Abstract

Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas.

© 2003 Optical Society of America

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2002

G. Deco, T. S. Lee, “A unified model of spatial and object attention based on inter-cortical biased competition,” Neurocomputing 44–46, 769–774 (2002).

Z. W. Tu, S. C. Zhu, “Image segmentation by data-driven Markov chain Monte Carlo,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 657–673 (2002).
[CrossRef]

W. S. Geisler, R. L. Diehl, “Bayesian natural selection and the evolution of perceptual systems,” Philos. Trans. R. Soc. London Ser. B 357, 419–448 (2002).
[CrossRef]

P. J. Sjostrom, S. B. Nelson, “Spike timing, calcium signals and synaptic plasticity,” Curr. Opin. Neurobiol. 12, 305–314 (2002).
[CrossRef] [PubMed]

T. S. Lee, C. Yang, R. Romero, D. Mumford, “Neural activity in early visual cortex reflects behavioral experience and higher order perceptual saliency,” Nat. Neurosci. 5, 589–597 (2002).
[CrossRef] [PubMed]

S. Hochstein, M. Ahissar, “View from the top: hierarchies and reverse hierarchies in the visual system,” Neuron 36, 791–804 (2002).
[CrossRef] [PubMed]

S. Murray, D. Kersten, B. Olshausen, P. Schrater, D. Woods, “Shape perception reduces activity in human primary visual cortex,” Proc. Natl. Acad. Sci. USA 99, 15164–19169 (2002).

2001

B. M. Ramsden, C. P. Hung, A. W. Roe, “Real and illusory contour processing in area V1 of the primate: a cortical balancing act,” Cereb. Cortex 11, 648–665 (2001).
[CrossRef] [PubMed]

Z. Li, “A neural model of contour integration,” Neural Comput. 10, 903–940 (2001).
[CrossRef]

H. Super, H. Spekreijse, V. A. F. Lamme, “Two distinct modes of sensory processing observed in monkey primary visual cortex (V1),” Nat. Neurosci. 4, 304–310 (2001).
[CrossRef] [PubMed]

T. S. Lee, M. Nguyen, “Dynamics of subjective contour formation in the early visual cortex,” Proc. Natl. Acad. Sci. USA 98, 1907–1911 (2001).

S. Thrun, D. Fox, W. Burgard, F. Dellaert, “Robust Monte Carlo localization for mobile robots,” Artif. Intell. 101, 99–141 (2001).
[CrossRef]

R. VanRullen, S. Thorpe, “Is it a bird? Is it a plane? Ultra-rapid visual categorization of natural and artificial objects,” Perception 30, 655–668 (2001).
[CrossRef]

2000

M. K. Kapadia, G. Westheimer, C. D. Gilbert, “Spatial distribution of contextual interactions in primary visual cortex and in visual perception,” J. Neurophysiol. 84, 2048–2062 (2000).
[PubMed]

1999

J. Braun, “On the detection of salient contours,” Spatial Vision 12, 211–225 (1999).
[CrossRef] [PubMed]

C. Koch, T. Poggio, “Predicting the visual world: silence is golden,” Nat. Neurosci. 2, 9–10 (1999).
[CrossRef] [PubMed]

C. M. Gray, “The temporal correlation hypothesis of visual feature integration: still alive and well,” Neuron 24, 31–47 (1999).
[CrossRef]

C. v. D. Malsburg, “The what and why of binding: the modeler’s perspective,” Neuron 24, 95–104 (1999).
[CrossRef]

Y. Kamitani, S. Shimojo, “Manifestation of scotomas by transcranial magnetic stimulation of human visual cortex,” Nat. Neurosci. 2, 767–771 (1999).
[CrossRef] [PubMed]

M. Ito, C. D. Gilbert, “Attention modulates contextual influences in the primary visual cortex of alert monkeys,” Neuron 22, 593–604 (1999).
[CrossRef] [PubMed]

N. P. Bichot, J. D. Schall, “Effects of similarity and history on neural mechanisms of visual selection,” Nat. Neurosci. 2, 549–554 (1999).
[CrossRef] [PubMed]

Y. Sugase, S. Yamane, S. Ueno, K. Kawano, “Global and fine information coded by single neurons in the temporal visual cortex,” Nature 400, 869–873 (1999).
[CrossRef] [PubMed]

1998

J. M. Hupe, A. C. James, B. R. Payne, S. G. Lomber, P. Girard, J. Bullier, “Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons,” Nature 394, 784–787 (1998).
[CrossRef] [PubMed]

P. R. Roelfsema, V. A. F. Lamme, H. Spekreijse, “Object-based attention in the primary visual cortex of the macaque monkey,” Nature 395, 376–381 (1998).
[CrossRef] [PubMed]

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

A. Blake, B. Bascle, M. Isard, J. MacCormick, “Statis-tical models of visual shape and motion,” Proc. R. Soc. London Ser. A 356, 1283–1302 (1998).

