tübingen, Fak. für Physik, 1982.
  31. Kohonen, T.: Self-organized Formation of Topologically Correct Feature Maps. Biol. Cybern., Vol. 43 (1982), pp.59-69 (Selbstorganisierende Karten)
  32. Kohonen, T.: Selforganization and Associative Memory. Springer, 1984
  33. Kolb, B., Whilshaw, I.Q.: Fundamentals of Human Neuropsychology. 2nd Ed., Freeman, New York, 1985.
  34. Kosko, B.: Neural Networks and Fuzzy Systems. Prentice Hall Int. Inc., 1992
  35. Krinke, h. E., Kulik, L.A.: Probleme der Meßwertgewinnung in der biomedizinischen Technik. Chirurgische Klinik der Med. Akademie 'Carl Gustav Carus' Dresden, 1983
  36. Kühn, Eberhard: Handbuch TTL- und CMOS- Schaltkreise. 3.Aufl., Verlag Technik Berlin, 1988
  37. Kung, S.Y.: VLSI Array Processors. Prentice Hall, 1988
  38. Lashley, K.S.: In Search of the Engram. Symp. Soc. Exp. Biol. 4, 1950, pp. 454-482
  39. Lippert, H.: Anatomie- Text und Atlas. 5. Aufl., Urban & Schwarzenberg, München, 1989
  40. Liß, E.: Lernfähiger Zuordnungskomplex - assoziatives Gedächtnissystem intelligenter Automaten. (Übersicht aller Arbeiten des Autors) Nachrichtentechnik-Elektronik, Berlin, H.7, 1984, S. 269-274
  41. Maruhashi, J., Mizuguchi, K., Tasaki, I.: Action currents in single afferent nerve fibres elicted by stimulation of the skin of the toad and the cat. J. Physiol. 17 (1952) 117, 129-151
  42. McClelland, J.L., Rumelhart, D.E.: Explorations in Parallel Distributed Processing. A Handbook of Models, Programs, and Exercises. MIT- Press Cambridge MA, 1988.
  43. McClelland, J.L., Rumelhart, D.E., u.a.: Parallel Distributed Processing. Vol. 2: Psychological and Biological Models. MIT Press, Cambridge, MA, eighth printing, 1986-1988 (Vol. 1 siehe Rumelhart)
  44. McCulloch, W. S., Pitts, W.: A Logical Calculus of the Ideas Immanent in Nervous Activity. Bull. Math. Biophysics Vol.5 (1943), pp.115-133 (McCulloch/Pitts- Neuronen, Schwellwertlogik)
  45. Mead, C., Ismail, M.: Analog VLSI Implementation of Neural Systems. Kluwer Acad. Publ., 1989
  46. Mead, C., Conway, L.: Introduction to VLSI Systems. Addison-Wesley-Publ. Comp., Reading MA, USA, 1980
  47. Mead, C.: Analog VLSI and Neural Systems. Addison Wesley, 1990
  48. Minsky, M., Papert, S.A.: Perceptrons. MIT- Press, Cambridge MA, 1969 (lineare Separabilität einlagiger Netze)
  49. Minsky, Marvin: Mentopolis - The Society of Mind. Klett Verlag Stuttgart 1990/ Simon & Schuster Inc. New York 1985/86
  50. Popper, K.R., Eccles, J.C.: The Self and its Brain. Springer Intern., New York 1977
  51. Pschyrembel, W.: Klinisches Wörterbuch. Verlag Walter de Gruyter, Berlin, 257.Auflage, 1994
  52. Ramacher, U., Rückert, U. (eds.): VLSI Design of Neural Networks. Kluwer Acad. Publ., 1991
  53. Rauber/Kopsch: Anatomie des Menschen. Herausgegeben von Leonhardt, H., Töndury, G., Zilles, K., Band III: Nervensystem, Sinnesorgane. Georg Thieme Verlag Stuttgart, 1987
  54. Ritter, H., Martinez, T., Schulten, K.: Neuronale Netze. Eine Einführung in die Neuroinformatik selbst- organisierender Netze. Addison Wesley, 1991
  55. Rojas, R.: Theorie der neuronalen Netze. Springer-Verlag, 1993
  56. Rosenblatt, F.: The Perceptron: a Probabilistic Model for Information Storage and Organization in the Brain. Psych. Review 65, 1958, pp. 386-408 (Perceptron Convergence Theorem)
  57. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back- Propagating Errors. Nature 323, 1986, pp. 533-536 (Backpropagation- Fundierung und Popularisierung)
  58. Rumelhart, D.E., McClelland, J.L., u.a.: Parallel Distributed Processing. Vol. 1: Foundations. MIT Press, Cambridge, MA, tenth printing, 1986-1992 (Vol. 2 siehe McClelland)
  59. Schefe, P.: Künstliche Intelligenz - Überblick und Grundlagen. BI Wissenschaftsverlag, Mannheim, 1991
  60. Schmidt, R.F., Thews, G.: Physiologie des Menschen. Springer-Verlag, 24. Auflage 1990
  61. Schöneburg, E., Hansen, N., Gawelczyk, A.: Neuronale Netzwerke. Markt & Technik Verlag, Haar bei München, 1990
  62. Schöning, U.: Theoretische Informatik kurz gefaßt. BI Wissenschaftsverlag, Mannheim, 1992
  63. Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. Urbana: The University of Illinois Press. 1949
  64. Sinz, R.: Gehirn und Gedächtnis. Verl. Volk und Gesundheit, Berlin, 1978
  65. Squire, L., Weinberger, N., Lynch, G., McGaugh, J.: Memory: Localization and Locus of Change. Oxford University Press, 1991. Darin enthalten: S.114-159: Scheich, H., Wallhäuser-Franke, E., Braun, K.: Does Synaptic Selection Explain Auditory Imprinting?
  66. Stern, A.: The Quantum Brain. Theory and Implications. North-Holland / Elsevier Science B.V., Amsterdam1994
  67. Sydow, H., Petzold, P.: Mathematische Psychologie. Berlin, Deutscher Verlag Wissenschaften, 1981
  68. Tietze, U., Schenk, Ch.: Halbleiter-Schaltungstechnik. 9.Aufl., Springer Berlin, 1991
  69. Weigend, A.S., Gershenfeld, N.A.: Time Series Prediction. Addison Wesley, Reading, MA, 1994
  70. Werbos, P.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Ph.D. thesis, Harvard University, Comittee on Applied Mathematics, 1974 (Backpropagation-Algorithmus)
  71. Widrow, B., Hoff, M.E.: Adaptive Switching Circuits. WESCON Conv. Record, part IV, 1960, pp. 96-104 (Adaptive Linear Element: Adaline, Multiple Adaline: Madaline, Delta- Lernregel)
  72. Wiener, N.: Kybernetik. Econ- Verlag Düsseldorf, 1963 (deutsche Übersetzung; Originalausgabe englisch, 1948)
  73. Wolfram, S.: Mathematica. Addison-Wesley, Reading MA, 2. Aufl. 1993
  74. Woschni, E.-G.: Informationstechnik. VEB Verlag Technik Berlin, 4. Auflage, 1990
  75. Zadeh, L.A.: Outline of a new approach to the analysis of complex system and decision processes. IEEE Transact. Syst. Man. Cybern., 3/1973, p.28-44

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