Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Center-Surround Processing, Computational Role of

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_569-1

Synonyms

Definition

Center-surround processing (CSP) describes the integration of localized “center” with contextual (“surround”) information into a more global representation. It has been studied mostly as a sensory computation, in particular in the early visual system (Carandini et al. 2005). Although center and surround are often defined in space, CSP is a more general processing in the sense that it is also performed in other physical dimensions such as in the auditory domain or in time (Schwartz et al. 2007). In neuroscientific contexts, CSP is defined rather mechanistically as the difference between the processing of localized and extended stimuli. Signatures for CSP are found in many different brain areas, where the responses of neurons to a localized stimulus are strongly and often nonlinearly influenced by its context. Computationally, these processes are thought to be...

Keywords

Retina 
This is a preview of subscription content, log in to check access

References

  1. Bach M (2013) 106 Visual phenomena & optical illusions. http://www.michaelbach.de/ot/. Accessed 6 Oct 2013
  2. Carandini M, Heeger DJ (1994) Summation and division by neurons in primate visual cortex. Science 264:1333–1336PubMedCrossRefGoogle Scholar
  3. Carandini M, Demb JB, Mante V, Tolhurst DJ, Dan Y, Olshausen BA, Gallant JL, Rust NC (2005) Do we know what the early visual system does? J Neurosci 25:10577–10597PubMedCrossRefGoogle Scholar
  4. Cavanaugh JR, Bair W, Movshon JA (2002) Nature and interaction of signals from the receptive field center and surround in macaque V1 neurons. J Neurophysiol 88:2530–2546PubMedCrossRefGoogle Scholar
  5. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR 2005 (IEEE computer society conference on computer vision and pattern recognition), vol 1, pp 886–893Google Scholar
  6. Ernst UA, Mandon S, Schinkel–Bielefeld N, Neitzel SD, Kreiter AK, Pawelzik KR (2012) Optimality of human contour integration. PLoS Comput Biol 8(5):e1002520. doi:10.1371/journal.pcbi.1002520PubMedCrossRefPubMedCentralGoogle Scholar
  7. Field DJ, Hayes A, Hess RF (1993) Contour integration by the human visual system: evidence for a local “Association Field”. Vision Res 33(2):173–193PubMedCrossRefGoogle Scholar
  8. Freeman RR, Ohzawa I, Walker G (2001) Beyond the classical receptive field in the visual cortex. Prog Brain Res 134:157–170PubMedCrossRefGoogle Scholar
  9. Geffen MN, de Vries SE, Meister M (2007) Retinal ganglion cells can rapidly change polarity from off to on. PLoS Biol 5:e65PubMedCrossRefPubMedCentralGoogle Scholar
  10. Geisler WS, Perry JS, Super BJ, Gallogly DP (2001) Edge co-occurrence in natural images predicts contour grouping performance. Vision Res 41:711–724PubMedCrossRefGoogle Scholar
  11. Graham DJ, Chandler DM, Field DJ (2006) Can the theory of “whitening” explain the center-surround properties of retinal ganglion cell receptive fields? Vision Res 46(18):2901–2913PubMedCrossRefPubMedCentralGoogle Scholar
  12. Herzog M (2009) Binding Problem. In: Binder M, Hirokawa N, Windhorst U (eds) Encyclopedia of Neuroscience: SpringerReference (www.springerreference.com). Springer-Verlag Berlin Heidelberg, 2011-01-31 23:00:00 UTC, http://www.springerreference.com/docs/html/chapterdbid/114175.html
  13. Ichida JM, Schwabe L, Bressloff PC, Angelucci A (2007) Response facilitation from the “suppressive” receptive field surround of macaque V1 neurons. J Neurophysiol 98:2168–2181PubMedCrossRefGoogle Scholar
  14. Kretzberg J, Ernst UA (2013) Vision. In: Galizia L (ed) Neurosciences, 1st edn. SpringerSpektrum, Berlin/Heidelberg, pp 363–407Google Scholar
  15. Levitt JB, Lund JS (1997) Contrast dependence of contextual effects in primate visual cortex. Nature 387:73–76PubMedCrossRefGoogle Scholar
  16. Li Z (1998) A neural model of contour integration in the primary visual cortex. Neural Comput 10:903–940PubMedCrossRefGoogle Scholar
