Skip to main content

Advertisement

Log in

Continuum limit of discrete neuronal structures: is cortical tissue an “excitable” medium?

  • Published:
Biological Cybernetics Aims and scope Submit manuscript

Abstract

As a simple model of cortical tissue, we study a locally connected network of spiking neurons in the continuum limit of space and time. This is to be contrasted with the usual numerical simulations that discretize both of them. Refractoriness, noise, axonal delays, and the time course of excitatory and inhibitory postsynaptic potentials have been taken into account explicitly. We pose, and answer, the question of whether the continuum limit presents a full description of scenarios found numerically (the answer is no, not quite). In other words, can the numerics be reduced to a continuum description of a well-known type? As a corollary, we derive some classical results such as those of Wilson and Cowan (1973), thus indicating under what conditions they are valid. Furthermore, we show that spatially discrete objects may be fragile due to noise arising from the stochasticity of the individual neurons, whereas they are not once the continuum limit has been taken. This, then, resolves the above question. Finally, we indicate how one can directly incorporate orientation preference of the neurons.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Bonhoeffer T, Grinvald A (1991) Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns. Nature 353:429–431

    Google Scholar 

  2. Bonhoeffer T, Grinvald A (1993) The layout of iso-orientation domains in area 18 of cat visual cortex: optical imaging reveals a pinwheel-like organization. J Neurosci 13:4157–4180

    Google Scholar 

  3. Braitenberg V, Schütz A (1991) Anatomy of the cortex: statistics and geometry. Springer, Berlin Heidelberg New York

  4. Breiman L (1968) Probability. Addison-Wesley, Reading, MA (Sect. 3.6)

  5. Cowan JD (1968) Statistical mechanics of nervous nets. In: Caianiello ER (ed) Neural networks. Springer, Berlin Heidelberg New York, pp 181–188

  6. Cowan JD (1985) What do drug-induced visual hallucinations tell us about the brain? In: Levy WB, Anderson JA, Lehmkuhle S (eds) Synaptic modification, neuron selectivity, and nervous system organization. Erlbaum, Hillsdale, NJ, pp 223–241

  7. Cross MC, Hohenberg PC (1993) Pattern formation outside of equilibrium. Rev Mod Phys 65:851–1112

    Google Scholar 

  8. Ermentrout G, Cowan JD (1979) A mathematical theory of visual hallucination patterns. Biol Cybern 34:137–150

    Google Scholar 

  9. Ermentrout GB, Cowan JD (1979) Temporal oscillations in neuronal nets. J Math Biol 7:265–28

    Google Scholar 

  10. Ermentrout GB, Cowan JD (1980) Large scale spatially organized activity in neural nets. SIAM J Appl Math 38:1–21 [especially Eqs. (1.6)–(1.8)]

    Google Scholar 

  11. Feldman JL, Cowan JD (1975) Large-scale activity in neural nets: I. Theory with applications to motoneuron pool responses. Biol Cybern 17:29–38 (see in particular mathematical appendix)

    Google Scholar 

  12. Fohlmeister C (1994) Modellierung von Halluzinationen im visuellen Cortex. Diploma thesis, Physik Department, Technische Universität München

  13. Fohlmeister C, Ritz R, Gerstner W, van Hemmen JL (1995) Spontaneous excitations in the visual cortex: stripes, spirals, rings, and collective bursts. Neural Comput 7:905–914

    Google Scholar 

  14. Gerstner W (1995) Time structure of the activity in neural network models. Phys Rev E 51:738–758

    Google Scholar 

  15. Gerstner W, van Hemmen JL (1992) Associative memory in a network of ‘spiking’ neurons. Network 3:139–164

    Google Scholar 

  16. Gerstner W, van Hemmen JL (1993) Coherence and incoherence in a globally coupled ensemble of pulse-emitting units. Phys Rev Lett 71:312–315

    Google Scholar 

  17. Gerstner W, van Hemmen JL (1994) Coding and information processing in neural networks. In: Domany E, van Hemmen JL, Schulten K (eds) Models of neural networks II. Springer, Berlin Heidelberg New York (Chap 1)

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

    Google Scholar 

  19. Gerstner W, van Hemmen JL, Cowan JD (1996) What matters in neuronal locking? Neural Comput 8:1689–1712

    Google Scholar 

  20. An der Heiden U (1980) Analysis of neural networks. Springer, Berlin Heidelberg New York

  21. Hessler NA, Shirke AM, Mallnow R (1993) The probability of transmitter release at a mammalian central synapse. Nature 366:569–572

    Google Scholar 

  22. Hopfield JJ (1984) Neurons with graded response have computational properties like those of two-state neurons. Proc Natl Acad Sci USA 81:3088–3092

    Google Scholar 

  23. Kandel ER, Schwartz JH, (eds) (1985) Principles of neural science, 2nd edn. Elsevier, New York

  24. Kistler W, Gerstner W, van Hemmen JL (1997) Reduction of Hodgkin–Huxley equations to single-variable threshold model. Neural Comput 9:1015–1045

    Google Scholar 

  25. Kistler WM, Seitz R, van Hemmen JL (1998) Modeling collective excitations in cortical tissue. Physica D 114:273–295

    Google Scholar 

  26. Klüver H (1966) Mescal and the mechanisms of hallucination. University of Chicago Press, Chicago (especially pp 65–80)

  27. Lamperti J (1966) Probability. Benjamin, New York

  28. Levin SA, Segel LA (1985) Pattern generation in space and aspect. SIAM Rev 27:45–67

    Google Scholar 

  29. Meron E (1992) Pattern formation in excitable media. Phys Rep 218:1–66

    Google Scholar 

  30. Milton JG, Mundel T, an der Heiden U, Spire J-P, Cowan JD (1995) Activity waves in neural networks. In: Arbib MA (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge, MA

  31. Murray JD (1989) Mathematical biology. Springer, Berlin Heidelberg New York (especially pp 161–166, 328–335, 481–505)

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

    Google Scholar 

  33. Rosenmund C, Clements JD, Westbrook G (1993) Nonuniform probability of glutamate release at a hyppocampal synapse. Science 262:754–757

    Google Scholar 

  34. Sattinger DH (1979) Group theoretic methods in bifurcation theory. Lecture notes in mathematics, vol 762. Springer, Berlin Heidelberg New York

  35. Sattinger DH (1980) Symmetry breaking and bifurcation in applied mathematics. Bull Am Math Soc 3:779–819

    Google Scholar 

  36. Sattinger DH (1983) Branching in the presence of symmetry. SIAM, Philadelphia

  37. Sholl DA (1956) The organization of the cerebral cortex. Wiley, New York

  38. Siegel RK, West LJ (1975) Hallucinations: behavior, experience, and theory. Wiley, New York

  39. Siegel RK (1977) Hallucinations. Sci Am 237(4): 132–140

    Google Scholar 

  40. Tyson JJ, Keener JP (1988) Singular perturbation theory of traveling waves in excitable media (a review). Physica D 32: 327–361

    Google Scholar 

  41. Wilson HR, Cowan JD (1973) A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13:55–80 [especially Eqs. (1.3.1) and (1.3.2)]

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. L. van Hemmen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

van Hemmen, J. Continuum limit of discrete neuronal structures: is cortical tissue an “excitable” medium?. Biol Cybern 91, 347–358 (2004). https://doi.org/10.1007/s00422-004-0530-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00422-004-0530-2

Keywords

Navigation