Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Top-down influences on visual processing

Subjects

Key Points

  • In contrast to the traditional idea that the processing of visual information consists of a sequence of feedforward operations, with neuronal functional properties taking on increasing complexity as the information progresses through a hierarchy of cortical areas, increasing evidence points towards a reverse process, with higher-order cognitive influences interacting with information coming from the retina.

  • Thus, rather than having a fixed functional role, neurons should be thought of as adaptive processors, changing their function according to the behavioural context.

  • Vision is an active process in which higher-order cognitive influences affect the operations performed by cortical neurons.

  • Visual pathways operate bidirectionally, with each feedforward connection being matched by feedback or re-entrant connections going from higher- to lower-order cortical areas.

  • Top-down influences include various forms of attention, such as spatial, object oriented and feature oriented attention.

  • Top-down influences are not limited to attention but mediate a much broader range of functional roles, including perceptual task, object expectation, scene segmentation, efference copy, working memory and the encoding and recall of learned information.

  • The effect of top-down influences is to change the information conveyed by neurons, both by altering the tuning of their responses to stimulus attributes and by changing the structure of correlations over neuronal ensembles.

  • All areas of the visual pathway, except for the retina, are subject to top-down influences, including early cortical stages of visual processing such as the primary visual cortex and the lateral geniculate nucleus, and all areas along the dorsal and ventral visual cortical pathways. Each area contains an association field of potential interactions, and expresses a subset of these interactions to execute different functions.

  • The sources of top-down influences are widespread, with each area providing information reflecting the functional properties of that area. As a consequence, even a single neuron can be viewed as a microcosm of activity occurring throughout the visual pathway.

  • We propose that the circuit mechanism of top-down control and adaptive processing involves a gating of intrinsic cortical circuits within an area mediated by long-range feedback connections to that area. By selecting a subset of inputs, a neuron can express different components of its association field, and as a result take on different functional roles.

Abstract

Re-entrant or feedback pathways between cortical areas carry rich and varied information about behavioural context, including attention, expectation, perceptual tasks, working memory and motor commands. Neurons receiving such inputs effectively function as adaptive processors that are able to assume different functional states according to the task being executed. Recent data suggest that the selection of particular inputs, representing different components of an association field, enable neurons to take on different functional roles. In this Review, we discuss the various top-down influences exerted on the visual cortical pathways and highlight the dynamic nature of the receptive field, which allows neurons to carry information that is relevant to the current perceptual demands.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Feedback pathways carrying top-down information.
Figure 2: Task-dependent changes in neural tuning and information content in the primary visual cortex.
Figure 3: Neurons in the prefrontal cortex carry out different functions in accordance with the task.
Figure 4: Learned association generates recall-related activity in the medial temporal area.
Figure 5: Task-dependent changes in local field potential coherence and noise correlations in the primary visual cortex.
Figure 6: Top-down influences on effective connectivity within and between cortical areas.

Similar content being viewed by others

References

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Roelfsema, P. R., Lamme, V. A. & Spekreijse, H. Object-based attention in the primary visual cortex of the macaque monkey. Nature 395, 376–381 (1998). The responses of neurons in area V1 are influenced by object-oriented attention. In a curve tracing task, responses depend on whether the receptive field lies along a target or distracter curve.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Li, W., Piech, V. & Gilbert, C. D. Contour saliency in primary visual cortex. Neuron 50, 951–962 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. Li, W., Piech, V. & Gilbert, C. D. Learning to link visual contours. Neuron 57, 442–451 (2008). Neurons in area V1 perform contour integration and their properties change during the course of perceptual learning. The learning-dependent properties are subject to top-down influences, in that they are expressed only when animals perform the trained task.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zhang, N. R. & von der Heydt, R. Analysis of the context integration mechanisms underlying figure–ground organization in the visual cortex. J. Neurosci. 30, 6482–6496 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. McManus, J. N., Li, W. & Gilbert, C. D. Adaptive shape processing in primary visual cortex. Proc. Natl Acad. Sci. USA 108, 9739–9746 (2011). Neurons in area V1 are selective for more complex geometric shapes than previously thought, and their shape-selectivity is dependent on the shape the animal is cued to expect. This reflects the ability of neurons to selectively express subcomponents of their association fields.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Womelsdorf, T., Anton-Erxleben, K. & Treue, S. Receptive field shift and shrinkage in macaque middle temporal area through attentional gain modulation. J. Neurosci. 28, 8934–8944 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Thomson, E. E. & Kristan, W. B. Quantifying stimulus discriminability: a comparison of information theory and ideal observer analysis. Neural Comput. 17, 741–778 (2005).