  17. Lindeberg T (2012) Scale-invariant feature transform. Scholarpedia 7(5):10491. Accessed 6 Oct 2013Google Scholar
  18. Lochmann T, Ernst UA, Deneve S (2012) Perceptual inference predicts contextual modulations of sensory responses. J Neurosci 32:4179–4195PubMedCrossRefGoogle Scholar
  19. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110CrossRefGoogle Scholar
  20. Machens CK, Wehr MS, Zador AM (2004) Linearity of cortical receptive fields measured with natural sounds. J Neurosci 24:1089–1100PubMedCrossRefGoogle Scholar
  21. Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607–609PubMedCrossRefGoogle Scholar
  22. Polat U, Mizobe K, Pettet MW, Kasamatsu T, Norcia AM (1998) Collinear stimuli regulate visual responses depending on cell’s contrast threshold. Nature 391:580–584PubMedCrossRefGoogle Scholar
  23. Rao RP, Ballard DH (1999) Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat Neurosci 2:79–87PubMedCrossRefGoogle Scholar
  24. Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in cortex. Nat Neurosci 2:1019–1025PubMedCrossRefGoogle Scholar
  25. Rozell CJ, Johnson DH, Baraniuk RG, Olshausen BA (2008) Sparse coding via thresholding and local competition in neural circuits. Neural Comput 20:2526–2563PubMedCrossRefGoogle Scholar
  26. Schwabe L, Obermayer K, Angelucci A, Bressloff PC (2006) The role of feedback in shaping the extra-classical receptive field of cortical neurons: a recurrent network model. J Neurosci 26:9117–9129PubMedCrossRefGoogle Scholar
  27. Schwartz O, Hsu A, Dayan P (2007) Space and time in visual context. Nat Rev Neurosci 8:522–535PubMedCrossRefGoogle Scholar
  28. Series P, Lorenceau J, Fregnac Y (2003) The “silent” surround of V1 receptive fields: theory and experiments. J Physiol (Paris) 97:453–474CrossRefGoogle Scholar
  29. Shushruth S, Ichida JM, Levitt JB, Angelucci A (2009) Comparison of spatial summation properties of neurons in macaque V1 and V2. J Neurophysiol 102:21069–22083Google Scholar
  30. Sigman M, Cecchi GA, Gilbert CD, Magnasco MO (2001) On a common circle: natural scenes and Gestalt rules. Proc Natl Acad Sci USA 98:1935–1940PubMedCrossRefPubMedCentralGoogle Scholar
  31. Sillito AM, Grieve KL, Jones HE, Cudeiro J, Davis J (1995) Visual cortical mechanisms detecting focal orientation discontinuities. Nature 378:492–496PubMedCrossRefGoogle Scholar
  32. Simoncelli EP, Olshausen BA (2001) Natural image statistics and neural representation. Annu Rev Neurosci 24:1193–1216PubMedCrossRefGoogle Scholar
  33. Solomon SG, Lee BB, Sun H (2006) Suppressive surrounds and contrast gain in magnocellular-pathway retinal ganglion cells of macaque. J Neurosci 26:8715–8726PubMedCrossRefPubMedCentralGoogle Scholar
  34. Spratling MW (2010) Predictive coding as a model of response properties in cortical area V1. J Neurosci 30:3531–3543PubMedCrossRefGoogle Scholar
  35. Srinivasan MV, Laughlin SB, Dubs A (1982) Predictive coding: a fresh view of inhibition in the retina. Proc R Soc Lond B Biol Sci 216:427–459PubMedCrossRefGoogle Scholar
  36. Theunissen FE, Sen K, Doupe AJ (2000) Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J Neurosci 20:2315–2331PubMedGoogle Scholar
  37. Todorović, D (2007). W. Metzger: Laws of Seeing. Gestalt Theory, 28, 176–180Google Scholar
  38. Vinje WE, Gallant JL (2000) Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287:1273–1276PubMedCrossRefGoogle Scholar
  39. von Helmholtz H (1856) Handbook of physiological optics. Leopold Voss, LeipzigGoogle Scholar
  40. Zhaoping L (2006) Theoretical understanding of the early visual processes by data compression and data selection. Network 17:301–334PubMedCrossRefGoogle Scholar
  41. Zhu M, Rozell CJ (2013) Visual nonclassical receptive field effects emerge from sparse coding in a dynamical system. PLoS Comp Biol 9(8):e1003191. doi:10.1371/journal.pcbi.1003191Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Computational NeuroscienceInstitute for Theoretical Physics, University of BremenBremenGermany