Abstract

Construction of surface is a crucial step toward the representation of shape through the integration of local information. Physiological studies have reported that the primary visual cortex (V1) codes the medial axis (MA) that is a skeletal structure equidistant from nearby contours, suggesting the early representation of surface in V1. Although the neural basis of surface construction has not been clarified, the onset synchronization of border ownership (BO)-selective cells is a plausible candidate for the generation of surface. We investigated computationally the representation of surface in a biophysically detailed model of primate V1-V2 networks. The simulation results showed that the simultaneous arrival of signals from BO-selective cells evoked strong responses of V1 cells located around the MA. The simulation results lead to a prediction that the perception of the direction of figure (DOF) depends on the degree of synchronous presentation of contour. We conducted a psychophysical experiment and showed that the perception of the DOF is biased toward a highly synchronized contour. These results suggest a crucial role of the onset synchronization of BO-selective cells for the construction of early representation of shape.

© 2014 Optical Society of America

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  1. H. Zhou, H. S. Friedman, and R. von der Heydt, “Coding of border ownership in monkey visual cortex,” J. Neurosci. 20, 6594–6611 (2000).
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  3. K. Sakai and H. Nishimura, “Surrounding suppression and facilitation in the determination of border ownership,” J. Cogn. Neurosci. 18, 562–579 (2006).
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    [CrossRef]
  5. T. S. Lee, D. Mumford, R. Romero, and V. A. F. Lamme, “The role of the primary visual cortex in higher level vision,” Vis. Res. 38, 2429–2454 (1998).
    [CrossRef]
  6. X. Huang and M. A. Paradiso, “V1 response timing and surface filling-in,” J. Neurophysiol. 100, 539–547 (2008).
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  7. V. A. F. Lamme, “The neurophysiology of figure-ground segregation in primary visual cortex,” J. Neurosci. 15, 1605–1615 (1995).
  8. K. Zipser, V. A. F. Lamme, and P. H. Schiller, “Contextual modulation in primary visual cortex,” J. Neurosci. 16, 7376–7389 (1996).
  9. M. D. Lescroart and I. Biederman, “Cortical representation of medial axis structure,” Cereb. Cortex 23, 629–637 (2013).
    [CrossRef]
  10. C. C. Hung, E. T. Carlson, and C. E. Connor, “Medial axis shape coding in macaque inferotemporal cortex,” Neuron 74, 1099–1113 (2012).
    [CrossRef]
  11. I. Kovacs and B. Julesz, “Perceptual sensitivity maps within globally defined visual shapes,” Nature 370, 644–646 (1994).
    [CrossRef]
  12. I. Kovacs, A. Feher, and B. Julesz, “Medial-point description of shape: a representation for action coding and its psychophysical correlates,” Vis. Res. 38, 2323–2333 (1998).
    [CrossRef]
  13. Y. Hatori and K. Sakai, “Robust detection of medial-axis by onset synchronization of border-ownership selective cells and shape reconstruction from its medial-axis,” Lect. Notes Comput. Sci. 5506, 301–309 (2009).
    [CrossRef]
  14. D. Marr and H. K. Nishihara, “Representation and recognition of the spatial organization of three-dimensional shapes,” Proc. R. Soc. Lond. B, Biol. Sci. 200, 269–294 (1978).
    [CrossRef]
  15. B. B. Kimia, “On the role of medial geometry in human vision,” J. Physiol. Paris 97, 155–190 (2003).
  16. V. Froyen, J. Feldman, and M. Singh, “A bayesian framework for figure–ground interpretation,” Adv. Neural Inf. Process Syst. 23, 631–639 (2010).
  17. Y. Dong, S. Mihalas, F. Qiu, R. von der Heydt, and E. Niebur, “Synchrony and the binding problem in macaque visual cortex,” J. Vis. 8(7):30, 1–16 (2008).
  18. J. M. Samonds and A. B. Bonds, “Gamma oscillation maintains stimulus structure-dependent synchronization in cat visual cortex,” J. Neurophysiol. 93, 223–236 (2005).
    [CrossRef]
  19. Z. Zhou, M. R. Bernard, and A. B. Bonds, “Deconstruction of spatial integrity in visual stimulus detected by modulation of synchronized activity in cat visual cortex,” J. Neurosci. 28, 3759–3768 (2008).
    [CrossRef]
  20. A. Angelucci, J. B. Levitt, E. J. S. Walton, J. M. Hupe, J. Bullier, and J. S. Lund, “Circuits for local and global signal integration in primary visual cortex,” J. Neurosci. 22, 8633–8646 (2002).
  21. A. B. Sekuler and P. J. Bennett, “Generalized common fate: grouping by common luminance changes,” Psychol. Sci. 12, 437–444 (2001).
  22. P. J. Hancock, L. Walton, G. Mitchell, Y. Plenderleith, and W. A. Phillips, “Segregation by onset asynchrony,” J. Vis. 8(7):21, 1–21 (2008).
    [CrossRef]
  23. M. Usher and N. Donnelly, “Visual synchrony affects binding and segmentation in perception,” Nature 394, 179–182 (1998).
    [CrossRef]
  24. M. L. Hines and N. T. Carnevale, “The NEURON simulation environment,” Neural Comput. 9, 1179–1209 (1997).
    [CrossRef]
  25. V. Bringuier, F. Chavane, L. Glaeser, and Y. Fregnac, “Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons,” Science 283, 695–699 (1999).
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  26. P. Girard, J. M. Hupe, and J. Bullier, “Feedforward and feedback connections between areas V1 and V2 of the Monkey have the similar rapid conduction velocities,” J. Neurophysiol. 85, 1328–1331 (2001).
  27. L. G. Nowak, M. H. Munk, P. Girard, and J. Bullier, “Visual latencies in areas V1 and V2 of the macaque monkey,” Vis. Neurosci. 12, 371–384 (1995).
    [CrossRef]
  28. A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,” J. Physiol. 117, 500–544 (1952).
  29. W. Gerstner and W. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity (Cambridge University, 2002).
  30. K. A. Archie and B. W. Mel, “A model for intradendritic computation of binocular disparity,” Nat. Neurosci. 3, 54–63 (2000).
    [CrossRef]
  31. H. E. Jones, W. Wang, and A. M. Sillito, “Spatial organization and magnitude of orientation contrast interactions in primate V1,” J. Neurophysiol. 88, 2796–2808 (2002).
    [CrossRef]
  32. T. S. Meese, R. J. Summers, D. J. Holmes, and S. A. Wallis, “Contextual modulation involves suppression and facilitation from the center and the surround,” J. Vis. 7(4):7, 1–21 (2007).
    [CrossRef]
  33. M. Carandini, D. J. Heeger, and J. A. Movshon, “Linearity and normalization in simple cells of the macaque primary visual cortex,” J. Neurosci. 17, 8621–8644 (1997).
  34. G. Deco and E. T. Rolls, “A neurodynamical cortical model of visual attention and invariant object recognition,” Vision Res. 44, 621–642 (2004).
    [CrossRef]
  35. T. Poggio and F. Girosi, “Regularization algorithm for learning that are equivalent to multilayer networks,” Science 247, 978–982 (1990).
    [CrossRef]
  36. L. G. Nowak and J. Builler, “The timing of information transfer in the visual system,” in Cerebral Cortex, K. S. Rockland, J. H. Kaas, and A. Peters, eds. (Plenum, 1997), Vol. 12, pp. 205–233.
  37. E. T. Rolls and G. Deco, Computational Neuroscience of Vision (Oxford University, 2002).
  38. C. C. Fowlkes, D. R. Martin, and J. Malik, “Local figure–ground cues are valid for natural images,” J. Vis. 7(8):2, 1–9 (2007).
    [CrossRef]
  39. L. Zhaoping, “V1 mechanisms and some figure–ground and border effects,” J. Physiol. Paris 97, 503–515 (2003).
  40. A. Cowey and E. T. Rolls, “Human cortical magnification factor and its relation to visual acuity,” Exp. Brain Res. 21, 447–454 (1974).
    [CrossRef]
  41. E. Craft, H. Schutze, E. Niebur, and R. von der Heydt, “A neural model of figure-ground organization,” J. Neurophysiol. 97, 4310–4326 (2007).
    [CrossRef]
  42. O. W. Layton, E. Mingolla, and A. Yazdanbakhsh, “Dynamic coding of border-ownership in visual cortex,” J. Vis. 12(13):8, 1–21 (2012).
    [CrossRef]
  43. L. F. Abbott, “A network of oscillators,” J. Phys. A 23, 3835–3859 (1990).
    [CrossRef]
  44. X. J. Wang and G. Buzsaki, “Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model,” J. Neurosci. 16, 6402–6413 (1996).
  45. A. Martin and R. von der Heydt, “Contour binding and selective attention increase coherence between neural signals in visual cortex,” Perception 40, 49 (2011).
  46. A. Martin and R. von der Heydt, “Binding and selective attention increase coherence between distant sites in early visual cortex,” J. Vis. 11(11), 179 (2011).
    [CrossRef]