N. K. Logothetis, “Object vision and visual awareness,” Curr. Opin. Neurobiol. 8, 536–544 (1998).
[CrossRef] [PubMed]

1997

L. Williams, D. Jacobs, “Stochastic completion fields: a neural model of illusory contour shape and saliency,” Neural Comput. 9, 837–858 (1997).
[CrossRef] [PubMed]

R. Rao, D. Ballard, “Dynamic model of visual recognition predicts neural response properties in the visual cortex,” Neural Comput. 9, 721–763 (1997).
[CrossRef] [PubMed]

1996

M. Usher, E. Niebur, “Modeling the temporal dynamics of IT neurons in visual search: A mechanism for top-down selective attention,” J. Cognit. Neurosci. 8, 311–327 (1996).
[CrossRef]

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

1995

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

S. Kosslyn, W. L. Thompson, I. J. Kim, N. M. Alpert, “Topographical representations of mental images in primary visual cortex,” Nature 378, 496–498 (1995).
[CrossRef] [PubMed]

R. Desimone, J. Duncan, “Neural mechanisms of selective visual attention,” Annu. Rev. Neurosci. 18, 193–222 (1995).
[CrossRef] [PubMed]

T. S. Lee, “A Bayesian framework for understanding texture segmentation in the primary visual cortex,” Vision Res. 35, 2643–2657 (1995).
[CrossRef] [PubMed]

G. Hinton, P. Dayan, B. Frey, R. Neal, “The wake-sleep algorithm for unsupervised neural networks,” Science 268, 1158–1161 (1995).
[CrossRef] [PubMed]

P. Dayan, G. E. Hinton, R. M. Neal, R. S. Zemel, “The Helmholtz machine,” Neural Comput. 7, 889–904 (1995).
[CrossRef] [PubMed]

1993

B. C. Motter, “Focal attention produces spatially selective processing in visual cortical areas V1, V2, V4 in the presence of competing stimuli,” J. Neurophysiol. 70, 909–919 (1993).
[PubMed]

1992

D. Mumford, “On the computational architecture of the neocortex II,” Biol. Cybern. 66, 241–251 (1992).
[CrossRef]

J. J. Knierim, D. C. Van Essen, “Neuronal responses to static texture patterns in area V1 of the alert macaque monkey,” J. Neurophysiol. 67, 961–980 (1992).
[PubMed]

1991

R. T. Born, R. B. H. Tootell, “Single-unit and 2-deoxyglucose studies of side inhibition in macaque striate cortex,” Proc. Natl. Acad. Sci. USA 88, 7071–7075 (1991).

D. J. Felleman, D. C. Van Essen, “Distributed hierarchical processing in the primate cerebral cortex,” Cereb. Cortex 1, 1–47 (1991).
[CrossRef] [PubMed]

D. Mumford, “On the computational architecture of the neocortex I,” Biol. Cybern. 65, 135–145 (1991).
[CrossRef]

1988

R. Gattass, A. P. Sousa, C. G. Gross, “Visuotopic organization and extent of V3 and V4 of the macaque,” J. Neurosci. 8, 1831–1845 (1988).
[PubMed]

V. S. Ramachandran, “Perception of shape from shading,” Nature 331, 163–166 (1988).
[CrossRef] [PubMed]

1987

G. Carpenter, S. Grossberg, “A massively parallel architecture for a self-organizing neural pattern recognition,” Comput. Vision Graphics Image Process. 37, 54–115 (1987).
[CrossRef]

1984

S. Ullman, “Visual routines,” Cognition 18, 97–159 (1984).
[CrossRef] [PubMed]

R. von der Heydt, E. Peterhans, G. Baumgarthner, “Illusory contours and cortical neuron responses,” Science 224, 1260–1262 (1984).
[CrossRef] [PubMed]

1981

J. L. McClelland, D. E. Rumelhart, “An interactive activation model of context effects in letter perception. Part I: an account of basic findings,” Psychol. Rev. 88, 375–407 (1981).
[CrossRef]

1978

D. H. Hubel, T. N. Wiesel, “Functional architecture of macaque monkey visual cortex,” Proc. R. Soc. London Ser. B 198, 1–59 (1978).
[CrossRef]

Adelson, E.

