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Population coding in cat visual cortex reveals nonlinear interactions as predicted by a neural field model

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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

We develop population coding ideas toward a general approach to the analysis of cortical function that operationalizes the notion of cooperativity. Neural ensemble activation distributions (population representations) are constructed over a defined stimulus parameter space, in our case the 2-dimensional retinal position of the central visual field. In contrast to classical approaches using receptive field centered stimuli the method presented here requires the stimulation of a whole cell ensemble with an identical common stimulus. The constructed activation distribution allows a quantitative investigation of activation dynamics and cooperative effects, like lateral inhibition and excitatory interaction. We simulated the data with a continuous neural network model as proposed by Wilson & Cowan [14].

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References

  1. Chang SS, Julesz B (1984) Vision Res 24: 1781–1788.

    Google Scholar 

  2. Dinse HR, Krüger K, Best J (1990). Concepts in Neuroscience (CINS) 1: 199–238.

    Google Scholar 

  3. Eckhorn R, Krause F, Nelson JI (1993) Biol. Cybern 69: 37–55.

    Google Scholar 

  4. Georgopoulos AP, Kettner RE, Schwartz AB (1988) J of Neurosc 8: 2928–2937.

    Google Scholar 

  5. Gilbert CD, Wiesel TN (1990) Vision Res 30: 1689–1701.

    Google Scholar 

  6. Heggelund P (1981) Exp Brain Res 42: 99–107.

    Google Scholar 

  7. Hock HS, Kelso JAS, Schöner G (1993) J Exp Psych HPP 19: 63–80.

    Google Scholar 

  8. Lee C, Rohrer WH, Sparks OL (1988) Nature 332: 357–360.

    Google Scholar 

  9. Lehky SR, Seijnowski TJ (1990) J Neurosc 10 (7): 2281–2299.

    Google Scholar 

  10. Movshon JA, Thompson ID, Tolhurst DJ (1978) J Physiol 283: 79–99.

    Google Scholar 

  11. Nelson, S.B. (1991) J. of Neurosc. 11 (2): 344–356.

    Google Scholar 

  12. Stern E, Aertsen A, Vaadia E, Hochstein S (1993) in: Giles CL, Hanson SJ, Cowan JD (eds) Morgan Kaufmann Publishers.

    Google Scholar 

  13. Williams D, Phillips G, Sekuler R (1986) Nature 324 (20): 253–255.

    Google Scholar 

  14. Wilson HR, Cowan HR (1973) Kybernetik 13: 55–80.

    Google Scholar 

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Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Jancke, D. et al. (1996). Population coding in cat visual cortex reveals nonlinear interactions as predicted by a neural field model. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_109

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  • DOI: https://doi.org/10.1007/3-540-61510-5_109

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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