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Neural coding: A theoretical vista of mechanisms, techniques, and applications

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Analysis of Dynamical and Cognitive Systems

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 888))

Abstract

This paper presents an overview of the capabilities of spiking neurons in processing complex information. We propose a flexible neuron model (the Spike Response Model), that is amenable to both analytic treatment and straightforward numerical simulation, and analyze the dynamics of a large network consisting of these neurons. We also present tools that, given some homogeneity, enable one to analytically treat the dynamical response of a network, a highly nonlinear system. Finally, we evaluate the underlying mechanisms, such as the dependence upon the axonal delays, the local inhibition, structural feedback, and discuss applications to associative feature linking, pattern segmentation, and context-sensitive binding.

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References

  1. Amit, D.J., Gutfreund, H., Sompolinsky, H.: Statistical mechanics of neural networks near saturation. Ann Phys (NY) 173 (1987) 30–67

    Google Scholar 

  2. Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Kruse, W., Munk, M., Reitboeck, H.J.: Coherent oscillations: A mechanism of feature linking in the visual cortex? Biol. Cybern. 60 (1988) 121–130

    Article  PubMed  Google Scholar 

  3. Eckhorn, R., Brosch, M.: Synchronous oscillatory activities between areas 17 and 18 in the cat's visual cortex. J. Neurophysiol. (1994) in press.

    Google Scholar 

  4. Eckhorn, R., Obermueller, A.: Single neurons are differently involved in stimulus-specific oscillations in cat visual cortex. Exp. Brain Res. 95 (1993) 177–182

    PubMed  Google Scholar 

  5. Eckhorn, R., Reitboeck, H.J., Arndt, M., Dicke, P.: Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex. Neur. Comp. 2 (1990) 293–307

    Google Scholar 

  6. Engel, A.K., König, P., Kreiter, K., Singer, W.: Interhemispheric synchronization of oscillatory neural responses in cat visual cortex. Science 252 (1991) 1177–1179

    PubMed  Google Scholar 

  7. Engel, A.K., König, P., Singer, W.: Direct physiological evidence for scene segmentation by temporal coding. Proc. Natl. Acad. Sci. USA 88 (1991) 9136–9140

    PubMed  Google Scholar 

  8. Felleman, D.J., van Essen, D.C.: Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1 (1991) 1–47

    PubMed  Google Scholar 

  9. Fuentes, U.: Einflu\ der Schicht-und Arealstruktur auf die Informationsverar-beitung im Cortex. Diplomarbeit, Technische Universität München, 1993.

    Google Scholar 

  10. Gerstner, W., van Hemmen, J.L.: Associative memory in a network of 'spiking’ neurons. Network 3 (1992) 139–164

    Article  Google Scholar 

  11. Gerstner, W., van Hemmen, J.L.: Universality in neural networks: The importance of the mean firing rate. Biol. Cybern. 67 (1992) 195–205

    PubMed  Google Scholar 

  12. Gerstner, W., van Hemmen, J.L.: Coding and information processing in neural networks. In E. Domany, J.L. van Hemmen, and K. Schulten, Editors, Models of neural networks II. Springer, New York., 1994. Chap. 1.

    Google Scholar 

  13. Gerstner, W., Ritz, R., van Hemmen, J.L.: A biologically motivated and analytically soluble model of collective oscillations in the cortex: I. Theory of weak locking. Biol. Cybern. 68 (1993) 363–374

    PubMed  Google Scholar 

  14. Gerstner, W., Ritz, R., van Hemmen, J.L.: Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biol. Cybern. 69 (1993) 503–515

    PubMed  Google Scholar 

  15. Goles, E., Olivos, J.: Comportement périodique des fonctions à seuil binaires et applications. Discr. Appl. Math. 3 (1981) 93–105

    Google Scholar 

  16. Goles, E., Vichniac, Y.: Lyapunov functions for parallel neural networks. In J.S. Denker, Editor, Neural networks for computing, pp. 165–181. American Institute of Physics, New York, 1986.

    Google Scholar 

  17. Gray, C.M., König, P., Engel, A.K., Singer, W.: Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338 (1989) 334–337

    Article  PubMed  Google Scholar 

  18. Gray, C.M., Singer, W.: Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl. Acad. Sci. USA 86 (1989) 1698–1702

    PubMed  Google Scholar 

  19. Hebb, D.O.: The organization of behavior. Wiley, New York, 1949

    Google Scholar 

  20. van Hemmen, J.L.: Hebbian learning and unlearning. In Neural networks and spin glasses, pp. 91–114. World Scientific, Singapore, 1990.

    Google Scholar 

  21. van Hemmen, J.L., Gerstner, W., Herz, A.V.M., Kühn, R., Sulzer, B., Vaas, M.: Encoding and decoding of patterns which are correlated in space and time. In G. Dorffner, Editor, Konnektionismus in Artificial Intelligence und Kognitions-forschung, pp. 153–162. Springer, Berlin, Heidelberg, New York, 1990.

    Google Scholar 

  22. van Hemmen, J.L, Gerstner, W., Ritz, R.: A ‘microscopic’ model of collective oscillations in the cortx. In J.G. Taylor et al., Editors, Perspectives in neural computing, pp. 250–257. Springer, Berlin, Heidelberg, New York, 1992.