E. Adelson, A. Pentland, “The perception of shading and reflectance,” in Perception as Bayesian Inference, D. Knill, W. Richards, eds. (Cambridge U. Press, Cambridge, UK, 1996), pp. 409–423.

P. Sinha, E. Adelson, “Recovering reflectance in a world of painted polyhedra,” in Proceedings of the 4th International Conference on Computer Vision (IEEE Computer Society Press, Los Alamitos, Calif., 1993), pp. 156–163.

Ahissar, M.

S. Hochstein, M. Ahissar, “View from the top: hierarchies and reverse hierarchies in the visual system,” Neuron 36, 791–804 (2002).
[CrossRef] [PubMed]

Alpert, N. M.

S. Kosslyn, W. L. Thompson, I. J. Kim, N. M. Alpert, “Topographical representations of mental images in primary visual cortex,” Nature 378, 496–498 (1995).
[CrossRef] [PubMed]

Anderson, C. H.

C. Eliasmith, C. H. Anderson, Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems (MIT Press, Cambridge, Mass., 2002).

Arathorn, D. W.

D. W. Arathorn, Map-Seeking Circuits in Visual Cognition: a Computational Mechanism for Biological and Machine Vision (Stanford U. Press, Palo Alto, Calif., 2002).

August, J.

J. August, S. W. Zucker, “The curve indicator random field: curve organization via edge correlation,” in Perceptual Organization for Artificial Vision Systems, K. Boyer, S. Sarka, eds. (Kluwer Academic, Boston, Mass., 2000), pp. 265–288.

Ballard, D.

R. Rao, D. Ballard, “Dynamic model of visual recognition predicts neural response properties in the visual cortex,” Neural Comput. 9, 721–763 (1997).
[CrossRef] [PubMed]

Bascle, B.

A. Blake, B. Bascle, M. Isard, J. MacCormick, “Statis-tical models of visual shape and motion,” Proc. R. Soc. London Ser. A 356, 1283–1302 (1998).

Baumgarthner, G.

R. von der Heydt, E. Peterhans, G. Baumgarthner, “Illusory contours and cortical neuron responses,” Science 224, 1260–1262 (1984).
[CrossRef] [PubMed]

Bichot, N. P.

N. P. Bichot, J. D. Schall, “Effects of similarity and history on neural mechanisms of visual selection,” Nat. Neurosci. 2, 549–554 (1999).
[CrossRef] [PubMed]

Bienenstock, E.

E. Bienenstock, S. Geman, D. Potter, “Compositionality, MDL priors, and object recognition,” in Advances in Neural Information Processing Systems, M. C. Mozer, M. I. Jordan, T. Petsche, eds. (MIT Press, Cambridge, Mass., 1997), Vol. 9, pp. 838–844.

Blake, A.

A. Blake, B. Bascle, M. Isard, J. MacCormick, “Statis-tical models of visual shape and motion,” Proc. R. Soc. London Ser. A 356, 1283–1302 (1998).

M. Isard, A. Blake, “ICONDENSATION: Unifying low-level and high-level tracking in a stochastic framework,” in Lecture Notes in Computer Science 1406, H. Burkhardt, B. Neumann, ed. (Springer-Verlag, Berlin, 1998), pp. 893–908.

Born, R. T.

R. T. Born, R. B. H. Tootell, “Single-unit and 2-deoxyglucose studies of side inhibition in macaque striate cortex,” Proc. Natl. Acad. Sci. USA 88, 7071–7075 (1991).

Braun, J.

J. Braun, “On the detection of salient contours,” Spatial Vision 12, 211–225 (1999).
[CrossRef] [PubMed]

Bullier, J.

J. M. Hupe, A. C. James, B. R. Payne, S. G. Lomber, P. Girard, J. Bullier, “Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons,” Nature 394, 784–787 (1998).
[CrossRef] [PubMed]

Burgard, W.