    Article  PubMed  Google Scholar 

  11. Gilbert, C. D. & Wiesel, T. N. Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. J. Neurosci. 9, 2432–2442 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Li, W. & Gilbert, C. D. Global contour saliency and local colinear interactions. J. Neurophysiol. 88, 2846–2856 (2002).

    Article  PubMed  Google Scholar 

  13. Gilbert, C. D. & Li, W. Adult visual cortical plasticity. Neuron 75, 250–264 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Stettler, D. D., Das, A., Bennett, J. & Gilbert, C. D. Lateral connectivity and contextual interactions in macaque primary visual cortex. Neuron 36, 739–750 (2002).

    Article  CAS  PubMed  Google Scholar 

  15. Hupe, J. M. et al. Feedback connections act on the early part of the responses in monkey visual cortex. J. Neurophysiol. 85, 134–145 (2001).

    Article  CAS  PubMed  Google Scholar 

  16. Bair, W., Cavanaugh, J. R. & Movshon, J. A. Time course and time-distance relationships for surround suppression in macaque V1 neurons. J. Neurosci. 23, 7690–7701 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Angelucci, A. & Bressloff, P. C. Contribution of feedforward, lateral and feedback connections to the classical receptive field center and extra-classical receptive field surround of primate V1 neurons. Prog. Brain Res. 154, 93–120 (2006).

    Article  PubMed  Google Scholar 

  18. Piech, V., Li, W., Reeke, G. N. & Gilbert, C. D. A network model of top-down influences on local gain and contextual interactions in visual cortex. Soc. Neurosi. Abstr. 701.10 (Chicago, 12–16 Nov 2009).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Li, W., Piech, V. & Gilbert, C. D. Perceptual learning and top-down influences in primary visual cortex. Nature Neurosci. 7, 651–657 (2004). The information conveyed by the responses of neurons in area V1 changes according to perceptual task. Even with the identical stimulus, neurons change their tuning to different stimulus configurations depending on which stimulus components are relevant or irrelevant to the task being executed.

    Article  CAS  PubMed  Google Scholar 

  21. O'Connor, D. H., Fukui, M. M., Pinsk, M. A. & Kastner, S. Attention modulates responses in the human lateral geniculate nucleus. Nature Neurosci. 5, 1203–1209 (2002).

    Article  CAS  PubMed  Google Scholar 

  22. McAlonan, K., Cavanaugh, J. & Wurtz, R. H. Guarding the gateway to cortex with attention in visual thalamus. Nature 456, 391–394 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Rao, R. P. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neurosci. 2, 79–87 (1999).

    Article  CAS  PubMed  Google Scholar 

  24. Spratling, M. W. Predictive coding as a model of response properties in cortical area V1. J. Neurosci. 30, 3531–3543 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chen, Y. et al. Task difficulty modulates the activity of specific neuronal populations in primary visual cortex. Nature Neurosci. 11, 974–982 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  27. Posner, M. I., Snyder, C. R. & Davidson, B. J. Attention and the detection of signals. J. Exp. Psychol. 109, 160–174 (1980).

    Article  CAS  PubMed  Google Scholar 

  28. Moran, J. & Desimone, R. Selective attention gates visual processing in the extrastriate cortex. Science 229, 782–784 (1985).

    Article  CAS  PubMed  Google Scholar 

  29. Mountcastle, V. B., Motter, B. C., Steinmetz, M. A. & Sestokas, A. K. Common and differential effects of attentive fixation on the excitability of parietal and prestriate (V4) cortical visual neurons in the macaque monkey. J. Neurosci. 7, 2239–2255 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Spitzer, H., Desimone, R. & Moran, J. Increased attention enhances both behavioral and neuronal performance. Science 240, 338–340 (1988).