    Google Scholar 

  23. van Hemmen, J.L., Grensing, D., Huber, A., Kühn, R.: Elementary solution of classical spin glass models. Z. Phys. B 65 (1986) 53–63

    Google Scholar 

  24. van Hemmen, J.L., Grensing, D., Huber, A., Kühn, R.: Nonlinear neural networks I and II. J. Stat. Phys. 50 (1988) 231–257 and 259–293

    Google Scholar 

  25. van Hemmen, J.L., Ioffe, L.B., Kühn, R., Vaas, M.: Increasing the efficiency of a neural network through unlearning. Physica A 163 (1990) 386–392

    Google Scholar 

  26. van Hemmen, J.L., Kühn, R.: Nonlinear neural networks. Phys. Rev. Lett. 57 (1986) 913–916

    PubMed  Google Scholar 

  27. van Hemmen, J.L., Kühn, R.: Collective phenomena in neural networks. In E. Domany, J.L. van Hemmen, and K.Schulten, Editors, Models of neural networks. Springer, Berlin, Heidelberg, New York, 1991.

    Google Scholar 

  28. Herz, A.V.M., Li, Z., van Hemmen J.L.: Statistical mechanics of temporal association in neural networks with transmission delays. Phys. Rev. Lett. 66 (1991) 1370–1373

    PubMed  Google Scholar 

  29. Herz, A.V.M., Sulzer, B., Kühn, R., van Hemmen, J.L.: The Hebb rule: Representation of static and dynamic objects in neural nets. Europhys. Lett. 7 (1988) 663–669

    Google Scholar 

  30. Herz, A.V.M., Sulzer, B., Kühn, R., van Hemmen, J.L.: Hebbian learning reconsidered: Representation of static and dynamic objects in associative neural nets. Biol. Cybern. 60 (1989) 457–467

    PubMed  Google Scholar 

  31. Hodgkin, A.L., Huxley, A.F.: A quantitative description of ion currents and its applications to conduction and excitation in nerve membranes. J. Physiol. (London) 117 (1952) 500–544

    Google Scholar 

  32. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79 (1982) 2554–2558

    PubMed  Google Scholar 

  33. Lamperti, J.: Probability. Benjamin, New York, 1966. Sects. 7 and 15.

    Google Scholar 

  34. Little, W.A., Shaw, G.L.: Analytical study of the memory storage capacity of a neural network. Math. Biosc. 39 (1974) 281–290

    Google Scholar 

  35. Pfeiffer, R.R., Kiang, Y.S.: Spike discharge patterns of spontaneous and continously stimulated activity in the cochlea nucleus. Biophys. J. 5 (1965) 301–316

    Google Scholar 

  36. Riedel, U., Kühn, R., van Hemmen, J.L.: Temporal sequences and chaos in neural nets. Phys. Rev. A 38 (1988) 1105–1108

    Google Scholar 

  37. Ritz, R., Gerstner, W., van Hemmen, J.L.: A biologically motivated and analytically soluble model of collective oscillations in the cortex: II. Application to binding and pattern segmentation. Biol. Cybern. (1994) submitted.

    Google Scholar 

  38. Ritz, R., Gerstner, W., van Hemmen, J.L.: Associative binding and segregation in a network of spiking neurons. In E. Domany, J.L. van Hemmen, and K. Schulten, Editors, Models of neural networks II. Springer, New York, 1994. Chap. 5.

    Google Scholar 

  39. Rudin, W.: Real and Complex Analysis. McGraw-Hill, New York, 1974. p. 63.

    Google Scholar 

  40. Sperling, G.: The information available in brief visual presentations. Psychol. Monogr. 74(11 Whole No. 498) (1960) 1–29

    Google Scholar 

  41. Stuart, G.J., Sakmann, B.: Active propagation of somatic action potentials into neocortical pyramidal cell dendrites. Nature 367 (1994) 69–72

    PubMed  Google Scholar 

  42. Sutherland, S.: Only four possible solutions. Nature 353 (1991) 389–390

    PubMed  Google Scholar 

  43. Sutton, J.P., Beis, J.S., Trainor, L.E.H.: Hierarchical model of memory and memory loss. J. Phys. A 21 (1988) 4443–4454

    Google Scholar 

  44. Trefz, T.: Oszillationen im Cortex. Diplomarbeit, Technische Universität München, 1991.

    Google Scholar 

  45. von der Malsburg, C.: The correlation theory of brain function. Internal Report 81-2, MPI für Biophysikalische Chemie, Göttingen, 1981; reprinted in E. Domany, J.L. van Hemmen, and K. Schulten, Editors, Models of neural networks II. Springer, New York, 1994. Chap. 2.

    Google Scholar 

  46. Wimbauer, S., Klemmer, N., van Hemmen, J.L.: Universality of unlearning. Neural Networks 7 (1994) in press

    Google Scholar 

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Authors and Affiliations

  1. Physik-Department, Technische Universität München, D-85747, Garching bei München, FR Germany

    J. Leo van Hemmen & Raphael Ritz

Authors
  1. J. Leo van Hemmen
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  2. Raphael Ritz
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Stig I. Andersson

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© 1995 Springer-Verlag Berlin Heidelberg

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van Hemmen, J.L., Ritz, R. (1995). Neural coding: A theoretical vista of mechanisms, techniques, and applications. In: Andersson, S.I. (eds) Analysis of Dynamical and Cognitive Systems. Lecture Notes in Computer Science, vol 888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58843-4_15

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  • DOI: https://doi.org/10.1007/3-540-58843-4_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58843-6

  • Online ISBN: 978-3-540-49113-2

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