Skip to main content

A Model That Captures Receptive Field Properties of Orientation Selective Neurons in the Visual Cortex

  • Conference paper
  • 73 Accesses

Part of the Lecture Notes in Computer Science book series (LNCS,volume 3316)

Abstract

A purely feedforward model has been shown to produce realistic simple cell receptive fields (RFs). The modeled cells capture a wide range of receptive field properties of orientation selective cortical cells in the primary visual cortex. We have analyzed the responses of 72 nearby cell pairs to study which RF properties are clustered. Orientation preference shows strongest clustering and RF phase the least clustering. Our results agree well with experimental data (DeAngelis et al, 1999, Swindale et al, 2003).

Keywords

  • visual cortex
  • orientation selectivity
  • receptive field
  • neuron

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-540-30499-9_8
  • Chapter length: 7 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   74.99
Price excludes VAT (USA)
  • ISBN: 978-3-540-30499-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bhaumik, B., Mathur, M.: A Cooperation and Competition Based Simple Cell Receptive Field Model and Study of Feed-Forward Linear and Nonlinear Contributions to Orientation Selectivity. Journal of Computational Neuroscience 14, 211–227 (2003)

    CrossRef  Google Scholar 

  2. Blasdel, G.: Orientation selectivity, preference, and continuity in monkey striate cortex. J. Neurosci. 12, 3139–3161 (1992)

    Google Scholar 

  3. Bonhoeffer, T.: Neurotrophins and activity dependent development of the neocortex. Curr.: Opin. Neurobiol 6, 119–126 (1996)

    CrossRef  Google Scholar 

  4. Carandini, M., Heeger, D.: Summation and division by neurons in the visual cortex. Science 264, 1333–1336 (1994)

    CrossRef  Google Scholar 

  5. DeAngelis, G.C., Ghose, G.M., Ohzawa, I., Freeman, R.: Functional micro-organization of primary visual cortex: receptive field analysis of nearby neurons. Journal of Neuroscience 19, 4046–4064 (1999)

    Google Scholar 

  6. Douglas, R.J., Koch, C., Mahowald, M., Martin, K.A.C., Suarez, H.: Recurrent excitation in neocortical circuits. Science 269, 981–985 (1995)

    CrossRef  Google Scholar 

  7. Ferster, D.: Origin of orientation selective EPSPs in simple cells of the cat visual cortex. Journal of Neuroscience 7, 1780–1791 (1987)

    Google Scholar 

  8. Freeman, R.: Cortical columns: A multi-parameter examination. Cereb Cortex 13, 70–72 (2003)

    CrossRef  Google Scholar 

  9. Gerstner, W.: Spiking Neurons. In: Mass, W., Bishop, C.M. (eds.) Pulsed Neural Networks, pp. 3–54. MIT Press, Cambridge (1999); Grinvald, A, Lieke, E, Frostig, R.D., Gilbert, C.D, Wiesel, T.N.: Functional architecture of cortex revealed by optical imaging of intrinsic signals. Nature, Vol. 324, pp. 361-364 (1986)

    Google Scholar 

  10. Hubel, D.H., Wiesel, T.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology 160, 106–154 (1962)

    Google Scholar 

  11. Koch, C., Poggio, T.: The synaptic veto mechanism: does it underlie direction and orientation selectivity in the visual cortex? In: Rose, D.R., Dobson, V.G. (eds.) Models of the visual cortex, pp. 408–419. John Wiley, New York (1985)

    Google Scholar 

  12. Linsker, R.: From basic network principles to neural architecture: Emergence of spatialopponent cells. In: Proceedings of National Academy of Sciences, USA, vol. 83, pp. 7508–7512 (1986)

    Google Scholar 

  13. Maldonado, P.E., Gödecke, I., Gray, C.M., Bonhoeffer, T.: Orientation selectivity in pinwheel centers in cat striate cortex. Science 276, 1551–1555 (1997)

    CrossRef  Google Scholar 

  14. Miller, K.: A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between ON and OFF center inputs. Journal of Neuroscience 14, 409–441 (1994)

    Google Scholar 

  15. Somers, D.C., Nelson, S.B., Sur, M.: An emergent model of orientation selectivity in cat visual cortical simple cells. Journal of Neuroscience 15, 5448–5465 (1995)

    Google Scholar 

  16. Swindale, N.V.: Spatial pattern of response magnitude and selectivity for orientation and direction in cat visual cortex. Cerebral Cortex 13, 225–238 (2003)

    CrossRef  Google Scholar 

  17. Von der Malsburg, C.: Self Organization of orientation selective cells in the striate cortex. Kybernetik 14, 85–100 (1973)

    CrossRef  Google Scholar 

  18. Wörgötter, F., Koch, C.: A detailed model of the primary visual pathway in the cat: Comparison of afferent excitatory and intracortical inhibitory connection schemes for orientation selectivity. Journal of Neuroscience 11(7), 1959–1979 (1991)

    Google Scholar 

  19. Xiong, M., Pallas, S.L., Lim, S., Finlay, B.: Regulation of retinal ganglion cell axon arbor size by target availability: Mechanism of compression and expansion of the retinotectal projection. J. Comp. Neurol 344, 581–597 (1994)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bhaumik, B., Agarwal, A., Mathur, M., Manohar, M. (2004). A Model That Captures Receptive Field Properties of Orientation Selective Neurons in the Visual Cortex. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30499-9_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23931-4

  • Online ISBN: 978-3-540-30499-9

  • eBook Packages: Springer Book Archive