2013 (1)

M. D. Lescroart and I. Biederman, “Cortical representation of medial axis structure,” Cereb. Cortex 23, 629–637 (2013).
[CrossRef]

2012 (2)

C. C. Hung, E. T. Carlson, and C. E. Connor, “Medial axis shape coding in macaque inferotemporal cortex,” Neuron 74, 1099–1113 (2012).
[CrossRef]

O. W. Layton, E. Mingolla, and A. Yazdanbakhsh, “Dynamic coding of border-ownership in visual cortex,” J. Vis. 12(13):8, 1–21 (2012).
[CrossRef]

2011 (2)

A. Martin and R. von der Heydt, “Contour binding and selective attention increase coherence between neural signals in visual cortex,” Perception 40, 49 (2011).

A. Martin and R. von der Heydt, “Binding and selective attention increase coherence between distant sites in early visual cortex,” J. Vis. 11(11), 179 (2011).
[CrossRef]

2010 (2)

V. Froyen, J. Feldman, and M. Singh, “A bayesian framework for figure–ground interpretation,” Adv. Neural Inf. Process Syst. 23, 631–639 (2010).

N. R. Zhang and R. von der Heydt, “Analysis of the context integration mechanisms underlying figure-ground organization in the visual cortex,” J. Neurosci. 30, 6482–6496 (2010).
[CrossRef]

2009 (2)

S. H. Kim and J. Feldman, “Globally inconsistent figure/ground relations induced by a negative part,” J. Vis. 9(10):8, 1–13 (2009).

Y. Hatori and K. Sakai, “Robust detection of medial-axis by onset synchronization of border-ownership selective cells and shape reconstruction from its medial-axis,” Lect. Notes Comput. Sci. 5506, 301–309 (2009).
[CrossRef]

2008 (4)

Y. Dong, S. Mihalas, F. Qiu, R. von der Heydt, and E. Niebur, “Synchrony and the binding problem in macaque visual cortex,” J. Vis. 8(7):30, 1–16 (2008).

X. Huang and M. A. Paradiso, “V1 response timing and surface filling-in,” J. Neurophysiol. 100, 539–547 (2008).
[CrossRef]

Z. Zhou, M. R. Bernard, and A. B. Bonds, “Deconstruction of spatial integrity in visual stimulus detected by modulation of synchronized activity in cat visual cortex,” J. Neurosci. 28, 3759–3768 (2008).
[CrossRef]

P. J. Hancock, L. Walton, G. Mitchell, Y. Plenderleith, and W. A. Phillips, “Segregation by onset asynchrony,” J. Vis. 8(7):21, 1–21 (2008).
[CrossRef]

2007 (3)

T. S. Meese, R. J. Summers, D. J. Holmes, and S. A. Wallis, “Contextual modulation involves suppression and facilitation from the center and the surround,” J. Vis. 7(4):7, 1–21 (2007).
[CrossRef]

C. C. Fowlkes, D. R. Martin, and J. Malik, “Local figure–ground cues are valid for natural images,” J. Vis. 7(8):2, 1–9 (2007).
[CrossRef]

E. Craft, H. Schutze, E. Niebur, and R. von der Heydt, “A neural model of figure-ground organization,” J. Neurophysiol. 97, 4310–4326 (2007).
[CrossRef]

2006 (1)

K. Sakai and H. Nishimura, “Surrounding suppression and facilitation in the determination of border ownership,” J. Cogn. Neurosci. 18, 562–579 (2006).

2005 (1)

J. M. Samonds and A. B. Bonds, “Gamma oscillation maintains stimulus structure-dependent synchronization in cat visual cortex,” J. Neurophysiol. 93, 223–236 (2005).
[CrossRef]

2004 (1)

G. Deco and E. T. Rolls, “A neurodynamical cortical model of visual attention and invariant object recognition,” Vision Res. 44, 621–642 (2004).
[CrossRef]

2003 (2)

L. Zhaoping, “V1 mechanisms and some figure–ground and border effects,” J. Physiol. Paris 97, 503–515 (2003).

B. B. Kimia, “On the role of medial geometry in human vision,” J. Physiol. Paris 97, 155–190 (2003).

2002 (2)

H. E. Jones, W. Wang, and A. M. Sillito, “Spatial organization and magnitude of orientation contrast interactions in primate V1,” J. Neurophysiol. 88, 2796–2808 (2002).
[CrossRef]

A. Angelucci, J. B. Levitt, E. J. S. Walton, J. M. Hupe, J. Bullier, and J. S. Lund, “Circuits for local and global signal integration in primary visual cortex,” J. Neurosci. 22, 8633–8646 (2002).

2001 (2)

A. B. Sekuler and P. J. Bennett, “Generalized common fate: grouping by common luminance changes,” Psychol. Sci. 12, 437–444 (2001).

P. Girard, J. M. Hupe, and J. Bullier, “Feedforward and feedback connections between areas V1 and V2 of the Monkey have the similar rapid conduction velocities,” J. Neurophysiol. 85, 1328–1331 (2001).

2000 (2)

K. A. Archie and B. W. Mel, “A model for intradendritic computation of binocular disparity,” Nat. Neurosci. 3, 54–63 (2000).
[CrossRef]

H. Zhou, H. S. Friedman, and R. von der Heydt, “Coding of border ownership in monkey visual cortex,” J. Neurosci. 20, 6594–6611 (2000).

1999 (1)

V. Bringuier, F. Chavane, L. Glaeser, and Y. Fregnac, “Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons,” Science 283, 695–699 (1999).
[CrossRef]

1998 (3)

M. Usher and N. Donnelly, “Visual synchrony affects binding and segmentation in perception,” Nature 394, 179–182 (1998).
[CrossRef]

T. S. Lee, D. Mumford, R. Romero, and V. A. F. Lamme, “The role of the primary visual cortex in higher level vision,” Vis. Res. 38, 2429–2454 (1998).
[CrossRef]

I. Kovacs, A. Feher, and B. Julesz, “Medial-point description of shape: a representation for action coding and its psychophysical correlates,” Vis. Res. 38, 2323–2333 (1998).
[CrossRef]

1997 (2)

M. L. Hines and N. T. Carnevale, “The NEURON simulation environment,” Neural Comput. 9, 1179–1209 (1997).
[CrossRef]

M. Carandini, D. J. Heeger, and J. A. Movshon, “Linearity and normalization in simple cells of the macaque primary visual cortex,” J. Neurosci. 17, 8621–8644 (1997).

1996 (2)

K. Zipser, V. A. F. Lamme, and P. H. Schiller, “Contextual modulation in primary visual cortex,” J. Neurosci. 16, 7376–7389 (1996).

X. J. Wang and G. Buzsaki, “Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model,” J. Neurosci. 16, 6402–6413 (1996).

1995 (2)

V. A. F. Lamme, “The neurophysiology of figure-ground segregation in primary visual cortex,” J. Neurosci. 15, 1605–1615 (1995).

L. G. Nowak, M. H. Munk, P. Girard, and J. Bullier, “Visual latencies in areas V1 and V2 of the macaque monkey,” Vis. Neurosci. 12, 371–384 (1995).
[CrossRef]

1994 (1)

I. Kovacs and B. Julesz, “Perceptual sensitivity maps within globally defined visual shapes,” Nature 370, 644–646 (1994).
[CrossRef]

1990 (2)

T. Poggio and F. Girosi, “Regularization algorithm for learning that are equivalent to multilayer networks,” Science 247, 978–982 (1990).
[CrossRef]

L. F. Abbott, “A network of oscillators,” J. Phys. A 23, 3835–3859 (1990).
[CrossRef]

1978 (1)

D. Marr and H. K. Nishihara, “Representation and recognition of the spatial organization of three-dimensional shapes,” Proc. R. Soc. Lond. B, Biol. Sci. 200, 269–294 (1978).
[CrossRef]

1974 (1)

A. Cowey and E. T. Rolls, “Human cortical magnification factor and its relation to visual acuity,” Exp. Brain Res. 21, 447–454 (1974).
[CrossRef]

1952 (1)

A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,” J. Physiol. 117, 500–544 (1952).

Abbott, L. F.

L. F. Abbott, “A network of oscillators,” J. Phys. A 23, 3835–3859 (1990).
[CrossRef]

Angelucci, A.

A. Angelucci, J. B. Levitt, E. J. S. Walton, J. M. Hupe, J. Bullier, and J. S. Lund, “Circuits for local and global signal integration in primary visual cortex,” J. Neurosci. 22, 8633–8646 (2002).

Archie, K. A.

K. A. Archie and B. W. Mel, “A model for intradendritic computation of binocular disparity,” Nat. Neurosci. 3, 54–63 (2000).
[CrossRef]

Bennett, P. J.

A. B. Sekuler and P. J. Bennett, “Generalized common fate: grouping by common luminance changes,” Psychol. Sci. 12, 437–444 (2001).

Bernard, M. R.

Z. Zhou, M. R. Bernard, and A. B. Bonds, “Deconstruction of spatial integrity in visual stimulus detected by modulation of synchronized activity in cat visual cortex,” J. Neurosci. 28, 3759–3768 (2008).
[CrossRef]

Biederman, I.

M. D. Lescroart and I. Biederman, “Cortical representation of medial axis structure,” Cereb. Cortex 23, 629–637 (2013).
[CrossRef]

Bonds, A. B.

Z. Zhou, M. R. Bernard, and A. B. Bonds, “Deconstruction of spatial integrity in visual stimulus detected by modulation of synchronized activity in cat visual cortex,” J. Neurosci. 28, 3759–3768 (2008).
[CrossRef]

J. M. Samonds and A. B. Bonds, “Gamma oscillation maintains stimulus structure-dependent synchronization in cat visual cortex,” J. Neurophysiol. 93, 223–236 (2005).
[CrossRef]

Bringuier, V.

V. Bringuier, F. Chavane, L. Glaeser, and Y. Fregnac, “Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons,” Science 283, 695–699 (1999).
[CrossRef]

Builler, J.

L. G. Nowak and J. Builler, “The timing of information transfer in the visual system,” in Cerebral Cortex, K. S. Rockland, J. H. Kaas, and A. Peters, eds. (Plenum, 1997), Vol. 12, pp. 205–233.

Bullier, J.

A. Angelucci, J. B. Levitt, E. J. S. Walton, J. M. Hupe, J. Bullier, and J. S. Lund, “Circuits for local and global signal integration in primary visual cortex,” J. Neurosci. 22, 8633–8646 (2002).

P. Girard, J. M. Hupe, and J. Bullier, “Feedforward and feedback connections between areas V1 and V2 of the Monkey have the similar rapid conduction velocities,” J. Neurophysiol. 85, 1328–1331 (2001).

L. G. Nowak, M. H. Munk, P. Girard, and J. Bullier, “Visual latencies in areas V1 and V2 of the macaque monkey,” Vis. Neurosci. 12, 371–384 (1995).
[CrossRef]

Buzsaki, G.

X. J. Wang and G. Buzsaki, “Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model,” J. Neurosci. 16, 6402–6413 (1996).

Carandini, M.

M. Carandini, D. J. Heeger, and J. A. Movshon, “Linearity and normalization in simple cells of the macaque primary visual cortex,” J. Neurosci. 17, 8621–8644 (1997).

Carlson, E. T.

C. C. Hung, E. T. Carlson, and C. E. Connor, “Medial axis shape coding in macaque inferotemporal cortex,” Neuron 74, 1099–1113 (2012).
[CrossRef]

Carnevale, N. T.

M. L. Hines and N. T. Carnevale, “The NEURON simulation environment,” Neural Comput. 9, 1179–1209 (1997).
[CrossRef]

Chavane, F.

V. Bringuier, F. Chavane, L. Glaeser, and Y. Fregnac, “Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons,” Science 283, 695–699 (1999).
[CrossRef]

Connor, C. E.

C. C. Hung, E. T. Carlson, and C. E. Connor, “Medial axis shape coding in macaque inferotemporal cortex,” Neuron 74, 1099–1113 (2012).
[CrossRef]

Cowey, A.

A. Cowey and E. T. Rolls, “Human cortical magnification factor and its relation to visual acuity,” Exp. Brain Res. 21, 447–454 (1974).
[CrossRef]

Craft, E.

E. Craft, H. Schutze, E. Niebur, and R. von der Heydt, “A neural model of figure-ground organization,” J. Neurophysiol. 97, 4310–4326 (2007).
[CrossRef]

Deco, G.

G. Deco and E. T. Rolls, “A neurodynamical cortical model of visual attention and invariant object recognition,” Vision Res. 44, 621–642 (2004).
[CrossRef]

E. T. Rolls and G. Deco, Computational Neuroscience of Vision (Oxford University, 2002).

Dong, Y.

Y. Dong, S. Mihalas, F. Qiu, R. von der Heydt, and E. Niebur, “Synchrony and the binding problem in macaque visual cortex,” J. Vis. 8(7):30, 1–16 (2008).

Donnelly, N.

M. Usher and N. Donnelly, “Visual synchrony affects binding and segmentation in perception,” Nature 394, 179–182 (1998).
[CrossRef]

Feher, A.

I. Kovacs, A. Feher, and B. Julesz, “Medial-point description of shape: a representation for action coding and its psychophysical correlates,” Vis. Res. 38, 2323–2333 (1998).
[CrossRef]

Feldman, J.

V. Froyen, J. Feldman, and M. Singh, “A bayesian framework for figure–ground interpretation,” Adv. Neural Inf. Process Syst. 23, 631–639 (2010).

S. H. Kim and J. Feldman, “Globally inconsistent figure/ground relations induced by a negative part,” J. Vis. 9(10):8, 1–13 (2009).

Fowlkes, C. C.

C. C. Fowlkes, D. R. Martin, and J. Malik, “Local figure–ground cues are valid for natural images,” J. Vis. 7(8):2, 1–9 (2007).
[CrossRef]

Fregnac, Y.

V. Bringuier, F. Chavane, L. Glaeser, and Y. Fregnac, “Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons,” Science 283, 695–699 (1999).
[CrossRef]

Friedman, H. S.

H. Zhou, H. S. Friedman, and R. von der Heydt, “Coding of border ownership in monkey visual cortex,” J. Neurosci. 20, 6594–6611 (2000).

Froyen, V.

V. Froyen, J. Feldman, and M. Singh, “A bayesian framework for figure–ground interpretation,” Adv. Neural Inf. Process Syst. 23, 631–639 (2010).

Gerstner, W.

W. Gerstner and W. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity (Cambridge University, 2002).

Girard, P.

P. Girard, J. M. Hupe, and J. Bullier, “Feedforward and feedback connections between areas V1 and V2 of the Monkey have the similar rapid conduction velocities,” J. Neurophysiol. 85, 1328–1331 (2001).

L. G. Nowak, M. H. Munk, P. Girard, and J. Bullier, “Visual latencies in areas V1 and V2 of the macaque monkey,” Vis. Neurosci. 12, 371–384 (1995).
[CrossRef]

Girosi, F.

T. Poggio and F. Girosi, “Regularization algorithm for learning that are equivalent to multilayer networks,” Science 247, 978–982 (1990).
[CrossRef]

Glaeser, L.

V. Bringuier, F. Chavane, L. Glaeser, and Y. Fregnac, “Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons,” Science 283, 695–699 (1999).
[CrossRef]

Hancock, P. J.

P. J. Hancock, L. Walton, G. Mitchell, Y. Plenderleith, and W. A. Phillips, “Segregation by onset asynchrony,” J. Vis. 8(7):21, 1–21 (2008).
[CrossRef]

Hatori, Y.

Y. Hatori and K. Sakai, “Robust detection of medial-axis by onset synchronization of border-ownership selective cells and shape reconstruction from its medial-axis,” Lect. Notes Comput. Sci. 5506, 301–309 (2009).
[CrossRef]

Heeger, D. J.

M. Carandini, D. J. Heeger, and J. A. Movshon, “Linearity and normalization in simple cells of the macaque primary visual cortex,” J. Neurosci. 17, 8621–8644 (1997).

Hines, M. L.

M. L. Hines and N. T. Carnevale, “The NEURON simulation environment,” Neural Comput. 9, 1179–1209 (1997).
[CrossRef]

Hodgkin, A. L.

A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,” J. Physiol. 117, 500–544 (1952).

Holmes, D. J.

T. S. Meese, R. J. Summers, D. J. Holmes, and S. A. Wallis, “Contextual modulation involves suppression and facilitation from the center and the surround,” J. Vis. 7(4):7, 1–21 (2007).
[CrossRef]

Huang, X.

X. Huang and M. A. Paradiso, “V1 response timing and surface filling-in,” J. Neurophysiol. 100, 539–547 (2008).
[CrossRef]

Hung, C. C.

C. C. Hung, E. T. Carlson, and C. E. Connor, “Medial axis shape coding in macaque inferotemporal cortex,” Neuron 74, 1099–1113 (2012).
[CrossRef]

Hupe, J. M.

A. Angelucci, J. B. Levitt, E. J. S. Walton, J. M. Hupe, J. Bullier, and J. S. Lund, “Circuits for local and global signal integration in primary visual cortex,” J. Neurosci. 22, 8633–8646 (2002).

P. Girard, J. M. Hupe, and J. Bullier, “Feedforward and feedback connections between areas V1 and V2 of the Monkey have the similar rapid conduction velocities,” J. Neurophysiol. 85, 1328–1331 (2001).

Huxley, A. F.

A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,” J. Physiol. 117, 500–544 (1952).

Jones, H. E.

H. E. Jones, W. Wang, and A. M. Sillito, “Spatial organization and magnitude of orientation contrast interactions in primate V1,” J. Neurophysiol. 88, 2796–2808 (2002).
[CrossRef]

Julesz, B.

I. Kovacs, A. Feher, and B. Julesz, “Medial-point description of shape: a representation for action coding and its psychophysical correlates,” Vis. Res. 38, 2323–2333 (1998).
[CrossRef]

I. Kovacs and B. Julesz, “Perceptual sensitivity maps within globally defined visual shapes,” Nature 370, 644–646 (1994).
[CrossRef]

Kim, S. H.

S. H. Kim and J. Feldman, “Globally inconsistent figure/ground relations induced by a negative part,” J. Vis. 9(10):8, 1–13 (2009).

Kimia, B. B.

B. B. Kimia, “On the role of medial geometry in human vision,” J. Physiol. Paris 97, 155–190 (2003).

Kistler, W.

W. Gerstner and W. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity (Cambridge University, 2002).

Kovacs, I.

I. Kovacs, A. Feher, and B. Julesz, “Medial-point description of shape: a representation for action coding and its psychophysical correlates,” Vis. Res. 38, 2323–2333 (1998).
[CrossRef]

I. Kovacs and B. Julesz, “Perceptual sensitivity maps within globally defined visual shapes,” Nature 370, 644–646 (1994).
[CrossRef]

Lamme, V. A. F.

T. S. Lee, D. Mumford, R. Romero, and V. A. F. Lamme, “The role of the primary visual cortex in higher level vision,” Vis. Res. 38, 2429–2454 (1998).
[CrossRef]

K. Zipser, V. A. F. Lamme, and P. H. Schiller, “Contextual modulation in primary visual cortex,” J. Neurosci. 16, 7376–7389 (1996).

V. A. F. Lamme, “The neurophysiology of figure-ground segregation in primary visual cortex,” J. Neurosci. 15, 1605–1615 (1995).

Layton, O. W.

O. W. Layton, E. Mingolla, and A. Yazdanbakhsh, “Dynamic coding of border-ownership in visual cortex,” J. Vis. 12(13):8, 1–21 (2012).
[CrossRef]

Lee, T. S.

T. S. Lee, D. Mumford, R. Romero, and V. A. F. Lamme, “The role of the primary visual cortex in higher level vision,” Vis. Res. 38, 2429–2454 (1998).
[CrossRef]

Lescroart, M. D.

M. D. Lescroart and I. Biederman, “Cortical representation of medial axis structure,” Cereb. Cortex 23, 629–637 (2013).
[CrossRef]

Levitt, J. B.

A. Angelucci, J. B. Levitt, E. J. S. Walton, J. M. Hupe, J. Bullier, and J. S. Lund, “Circuits for local and global signal integration in primary visual cortex,” J. Neurosci. 22, 8633–8646 (2002).

Lund, J. S.

A. Angelucci, J. B. Levitt, E. J. S. Walton, J. M. Hupe, J. Bullier, and J. S. Lund, “Circuits for local and global signal integration in primary visual cortex,” J. Neurosci. 22, 8633–8646 (2002).

Malik, J.

C. C. Fowlkes, D. R. Martin, and J. Malik, “Local figure–ground cues are valid for natural images,” J. Vis. 7(8):2, 1–9 (2007).
[CrossRef]

Marr, D.

D. Marr and H. K. Nishihara, “Representation and recognition of the spatial organization of three-dimensional shapes,” Proc. R. Soc. Lond. B, Biol. Sci. 200, 269–294 (1978).
[CrossRef]

Martin, A.

A. Martin and R. von der Heydt, “Contour binding and selective attention increase coherence between neural signals in visual cortex,” Perception 40, 49 (2011).

A. Martin and R. von der Heydt, “Binding and selective attention increase coherence between distant sites in early visual cortex,” J. Vis. 11(11), 179 (2011).
[CrossRef]

Martin, D. R.

C. C. Fowlkes, D. R. Martin, and J. Malik, “Local figure–ground cues are valid for natural images,” J. Vis. 7(8):2, 1–9 (2007).
[CrossRef]

Meese, T. S.

T. S. Meese, R. J. Summers, D. J. Holmes, and S. A. Wallis, “Contextual modulation involves suppression and facilitation from the center and the surround,” J. Vis. 7(4):7, 1–21 (2007).
[CrossRef]

Mel, B. W.

K. A. Archie and B. W. Mel, “A model for intradendritic computation of binocular disparity,” Nat. Neurosci. 3, 54–63 (2000).
[CrossRef]

Mihalas, S.

Y. Dong, S. Mihalas, F. Qiu, R. von der Heydt, and E. Niebur, “Synchrony and the binding problem in macaque visual cortex,” J. Vis. 8(7):30, 1–16 (2008).

Mingolla, E.

O. W. Layton, E. Mingolla, and A. Yazdanbakhsh, “Dynamic coding of border-ownership in visual cortex,” J. Vis. 12(13):8, 1–21 (2012).
[CrossRef]

Mitchell, G.

P. J. Hancock, L. Walton, G. Mitchell, Y. Plenderleith, and W. A. Phillips, “Segregation by onset asynchrony,” J. Vis. 8(7):21, 1–21 (2008).
[CrossRef]

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M. Carandini, D. J. Heeger, and J. A. Movshon, “Linearity and normalization in simple cells of the macaque primary visual cortex,” J. Neurosci. 17, 8621–8644 (1997).

Mumford, D.

T. S. Lee, D. Mumford, R. Romero, and V. A. F. Lamme, “The role of the primary visual cortex in higher level vision,” Vis. Res. 38, 2429–2454 (1998).
[CrossRef]

Munk, M. H.

L. G. Nowak, M. H. Munk, P. Girard, and J. Bullier, “Visual latencies in areas V1 and V2 of the macaque monkey,” Vis. Neurosci. 12, 371–384 (1995).
[CrossRef]

Niebur, E.

Y. Dong, S. Mihalas, F. Qiu, R. von der Heydt, and E. Niebur, “Synchrony and the binding problem in macaque visual cortex,” J. Vis. 8(7):30, 1–16 (2008).

E. Craft, H. Schutze, E. Niebur, and R. von der Heydt, “A neural model of figure-ground organization,” J. Neurophysiol. 97, 4310–4326 (2007).
[CrossRef]

Nishihara, H. K.

D. Marr and H. K. Nishihara, “Representation and recognition of the spatial organization of three-dimensional shapes,” Proc. R. Soc. Lond. B, Biol. Sci. 200, 269–294 (1978).
[CrossRef]

Nishimura, H.

K. Sakai and H. Nishimura, “Surrounding suppression and facilitation in the determination of border ownership,” J. Cogn. Neurosci. 18, 562–579 (2006).

Nowak, L. G.

L. G. Nowak, M. H. Munk, P. Girard, and J. Bullier, “Visual latencies in areas V1 and V2 of the macaque monkey,” Vis. Neurosci. 12, 371–384 (1995).
[CrossRef]

L. G. Nowak and J. Builler, “The timing of information transfer in the visual system,” in Cerebral Cortex, K. S. Rockland, J. H. Kaas, and A. Peters, eds. (Plenum, 1997), Vol. 12, pp. 205–233.

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X. Huang and M. A. Paradiso, “V1 response timing and surface filling-in,” J. Neurophysiol. 100, 539–547 (2008).
[CrossRef]

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P. J. Hancock, L. Walton, G. Mitchell, Y. Plenderleith, and W. A. Phillips, “Segregation by onset asynchrony,” J. Vis. 8(7):21, 1–21 (2008).
[CrossRef]

Plenderleith, Y.

P. J. Hancock, L. Walton, G. Mitchell, Y. Plenderleith, and W. A. Phillips, “Segregation by onset asynchrony,” J. Vis. 8(7):21, 1–21 (2008).
[CrossRef]

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T. Poggio and F. Girosi, “Regularization algorithm for learning that are equivalent to multilayer networks,” Science 247, 978–982 (1990).
[CrossRef]

Qiu, F.

Y. Dong, S. Mihalas, F. Qiu, R. von der Heydt, and E. Niebur, “Synchrony and the binding problem in macaque visual cortex,” J. Vis. 8(7):30, 1–16 (2008).

Rolls, E. T.

G. Deco and E. T. Rolls, “A neurodynamical cortical model of visual attention and invariant object recognition,” Vision Res. 44, 621–642 (2004).
[CrossRef]

A. Cowey and E. T. Rolls, “Human cortical magnification factor and its relation to visual acuity,” Exp. Brain Res. 21, 447–454 (1974).
[CrossRef]

E. T. Rolls and G. Deco, Computational Neuroscience of Vision (Oxford University, 2002).

Romero, R.

T. S. Lee, D. Mumford, R. Romero, and V. A. F. Lamme, “The role of the primary visual cortex in higher level vision,” Vis. Res. 38, 2429–2454 (1998).
[CrossRef]

Sakai, K.

Y. Hatori and K. Sakai, “Robust detection of medial-axis by onset synchronization of border-ownership selective cells and shape reconstruction from its medial-axis,” Lect. Notes Comput. Sci. 5506, 301–309 (2009).
[CrossRef]

K. Sakai and H. Nishimura, “Surrounding suppression and facilitation in the determination of border ownership,” J. Cogn. Neurosci. 18, 562–579 (2006).

Samonds, J. M.

J. M. Samonds and A. B. Bonds, “Gamma oscillation maintains stimulus structure-dependent synchronization in cat visual cortex,” J. Neurophysiol. 93, 223–236 (2005).
[CrossRef]

Schiller, P. H.

K. Zipser, V. A. F. Lamme, and P. H. Schiller, “Contextual modulation in primary visual cortex,” J. Neurosci. 16, 7376–7389 (1996).

Schutze, H.

E. Craft, H. Schutze, E. Niebur, and R. von der Heydt, “A neural model of figure-ground organization,” J. Neurophysiol. 97, 4310–4326 (2007).
[CrossRef]

Sekuler, A. B.

A. B. Sekuler and P. J. Bennett, “Generalized common fate: grouping by common luminance changes,” Psychol. Sci. 12, 437–444 (2001).

Sillito, A. M.

H. E. Jones, W. Wang, and A. M. Sillito, “Spatial organization and magnitude of orientation contrast interactions in primate V1,” J. Neurophysiol. 88, 2796–2808 (2002).
[CrossRef]

Singh, M.

V. Froyen, J. Feldman, and M. Singh, “A bayesian framework for figure–ground interpretation,” Adv. Neural Inf. Process Syst. 23, 631–639 (2010).

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T. S. Meese, R. J. Summers, D. J. Holmes, and S. A. Wallis, “Contextual modulation involves suppression and facilitation from the center and the surround,” J. Vis. 7(4):7, 1–21 (2007).
[CrossRef]

Usher, M.

M. Usher and N. Donnelly, “Visual synchrony affects binding and segmentation in perception,” Nature 394, 179–182 (1998).
[CrossRef]

von der Heydt, R.

A. Martin and R. von der Heydt, “Binding and selective attention increase coherence between distant sites in early visual cortex,” J. Vis. 11(11), 179 (2011).
[CrossRef]

A. Martin and R. von der Heydt, “Contour binding and selective attention increase coherence between neural signals in visual cortex,” Perception 40, 49 (2011).

N. R. Zhang and R. von der Heydt, “Analysis of the context integration mechanisms underlying figure-ground organization in the visual cortex,” J. Neurosci. 30, 6482–6496 (2010).
[CrossRef]

Y. Dong, S. Mihalas, F. Qiu, R. von der Heydt, and E. Niebur, “Synchrony and the binding problem in macaque visual cortex,” J. Vis. 8(7):30, 1–16 (2008).

E. Craft, H. Schutze, E. Niebur, and R. von der Heydt, “A neural model of figure-ground organization,” J. Neurophysiol. 97, 4310–4326 (2007).
[CrossRef]

H. Zhou, H. S. Friedman, and R. von der Heydt, “Coding of border ownership in monkey visual cortex,” J. Neurosci. 20, 6594–6611 (2000).

Wallis, S. A.

T. S. Meese, R. J. Summers, D. J. Holmes, and S. A. Wallis, “Contextual modulation involves suppression and facilitation from the center and the surround,” J. Vis. 7(4):7, 1–21 (2007).
[CrossRef]

Walton, E. J. S.

A. Angelucci, J. B. Levitt, E. J. S. Walton, J. M. Hupe, J. Bullier, and J. S. Lund, “Circuits for local and global signal integration in primary visual cortex,” J. Neurosci. 22, 8633–8646 (2002).

Walton, L.

P. J. Hancock, L. Walton, G. Mitchell, Y. Plenderleith, and W. A. Phillips, “Segregation by onset asynchrony,” J. Vis. 8(7):21, 1–21 (2008).
[CrossRef]

Wang, W.

H. E. Jones, W. Wang, and A. M. Sillito, “Spatial organization and magnitude of orientation contrast interactions in primate V1,” J. Neurophysiol. 88, 2796–2808 (2002).
[CrossRef]

Wang, X. J.

X. J. Wang and G. Buzsaki, “Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model,” J. Neurosci. 16, 6402–6413 (1996).

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O. W. Layton, E. Mingolla, and A. Yazdanbakhsh, “Dynamic coding of border-ownership in visual cortex,” J. Vis. 12(13):8, 1–21 (2012).
[CrossRef]

Zhang, N. R.

N. R. Zhang and R. von der Heydt, “Analysis of the context integration mechanisms underlying figure-ground organization in the visual cortex,” J. Neurosci. 30, 6482–6496 (2010).
[CrossRef]

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L. Zhaoping, “V1 mechanisms and some figure–ground and border effects,” J. Physiol. Paris 97, 503–515 (2003).

Zhou, H.

H. Zhou, H. S. Friedman, and R. von der Heydt, “Coding of border ownership in monkey visual cortex,” J. Neurosci. 20, 6594–6611 (2000).

Zhou, Z.

Z. Zhou, M. R. Bernard, and A. B. Bonds, “Deconstruction of spatial integrity in visual stimulus detected by modulation of synchronized activity in cat visual cortex,” J. Neurosci. 28, 3759–3768 (2008).
[CrossRef]

Zipser, K.

K. Zipser, V. A. F. Lamme, and P. H. Schiller, “Contextual modulation in primary visual cortex,” J. Neurosci. 16, 7376–7389 (1996).

Adv. Neural Inf. Process Syst. (1)

V. Froyen, J. Feldman, and M. Singh, “A bayesian framework for figure–ground interpretation,” Adv. Neural Inf. Process Syst. 23, 631–639 (2010).

Cereb. Cortex (1)

M. D. Lescroart and I. Biederman, “Cortical representation of medial axis structure,” Cereb. Cortex 23, 629–637 (2013).
[CrossRef]

Exp. Brain Res. (1)

A. Cowey and E. T. Rolls, “Human cortical magnification factor and its relation to visual acuity,” Exp. Brain Res. 21, 447–454 (1974).
[CrossRef]

J. Cogn. Neurosci. (1)

K. Sakai and H. Nishimura, “Surrounding suppression and facilitation in the determination of border ownership,” J. Cogn. Neurosci. 18, 562–579 (2006).

J. Neurophysiol. (5)

X. Huang and M. A. Paradiso, “V1 response timing and surface filling-in,” J. Neurophysiol. 100, 539–547 (2008).
[CrossRef]

J. M. Samonds and A. B. Bonds, “Gamma oscillation maintains stimulus structure-dependent synchronization in cat visual cortex,” J. Neurophysiol. 93, 223–236 (2005).
[CrossRef]

P. Girard, J. M. Hupe, and J. Bullier, “Feedforward and feedback connections between areas V1 and V2 of the Monkey have the similar rapid conduction velocities,” J. Neurophysiol. 85, 1328–1331 (2001).

H. E. Jones, W. Wang, and A. M. Sillito, “Spatial organization and magnitude of orientation contrast interactions in primate V1,” J. Neurophysiol. 88, 2796–2808 (2002).
[CrossRef]

E. Craft, H. Schutze, E. Niebur, and R. von der Heydt, “A neural model of figure-ground organization,” J. Neurophysiol. 97, 4310–4326 (2007).
[CrossRef]

J. Neurosci. (8)

X. J. Wang and G. Buzsaki, “Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model,” J. Neurosci. 16, 6402–6413 (1996).

M. Carandini, D. J. Heeger, and J. A. Movshon, “Linearity and normalization in simple cells of the macaque primary visual cortex,” J. Neurosci. 17, 8621–8644 (1997).

Z. Zhou, M. R. Bernard, and A. B. Bonds, “Deconstruction of spatial integrity in visual stimulus detected by modulation of synchronized activity in cat visual cortex,” J. Neurosci. 28, 3759–3768 (2008).
[CrossRef]

A. Angelucci, J. B. Levitt, E. J. S. Walton, J. M. Hupe, J. Bullier, and J. S. Lund, “Circuits for local and global signal integration in primary visual cortex,” J. Neurosci. 22, 8633–8646 (2002).

V. A. F. Lamme, “The neurophysiology of figure-ground segregation in primary visual cortex,” J. Neurosci. 15, 1605–1615 (1995).

K. Zipser, V. A. F. Lamme, and P. H. Schiller, “Contextual modulation in primary visual cortex,” J. Neurosci. 16, 7376–7389 (1996).

N. R. Zhang and R. von der Heydt, “Analysis of the context integration mechanisms underlying figure-ground organization in the visual cortex,” J. Neurosci. 30, 6482–6496 (2010).
[CrossRef]

H. Zhou, H. S. Friedman, and R. von der Heydt, “Coding of border ownership in monkey visual cortex,” J. Neurosci. 20, 6594–6611 (2000).

J. Phys. A (1)

L. F. Abbott, “A network of oscillators,” J. Phys. A 23, 3835–3859 (1990).
[CrossRef]

J. Physiol. (1)

A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,” J. Physiol. 117, 500–544 (1952).