S. Thrun, D. Fox, W. Burgard, F. Dellaert, “Robust Monte Carlo localization for mobile robots,” Artif. Intell. 101, 99–141 (2001).
[CrossRef]

Carpenter, G.

G. Carpenter, S. Grossberg, “A massively parallel architecture for a self-organizing neural pattern recognition,” Comput. Vision Graphics Image Process. 37, 54–115 (1987).
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Figures (8)

Fig. 1
Fig. 1

V1 is reciprocally connected to all the expert visual modules either directly or indirectly. It therefore can serve as a high-resolution buffer to integrate various information together into a coherent percept. In this example of the high-resolution buffer, the bottom-up cues from the illuminated part of the face cause a face hypothesis to respond, which provides the contextual priors of the face to reexamine the data at the high-resolution buffer, locating the faint edge in the shadow as a part of the face.

Fig. 2
Fig. 2

(a) Schematic of the proposed hierarchical Bayesian inference framework in the cortex: The different visual areas (boxes) are linked together as a Markov chain. The activity in V1, x1, is influenced by the bottom-up feedforward data x0 and the probabilistic priors P(x1|x2) fed back from V2. The concept of a Markov chain is important computationally because each area is influenced mainly by its direct neighbors. (b) An alternative way of implementing hierarchical Bayesian inference by using particle filtering and belief propagation: B1 and B2 are bottom-up and top-down beliefs, respectively. They are sets of numbers that reflect the conditional probabilities of the particles conditioned on the context that has been incorporated by the belief propagation so far. The top-down beliefs are the responses of the deep layer pyramidal cells that project backward, and the bottom-up beliefs are the activities of the responses of the superficial layer pyramidal cells that project to the higher areas. The potentials ϕ are the synaptic weights at the terminals of the projecting axons. A hypothesis particle may link a set of particles spanning several cortical areas, and the probability of this hypothesis particle could be signified by its binding strength via either synchrony or rapid synaptic weight changes.

Fig. 3
Fig. 3

Selected stimuli in the subjective contour experiment. (a) Example of a stimulus presentation sequence in a single trial. (b) Kanizsa square with illusory contour. Receptive field of the tested neuron was “placed” at ten different positions across the illusory contour, one per trial. (c) Amodal contour stimulus; the subjective contour was interrupted by intersecting lines. (d) One of the several rotated partial disk controls. The surround stimulus was roughly the same, but there was no illusory contour. (e) One of the several types of real squares defined by luminance contrast. (f) Square defined by lines, used as control to assess the neuron’s sensitivity to the spatial location of the real contour as well as to comparing the temporal responses between real and illusory contours. See Lee and Nguyen48 for details.

Fig. 4
Fig. 4

(a) Spatial profile of a V1 neuron’s response to the contours of both real and illusory squares, in a temporal window 100–150 ms after stimulus onset. The real or illusory square was placed at different spatial locations relative to the receptive field of the cell. This cell responded to the illusory contour when it was at precisely the same location where a real contour evoked the maximal response from the neuron. It also responded significantly better to the illusory contour than to the amodal contour (t test, p<0.003) and did not respond much when the partial disks were rotated. (b) Temporal evolution of the cell’s response to the illusory contour compared with its response to the real contours of a line square or a white square, as well as to the amodal contour. The onset of the response to the real contours was at 45 ms, ∼55 ms ahead the illusory contour response. (c) Population-averaged temporal response of 49 V1 neurons in the superficial layer to the illusory contours and controls. (d) Population-averaged temporal response of 39 V2 neurons in the superficial layer to the illusory contours and controls. The results show that V2 responds to illusory contour earlier than V1. See Lee and Nguyen48 for details.

Fig. 5
Fig. 5

Ramachandran67 showed that SFS stimuli produced instantaneous segregation, whereas BW contrast stimuli did not. Given the main distinction between the two types of stimuli is that only the SFS stimulus elements in (a) but not those in (b) afford 3D interpretation; 3D information must directly influence the early parallel processes of perceptual grouping.

