Modeling Visual Cortical Contrast Adaptation Effects

  • E. V. Todorov
  • A. G. Siapas
  • D. C. Somers
  • S. B. Nelson

Abstract

We demonstrate model visual cortical circuits which exhibit robust contrast adaptation properties, consistent with physiological observations in V1. The adaptation mechanism we employ is activity-dependent synaptic depression at thalamocortical and local intra-cortical synapses. Model contrast response functions (CRF) shift so that cells remain maximally responsive to changes around the recent average stimulus contrast level. Hysteresis effects for both stimulus contrast and orientation are achieved; orientation hysteresis is weaker, and depends exclusively on intracortical adaptation. Following stimulation of the receptive field (RF) surround, RFs dynamically expand to “fill in” for the missing stimulation in the RF center; in our model this expansion results from adaptation of local inhibitory synapses, triggered by excitation from long range horizontal projections. All adaptation effects are achieved using the same synaptic depression mechanisms.

Keywords

Receptive Field Adaptation Effect Hysteresis Effect Contrast Level Synaptic Depression 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    D.G. Albrecht, S.B. Farrar, and D.B. Hamilton (1984) J. Physiol. 347: 713–739.PubMedGoogle Scholar
  2. [2]
    C. Blakemore, R.H.S. Carpenter, and M. Georgeson (1970). Nature 228: 37–39.PubMedCrossRefGoogle Scholar
  3. [3]
    A.B. Bonds (1991) Vis. Neurosci. 6: 239–255.PubMedCrossRefGoogle Scholar
  4. [4]
    G.C. DeAngelis, A. Anzai, I. Ohzawa, and R.D. Freeman (1995). Proc. Natl. Acad. Sci (USA) 92: 9682–9686.Google Scholar
  5. [5]
    E.J. DeBruyn and A.B. Bonds (1986). Brain Research. 383: 339–342.PubMedCrossRefGoogle Scholar
  6. [6]
    R.J. Douglas, K.A.C. Martin, and D. Whitteridge (1988). Nature 332: 642–644.PubMedCrossRefGoogle Scholar
  7. [7]
    D.J. Heeger (1992). Vis. Neurosci. 9: 181–197.PubMedCrossRefGoogle Scholar
  8. [8]
    L. Maffei, A. Fiorentini, and S. Bisti (1973). Science. 182: 1036–1038.PubMedCrossRefGoogle Scholar
  9. [9]
    D.A. McCormick, B.W. Connors, J.W. Lighthall, and D.A. Prince, D.A. (1985). J. Neurophysiol., 54: 782.PubMedGoogle Scholar
  10. [10]
    J. McLean and L.A. Palmer. (1996) Invest. Opthalmol. and Vis. Sci. Suppl. 37 (3): 2197.Google Scholar
  11. [11]
    J.A. Movshon and P. Lennie (1979). Nature 278: 850–852.PubMedCrossRefGoogle Scholar
  12. [12]
    S.B. Nelson (1991). J. Neurosci. 11: 344–56.PubMedGoogle Scholar
  13. [13]
    S.B. Nelson, J.A. Varela, K. Sen, and L.F. Abbott (1996). CNS96 Proceedings, Submitted.Google Scholar
  14. [14]
    Ohzawa, G. Sclar, and R.D. Freeman (1985). J. Neurophysiol. 54: 651–667.PubMedGoogle Scholar
  15. [15]
    M.W. Pettet and C.D. Gilbert (1992). Proc. Natl. Acad. Sci (USA) 89: 8366–8370.Google Scholar
  16. [16]
    G. Sclar, 1. Ohzawa, and R.D. Freeman (1985). J. Neurophysiol. 54: 666–673.Google Scholar
  17. [17]
    D.C. Somers, S.B. Nelson, and M. Sur (1995) J. Neurosci. 15: 5448–5465.PubMedGoogle Scholar
  18. [18]
    D.C. Somers, E.V. Todorov, A.G. Siapas, and M. Sur (1996) CNS96 Proceedings, this volume.Google Scholar
  19. [19]
    T.R. Vidyasagar (1990). Neuroscience 36: 175–179.PubMedCrossRefGoogle Scholar
  20. [20]
    Worgotter, F. and Koch, C. (1991). J. Neurosci. 11: 1959.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • E. V. Todorov
    • 1
  • A. G. Siapas
    • 1
  • D. C. Somers
    • 1
  • S. B. Nelson
    • 2
  1. 1.Department of Brain and Cognitive SciencesMITCambridgeUSA
  2. 2.Department of BiologyBrandeis UniversityWalthamUSA

Personalised recommendations