J. Physiol. Paris (2)

B. B. Kimia, “On the role of medial geometry in human vision,” J. Physiol. Paris 97, 155–190 (2003).

L. Zhaoping, “V1 mechanisms and some figure–ground and border effects,” J. Physiol. Paris 97, 503–515 (2003).

J. Vis. (7)

C. C. Fowlkes, D. R. Martin, and J. Malik, “Local figure–ground cues are valid for natural images,” J. Vis. 7(8):2, 1–9 (2007).
[CrossRef]

O. W. Layton, E. Mingolla, and A. Yazdanbakhsh, “Dynamic coding of border-ownership in visual cortex,” J. Vis. 12(13):8, 1–21 (2012).
[CrossRef]

A. Martin and R. von der Heydt, “Binding and selective attention increase coherence between distant sites in early visual cortex,” J. Vis. 11(11), 179 (2011).
[CrossRef]

T. S. Meese, R. J. Summers, D. J. Holmes, and S. A. Wallis, “Contextual modulation involves suppression and facilitation from the center and the surround,” J. Vis. 7(4):7, 1–21 (2007).
[CrossRef]

Y. Dong, S. Mihalas, F. Qiu, R. von der Heydt, and E. Niebur, “Synchrony and the binding problem in macaque visual cortex,” J. Vis. 8(7):30, 1–16 (2008).

P. J. Hancock, L. Walton, G. Mitchell, Y. Plenderleith, and W. A. Phillips, “Segregation by onset asynchrony,” J. Vis. 8(7):21, 1–21 (2008).
[CrossRef]

S. H. Kim and J. Feldman, “Globally inconsistent figure/ground relations induced by a negative part,” J. Vis. 9(10):8, 1–13 (2009).

Lect. Notes Comput. Sci. (1)

Y. Hatori and K. Sakai, “Robust detection of medial-axis by onset synchronization of border-ownership selective cells and shape reconstruction from its medial-axis,” Lect. Notes Comput. Sci. 5506, 301–309 (2009).
[CrossRef]

Nat. Neurosci. (1)

K. A. Archie and B. W. Mel, “A model for intradendritic computation of binocular disparity,” Nat. Neurosci. 3, 54–63 (2000).
[CrossRef]

Nature (2)

M. Usher and N. Donnelly, “Visual synchrony affects binding and segmentation in perception,” Nature 394, 179–182 (1998).
[CrossRef]

I. Kovacs and B. Julesz, “Perceptual sensitivity maps within globally defined visual shapes,” Nature 370, 644–646 (1994).
[CrossRef]

Neural Comput. (1)

M. L. Hines and N. T. Carnevale, “The NEURON simulation environment,” Neural Comput. 9, 1179–1209 (1997).
[CrossRef]

Neuron (1)

C. C. Hung, E. T. Carlson, and C. E. Connor, “Medial axis shape coding in macaque inferotemporal cortex,” Neuron 74, 1099–1113 (2012).
[CrossRef]

Perception (1)

A. Martin and R. von der Heydt, “Contour binding and selective attention increase coherence between neural signals in visual cortex,” Perception 40, 49 (2011).

Proc. R. Soc. Lond. B, Biol. Sci. (1)

D. Marr and H. K. Nishihara, “Representation and recognition of the spatial organization of three-dimensional shapes,” Proc. R. Soc. Lond. B, Biol. Sci. 200, 269–294 (1978).
[CrossRef]

Psychol. Sci. (1)

A. B. Sekuler and P. J. Bennett, “Generalized common fate: grouping by common luminance changes,” Psychol. Sci. 12, 437–444 (2001).

Science (2)

V. Bringuier, F. Chavane, L. Glaeser, and Y. Fregnac, “Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons,” Science 283, 695–699 (1999).
[CrossRef]

T. Poggio and F. Girosi, “Regularization algorithm for learning that are equivalent to multilayer networks,” Science 247, 978–982 (1990).
[CrossRef]

Vis. Neurosci. (1)

L. G. Nowak, M. H. Munk, P. Girard, and J. Bullier, “Visual latencies in areas V1 and V2 of the macaque monkey,” Vis. Neurosci. 12, 371–384 (1995).
[CrossRef]

Vis. Res. (2)

I. Kovacs, A. Feher, and B. Julesz, “Medial-point description of shape: a representation for action coding and its psychophysical correlates,” Vis. Res. 38, 2323–2333 (1998).
[CrossRef]

T. S. Lee, D. Mumford, R. Romero, and V. A. F. Lamme, “The role of the primary visual cortex in higher level vision,” Vis. Res. 38, 2429–2454 (1998).
[CrossRef]

Vision Res. (1)

G. Deco and E. T. Rolls, “A neurodynamical cortical model of visual attention and invariant object recognition,” Vision Res. 44, 621–642 (2004).
[CrossRef]

Other (3)

W. Gerstner and W. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity (Cambridge University, 2002).

L. G. Nowak and J. Builler, “The timing of information transfer in the visual system,” in Cerebral Cortex, K. S. Rockland, J. H. Kaas, and A. Peters, eds. (Plenum, 1997), Vol. 12, pp. 205–233.

E. T. Rolls and G. Deco, Computational Neuroscience of Vision (Oxford University, 2002).

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Figures (11)

Fig. 1.
Fig. 1.

Schematic illustrations of the model. (a) Model connectivity. The model has two layers (V1 and V2) and three types of connection (feedforward as indicated by FF; feedback, FB; and lateral, LA). The gray ellipses indicate the facilitatory (light gray) and suppressive (dark gray) surrounding regions of a BO-selective cell in V2, respectively, which are projected from V1 (see details in the “Determination of the DOF” section). (b) Processing flow of the model. The model is comprised of four distinct functional stages: (i) contrast detection, (ii) determination of the DOF, (iii) integration of DOF signals, and (iv) competition by winner-take-all mechanism. Luminance contrast of a stimulus is detected by four oriented Gabor filters in V1 layer (i). The DOF of every point on the object contour is computed based on the luminance contrast within CRF of BO-selective neuron and its surround (ii). The DOF signals and contrast signals are integrated via feedback connections by three distinct sizes of integration field, and via lateral connections by single integration field (iii). The responses at each retinotopic position are determined based on the maximum response among the cells (iv).

Fig. 2.
Fig. 2.

Simulation results for a single square. (a) Stimulus (a black square) used in the simulation. (b) The black circles in the right panels represent the retinal positions of two example V1 cells. The left panels show the time course of the activities of the example cells. The cell located at the square center (panels on the top) showed a strong response, whereas the cell located away from the center (the bottom) showed a weak response. (c) The spatial distribution of V1 activities along the horizontal midline of the square, showing a distinct peak at the center (the left panel), which is similar to the result of a physiological study (the right; replotted from [5]). (d) The two-dimensional spatial distribution of V1 activities. The activities that respond directly to stimulus contours are not shown. Light and dark colors indicate strong and weak responses, respectively. This shows the MA computed by the model. (e) The number of equidistant contour pairs from given point is normalized, and indicated by color. It showed that strong responses potentially occur elsewhere within the square, but no responses corresponding to the MA were evoked. (f) The MA computed using a mathematical method (the 2D Medial Axis Computation package of MATLAB). The MA computed from the package was passed through Gaussian filter, and normalized. The correlation coefficient between (d) and (f) was 0.91. (g) The shape reconstructed from (d). The reconstruction error was 0.028.

Fig. 3.
Fig. 3.

Comparison of the latencies between the model (black) and physiology (gray; replotted from [6]). Edge and Axis indicate spatial positions of the cells in examination, whose CRF is located on the contours and center of the square, respectively. Diff. indicates the difference in latency between the Edge and Axis, indicating a good agreement between the model and physiology with a constant difference of about 20 ms.

Fig. 4.
Fig. 4.

