Skip to main content

Modeling Visual Cortical Contrast Adaptation Effects

  • Chapter

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.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. D.G. Albrecht, S.B. Farrar, and D.B. Hamilton (1984) J. Physiol. 347: 713–739.

    PubMed  CAS  Google Scholar 

  2. C. Blakemore, R.H.S. Carpenter, and M. Georgeson (1970). Nature 228: 37–39.

    Article  PubMed  CAS  Google Scholar 

  3. A.B. Bonds (1991) Vis. Neurosci. 6: 239–255.

    Article  PubMed  CAS  Google Scholar 

  4. G.C. DeAngelis, A. Anzai, I. Ohzawa, and R.D. Freeman (1995). Proc. Natl. Acad. Sci (USA) 92: 9682–9686.

    Google Scholar 

  5. E.J. DeBruyn and A.B. Bonds (1986). Brain Research. 383: 339–342.

    Article  PubMed  CAS  Google Scholar 

  6. R.J. Douglas, K.A.C. Martin, and D. Whitteridge (1988). Nature 332: 642–644.

    Article  PubMed  CAS  Google Scholar 

  7. D.J. Heeger (1992). Vis. Neurosci. 9: 181–197.

    Article  PubMed  CAS  Google Scholar 

  8. L. Maffei, A. Fiorentini, and S. Bisti (1973). Science. 182: 1036–1038.

    Article  PubMed  CAS  Google Scholar 

  9. D.A. McCormick, B.W. Connors, J.W. Lighthall, and D.A. Prince, D.A. (1985). J. Neurophysiol., 54: 782.

    PubMed  CAS  Google Scholar 

  10. J. McLean and L.A. Palmer. (1996) Invest. Opthalmol. and Vis. Sci. Suppl. 37 (3): 2197.

    Google Scholar 

  11. J.A. Movshon and P. Lennie (1979). Nature 278: 850–852.

    Article  PubMed  CAS  Google Scholar 

  12. S.B. Nelson (1991). J. Neurosci. 11: 344–56.

    PubMed  CAS  Google Scholar 

  13. S.B. Nelson, J.A. Varela, K. Sen, and L.F. Abbott (1996). CNS96 Proceedings, Submitted.

    Google Scholar 

  14. Ohzawa, G. Sclar, and R.D. Freeman (1985). J. Neurophysiol. 54: 651–667.

    PubMed  CAS  Google Scholar 

  15. M.W. Pettet and C.D. Gilbert (1992). Proc. Natl. Acad. Sci (USA) 89: 8366–8370.

    Google Scholar 

  16. G. Sclar, 1. Ohzawa, and R.D. Freeman (1985). J. Neurophysiol. 54: 666–673.

    Google Scholar 

  17. D.C. Somers, S.B. Nelson, and M. Sur (1995) J. Neurosci. 15: 5448–5465.

    PubMed  CAS  Google Scholar 

  18. D.C. Somers, E.V. Todorov, A.G. Siapas, and M. Sur (1996) CNS96 Proceedings, this volume.

    Google Scholar 

  19. T.R. Vidyasagar (1990). Neuroscience 36: 175–179.

    Article  PubMed  CAS  Google Scholar 

  20. Worgotter, F. and Koch, C. (1991). J. Neurosci. 11: 1959.

    PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer Science+Business Media New York

About this chapter

Cite this chapter

Todorov, E.V., Siapas, A.G., Somers, D.C., Nelson, S.B. (1997). Modeling Visual Cortical Contrast Adaptation Effects. In: Bower, J.M. (eds) Computational Neuroscience. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9800-5_83

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-9800-5_83

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4757-9802-9

  • Online ISBN: 978-1-4757-9800-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics