116
Views
0
CrossRef citations to date
0
Altmetric
Original Article

A model of the response of visual area V2 to combinations of orientations

Pages 105-122 | Received 14 Oct 2011, Accepted 29 Apr 2012, Published online: 23 May 2012

References

  • Anzai A, Peng X, Van Essen DC. Neurons in monkey visual area V2 encode combinations of orientations. Nature Neuroscience 2007; 10: 1313–1321
  • Bednar JA. Learning to see: Genetic and environmental influences on visual development. Unpublished doctoral dissertation, University of Texas at Austin. (Tech Report AI-TR-02-294). 2002
  • Bednar JA. Topographica: Building and analyzing map-level simulations from Python, C/C++, MATLAB, NEST, or NEURON components. Frontiers in Neuroinformatics 2009; 3: 8
  • Bednar JA, Miikkulainen R. Joint maps for orientation, eye, and direction preference in a self-organizing model of V1. Neurocomputing 2006; 69: 1272–1276
  • Berkes P, Wiskott L. Slow feature analysis yields a rich repertoire of complex cell properties. Journal of Vision 2005; 5: 579–602
  • Boynton GM, Hegdé J. Visual cortex: The continuing puzzle of area V2. Current Biology 2004; 14: R523–R524
  • Dowling JE. The retina: An approachable part of the brain. Cambridge University Press, CambridgeUK 1987
  • Hegdé J, Van Essen DC. Selectivity for complex shapes in primate visual area V2. Journal of Neuroscience 2000; 20: 4117–4130
  • Hegdé J, Van Essen DC. Strategies of shape representation in macaque visual area V2. Visual Neuroscience 2003; 20: 313–328
  • Hegdé J, Van Essen DC. A comparative study of shape representation in macaque visual areas V2 and V4. Cerebral Cortex 2007; 17: 1100–1116
  • Hubel D, Wiesel T. Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex. Journal of Physiology 1962; 160: 106–154
  • Hyvärinen A, Hoyer PO. A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images. Vision Research 2001; 41: 2413–2423
  • Ito M, Goda N. Mechanisms underlying the representation of angles embedded within contour stimuli in area V2 of macaque monkeys. European Journal of Neuroscience 2011; 33: 130–142
  • Ito M, Komatsu H. Representation of angles embedded within contour stimuli in area V2 of macaque monkeys. Journal of Neuroscience 2004; 24: 3313–3324
  • Kobatake E, Tanaka K. Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. Journal of Neurophysiology 1994; 71: 856–867
  • Lee H, Ekanadham C, Ng AY. Sparse deep belief net model for visual area V2. Advances in neural information processing systems, J Platt, D Koller, Y Singer, S Roweis. MIT Press, Cambridge, MA 2008; 20: 873–880
  • Lu HD, Roe AW. Functional organization of color domains in V1 and V2 of macaque monkey revealed by optical imaging. Cerebral Cortex 2007; 18: 516–533
  • Miikkulainen R, Bednar J, Choe Y, Sirosh J. Computational maps in the visual cortex. Springer-Science, New York 2005
  • Moré JJ, Garbow BS, Hillstrom KE. User guide for minpack 1 (Tech. Rep. No. ANL-80-74). Argonne National Laboratory, Chicago, IL 1980
  • Plebe A. A model of angle selectivity development in visual area V2. Neurocomputing 2007; 70: 2060–2066
  • Plebe A. The ventral visual path: Moving beyond V1 with computational models. Visual cortex: New research, TA Portocello, RB Velloti. Nova Science Publishers, New York 2008; 97–160
  • Plebe A, Domenella RG. The emergence of visual object recognition. Duch W, Kacprzyk J, Oja E, Zadrony S, editors. 2005, Artificial neural networks – icann 2005 15th international conference, warsaw. Berlin: Springer-Verlag, pp. 507–512
  • Plebe A, Domenella RG. Early development of visual recognition. BioSystems 2006; 86: 63–74
  • Plebe A, Domenella RG. Object recognition by artificial cortical maps. Neural Networks 2007; 20: 763–780
  • Schira MM, Wade AR, Tyler CW. Two-dimensional mapping of the central and parafoveal visual field to human visual cortex. Journal of Neurophysiology 2007; 97: 4284–4295
  • Sirosh J, Miikkulainen R. Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex. Neural Computation 1997; 9: 577–594
  • Sit YF, Miikkulainen R. Computational predictions on the receptive fields and organization of V2 for shape processing. Neural Computation 2009; 21: 762–785
  • Skottun BC, De Valois RL, Grosof DH, Movshon A, Albrecht DG, Bonds A. Classifying simple and complex cells on the basis of response modulation. Vision Research 1991; 31: 1079–1086
  • Talbot SA. A lateral localization in the cat's visual cortex. Federation Proceeding 1942; 1: 84
  • Taylor NR, Hartley M, Taylor, JG. 2005. Coding of objects in lowlevel visual cortical areas. In: Duch W, Kacprzyk J, Oja E, Zadrony S, editors. Artificial neural networks – icann '05. 15th international conference proceedings. Berlin: Springer-Verlag, pp. 57–63
  • Thompson JM, Woolsey CN, Talbot SA. Visual areas I and II of cerebral cortex of rabbit. Journal of Neurophysiology 1950; 13: 277–288
  • von der Heydt R, Zhou H, Friedman HS. Representation of stereoscopic edges in monkey visual cortex. Vision Research 2000; 40: 1955–1967
  • von der Heydt R, Zhou H, Friedman HS. Neural coding of border ownership: Implications for the theory of figure-ground perception. Perceptual organization in vision: Behavioral and neural perspectives, M Behrmann, R Kimchi, CR Olson. Lawrence Erlbaum Associates, Mahwah, NJ 2003; 281–304
  • von der Malsburg C. Self-organization of orientation sensitive cells in the striate cortex. Kybernetic 1973; 14: 85–100
  • Von Mises R. Wahrscheinlichkeitsrechnung und ihre Anwendungen in der Statistik und theoretischen Physik. Franz Deuticke, Leipzig, DE 1931
  • Wallis G, Rolls E. Invariant face and object recognition in the visual system. Progress in Neurobiology 1997; 51: 167–194
  • Willshaw DJ, von der Malsburg C. How patterned neural connections can be set up by self-organization. Proceedings of the Royal Society of London 1976; B194: 431–445
  • Wiskott L, Sejnowski TJ. Slow feature analysis: Unsupervised learning of invariances. Neural Computation 2002; 14: 715–770

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.