Simulation results obtained for an L-shaped tree branch (a)–(e), a rounded stone (f)–(j), and a bear ((k)–(o); the natural image of a bear was taken from the Berkeley Segmentation Dataset [38]). The conventions used were the same as those described in Fig. 2. (a), (f), (k) Natural images of the L-shaped tree branch, the rounded stone, and the bear. (b), (g), (l) The binary stimuli used in the simulations. (c), (h), (m) The two-dimensional, spatial distribution of V1 activities, representing the MA. (d), (i), (n) The MA computed using a mathematical method. (e), (j), (o) The reconstructed images from (c), (h), (m), respectively. (a)–(e) The correlation between (c) and (d) was 0.65. The reconstruction error was 0.17. (f)–(j) The correlation between (h) and (i) was 0.76 and the reconstruction error was 0.14. (k)–(o) The correlation of (m) and (n) was 0.78, and the reconstruction error was 0.15.

Fig. 5.
Fig. 5.

Simulation results obtained for two separated squares. The conventions used were the same as those described in Fig. 2. (a) The stimulus. (b) Strong responses were observed inside the squares, whereas no response was observed between the two squares. (c) The MA computed using a mathematical method. (d) The reconstructed images. The correlation between (b) and (c) was 0.89 and the reconstruction error was 0.028.

Fig. 6.
Fig. 6.

Simulation results obtained for two overlapping squares. The conventions used were the same as those described in Fig. 2. (a) The stimulus used for the simulation. (b) The two-dimensional, spatial distribution of V1 activities. Strong responses were observed inside the squares. (c) and (d) MAs computed using a mathematical method for occluding (c) and occluded squares (d). The correlations between the mathematical MA and the model MA were 0.75 and 0.69 for the occluding and occluded squares, respectively. (e) and (f) The reconstructed shapes from each model MA of occluding and occluded squares, respectively. Those MAs were plotted together in (b). The reconstruction errors were 0.082 and 0.038 for (e) and (f), respectively.

Fig. 7.
Fig. 7.

Simulation results for ambiguous figures. (a) An example stimulus used for the simulation. The stimuli were small patches of natural images from the Berkeley Segmentation Dataset [38]. The two regions divided by a border were filled with dark and light grays. (b) Schematic illustrations of the degree of synchronization. A portion (90% or 60%) of BO-selective cells responded to the border of an ambiguous figure were synchronized with those responded to the peripheral contours of an either side of the border [the left or right; highly synchronized side is denoted by red (solid) lines], and the rest (10% or 40%) of BO-selective cells were synchronized with those on the opposite side [denoted by blue (dotted) lines]. The simulations were conducted with 12 conditions (stimulus types=3; highly synchronized side=the left or right; synchronization ratio=91 or 64). (c) The spatial distributions of V1 activities when BO-selective cells were mostly (90%) synchronized with the left and right sides of the stimulus are shown in the left and right panels, respectively. The activities appear to be biased toward the direction of stronger synchronization. (d) A quantitative analysis of the bias shown in (c). The total activities within the left (black) and right (white) regions were plotted for the two synchronization conditions [red (solid) icons indicate highly synchronized side]. A bias toward the direction of stronger synchronization was observed. (e) The simulation results for the three stimuli that are shown at the top. The degrees of synchronization were 91 and 64 for the left and right panels, respectively. The bias was observed in all stimuli used in the simulations, albeit to a lesser degree in the 64 condition.

Fig. 8.
Fig. 8.

Schematic diagram of the sequence used for stimulus presentation. A fixation point (a red small dot) was presented at the center of a stimulus for 1500 ms with a random mask. The participants were instructed to remember the position of the fixation point. The test stimulus was presented on a gray background for 860 ms. Participants were asked to judge the direction of figure at the fixation point using two alternative forced choices without the feedback of correct answer. The fixation point on gray background was presented until the participants responded.

Fig. 9.
Fig. 9.

Configurations of test stimulus. (a) An original image from the Berkeley Segmentation Dataset (the top-left panel; [38]) and the contour of an object (cheetah) drawn by human participants (white line in the bottom-left panel). Small patches (the right panel) were extracted from the contour (denoted by black squares in the lower-left panel). (b) An example of the stimulus dots that were aligned in two lines along the contour that passed through the center of the stimulus and those along the outline square. Noise dots are not shown here for presentation purpose.

Fig. 10.
Fig. 10.

Results of the psychophysical experiments. The graphs on the left and right show the results obtained for 91 and 64 synchronization ratios, respectively. The icons placed at the bottom of the graphs show the configuration of stimuli, with red (solid) lines indicating the region of higher synchronization. Black and white bars indicate the ratio of the perceived DOF. In both the 91 and 64 conditions, the participants more often perceived a figure in the direction of higher synchronization (P<0.01). The magnitude of perceptual bias toward a higher synchronization direction was larger in the 9:1 case than in the 6:4 case (P<0.001).

Fig. 11.
Fig. 11.

Simulation results for three additional stimuli. The conventions used were the same as those described in Fig. 2. (a), (e), (i) The binary stimuli used for the simulations. (b), (f), (j) The spatial distributions of V1 activities. (c), (g), (k) MA computed from a mathematical method. (d), (h), (l) The reconstructed shapes from the MA computed by the model. (The top row) The simulation results for the rectangle. The correlation coefficient of (b) and (c) was 0.67 and the reconstruction error of (d) was 0.14. (The middle row) The simulation results for the triangle. The correlation coefficient of (f) and (g) was 0.60 and the reconstruction error of (h) was 0.32. (The bottom row) The correlation coefficient of (j) and (k) was 0.73 and the reconstruction error of (l) was 0.46.

Tables (5)

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Table 1. Parameters for Each Model Cell

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Table 2. Time Constants of EPSP and IPSP

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Table 3. Weights for Feedback and Lateral Connectionsa

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Table 4. Correlation with Mathematical MA and the Reconstruction Error

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Table 5. Weights for Reconstructiona

Equations (17)

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Cmdvdt=gNa(VENa)+gK(VEK)+gl(VEl)+I,
O2(x1,y1,t)=input(x1,y1)+cx,y{E(x,y,td)+I(x,y,td)},
E(x,y,td)=w(ve){exp((td)/τdecayexc)exp((td)/τriseexc)},
I(x,y,td)=w(ve){exp((td)/τdecayinh)exp((td)/τriseinh)},
Oσ3(x2,y2,t)=cx,y[Fσ(t,D(x2,y2,x,y,V2))+H(t,D(x2,y2,x,y,V1))],
Fσ(t,D(x2,y2,x,y,V2))=wfeedbackσE(x,y,tD(x2,y2,x,y,V2)),
H(t,D(x2,y2,x,y,V1))=wlateralE(x,y,tD(x2,y2,x,y,V1)),
D(x2,y2,x,y,L)=(x2x)2+(y2y)2+dV1,L2/vL,
O(x,y)=max(S0.7(x,y),S2.1(x,y),S3.5(x,y)),
Error=x,y[I(x,y)RC(x,y)]2x,y[I(x,y)+RC(x,y)]2,
Tθ(x0,y0)={(I0*Gθ)(x,y)(ifTθ>0)0(otherwise),
M(x,y)={1(iftresptthreshold)0(otherwise),
N(x,y)=maxσ(S0.7(x,y),S2.1(x,y),S3.5(x,y)),
σ(x,y)={argmaxσ(S0.7(x,y),S2.1(x,y),S3.5(x,y))(ifM(x,y)=1)0(otherwise),
T(x,y)=x1,y1N(x1,y1)×gaussx1,y1(x,y),
gaussx1,y1(x,y)=wσ(x1,y1)2πG(x1,y1)exp((xx1)2+(yy1)2σ(x1,y1)2).
RC(x,y)=11+exp((T(x,y)threshold)slope),

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