Fig. 6
Fig. 6

Higher-order perceptual pop-out. (a) A typical stimulus display was composed of ten×10 stimulus elements. Each element was 1° visual angle in diameter. The diameter of the classical receptive field of a typical cell at the eccentricities tested ranged from 0.4° to 0.8° visual angle. Displayed is an example of a lighting from above (LA) oddball condition, with the LA oddball placed on top of the cell’s receptive field, indicated by the open circle. The solid dot indicates the fixation spot. (b) There are six sets of stimuli. The SFS stimulus elements include LA and Lambertian sphere with lighting from above, below, left and right (LB, LL, and LR, respectively). The BW stimulus elements include white above (WA) and white below (WB). Each stimulus set had four conditions: singleton, oddball, uniform, and hole. Displayed are the iconic diagrams of all the conditions for the LA set and the LB set and the oddball conditions for the other four sets. The center element in the iconic diagram covers the receptive field of the neuron in the experiment. The surround stimulus elements were placed outside the receptive field of the neuron. The key comparison was made between the oddball condition and the uniform condition, while the singleton and the hole conditions were controls. See Lee et al.49 for details.

Fig. 7
Fig. 7

Temporal evolution of the average population response of 22 V2 units and 30 V1 units from a monkey to the LA set and the WA set in a stage after the monkey had utilized the stimuli in its behavior. Each unit’s response was first smoothed by a running average within a 15-ms window and then averaged across the population. A significant difference (pop-out response) was observed between the population average response to the oddball condition and that to the uniform condition in the LA set for both V2 and V1 [(a), (c)] neurons, starting at 100-ms poststimulus onset. No pop-out response was observed in the WA set [(b), (d)]. See Lee et al.49 for details.

Fig. 8
Fig. 8

(a)–(c) Three examples of the texture stimuli used in the experiment. Different parts of the stimuli, along a horizontal line across the middle of the square or across the strip, were placed on the receptive field of the recorded neuron over successive trials. The width of the square or the strip is 4°. (d), (e). Spatiotemporal evolution of the summed response of a population of V1 neurons to the texture squares. The summed response was obtained by adding the response to stimulus (a) and the response to stimulus (b) at the corresponding locations. This addition eliminates the effect due to orientation tuning and reveals a signal that enhances the figure’s interior relative to the background. The spatial offset is the distance in degrees of visual angle from the center of the square or the strip; hence -2° and 2° offsets represent the boundary locations. When the neurons’ preferred orientation was parallel to the texture boundaries, a very strong boundary signal was superimposed on the interior enhancement (coloring) signal [(d)]. When the neurons’ preferred orientation was orthogonal to the vertical texture boundaries, the enhancement was relatively uniform within the figure [(e)]. (f). Population-averaged response of a set of vertical neurons to stimulus (c). The initial response was characterized by a burst, whose magnitude was correlated with sensitivity to local feature orientation, followed by a more sustained response at a lower level. The response at the boundary is significantly higher than the response at the interior. These phenomena underscore the interplay among resonance, competition and “explaining away.” See Lee et al.41 for details.

Equations (18)

Equations on this page are rendered with MathJax. Learn more.

P(x0,x1|xh)=P(x0|x1, xh)P(x1|xh),
P(x1|x0, xh)P(x0|xh)=P(x0, x1|xh),
P(x1|x0, xh)=P(x0|x1, xh)P(x1|xh)P(x0|xh).
P(x0, xv1, xv2, xv4, xIT)
=P(x0|xv1)P(xv1|xv2)P(xv2|xv4)P(xv4|xIT)P(xIT)
x0xv1xv2xv4xIT.
P(xv1|x0, xv2, xv4, xIT)=P(x0|xv1)P(xv1|xv2)/Z1,
P(xv2|x0, xv1, xv4, xIT)=P(xv1|xv2)P(xv2|xv4)/Z2,
P(xv4|x0, xv1, xv2, xIT)=P(xv2|xv4)P(xv4|xIT)/Z4.
P(xv1|x0, xv2, xv4, xIT)
=ϕ(x0, xv1)ϕ(xv1, xv2)/Z(x0, xv2), 
P(xv2|x0, xv1, xv4, xIT)
=ϕ(xv1, xv2)ϕ(xv2, xv4)/Z(vv1, xv4),
P(xv4|x0, xv1, xv2, xIT)
=ϕ(xv2, xv4)ϕ(xv4, xIT)/Z(xv2, xIT),
B1(xi(j))=maxk[B1(xi-1(k))ϕ(xi-1(k), xi(j))],
B2(xi(j))=maxk[B2(xi+1(k))ϕ(xi(j), xi+1(k))],
wi,j=B1(xi(j))B2(xi(j))/(normalizingfactorZi).

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