    Article  CAS  PubMed  Google Scholar 

  31. Chelazzi, L., Miller, E. K., Duncan, J. & Desimone, R. A neural basis for visual search in inferior temporal cortex. Nature 363, 345–347 (1993).

    Article  CAS  PubMed  Google Scholar 

  32. Chelazzi, L., Miller, E. K., Duncan, J. & Desimone, R. Responses of neurons in macaque area V4 during memory-guided visual search. Cereb. Cortex 11, 761–772 (2001).

    Article  CAS  PubMed  Google Scholar 

  33. Treue, S. & Maunsell, J. H. Attentional modulation of visual motion processing in cortical areas MT and MST. Nature 382, 539–541 (1996).

    Article  CAS  PubMed  Google Scholar 

  34. Luck, S. J., Chelazzi, L., Hillyard, S. A. & Desimone, R. Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. J. Neurophysiol. 77, 24–42 (1997).

    Article  CAS  PubMed  Google Scholar 

  35. Ito, M. & Gilbert, C. D. Attention modulates contextual influences in the primary visual cortex of alert monkeys. Neuron 22, 593–604 (1999). Although early studies indicated that there is little effect of attention in area V1 on responses to simple stimuli, this study showed that contextual influences were particularly subject to the allocation of attention and therefore that the effect on area V1 responses to complex stimuli could be substantial.

    Article  CAS  PubMed  Google Scholar 

  36. McAdams, C. J. & Maunsell, J. H. Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4. J. Neurosci. 19, 431–441 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Reynolds, J. H., Chelazzi, L. & Desimone, R. Competitive mechanisms subserve attention in macaque areas V2 and V4. J. Neurosci. 19, 1736–1753 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Crist, R. E., Li, W. & Gilbert, C. D. Learning to see: experience and attention in primary visual cortex. Nature Neurosci. 4, 519–525 (2001).

    Article  CAS  PubMed  Google Scholar 

  39. Treue, S. Neural correlates of attention in primate visual cortex. Trends Neurosci. 24, 295–300 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. Reynolds, J. H. & Desimone, R. Interacting roles of attention and visual salience in V4. Neuron 37, 853–863 (2003).

    Article  CAS  PubMed  Google Scholar 

  41. Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995).

    Article  CAS  PubMed  Google Scholar 

  42. Maunsell, J. H. & Cook, E. P. The role of attention in visual processing. Phil. Trans. R. Soc. Lond. B 357, 1063–1072 (2002).

    Article  Google Scholar 

  43. Gandhi, S. P., Heeger, D. J. & Boynton, G. M. Spatial attention affects brain activity in human primary visual cortex. Proc. Natl Acad. Sci. USA 96, 3314–3319 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Poghosyan, V. & Ioannides, A. A. Attention modulates earliest responses in the primary auditory and visual cortices. Neuron 58, 802–813 (2008).

    Article  CAS  PubMed  Google Scholar 

  45. Kastner, S., De Weerd, P., Desimone, R. & Ungerleider, L. G. Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI. Science 282, 108–111 (1998).

    Article  CAS  PubMed  Google Scholar 

  46. Watanabe, T. et al. Task-dependent influences of attention on the activation of human primary visual cortex. Proc. Natl Acad. Sci. USA 95, 11489–11492 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ito, M., Westheimer, G. & Gilbert, C. D. Attention and perceptual learning modulate contextual influences on visual perception. Neuron 20, 1191–1197 (1998).

    Article  CAS  PubMed  Google Scholar 

  48. Chelazzi, L., Duncan, J., Miller, E. K. & Desimone, R. Responses of neurons in inferior temporal cortex during memory-guided visual search. J. Neurophysiol. 80, 2918–2940 (1998).

    Article  CAS  PubMed  Google Scholar 

  49. Motter, B. C. Neural correlates of attentive selection for color or luminance in extrastriate area V4. J. Neurosci. 14, 2178–2189 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Treue, S. & Martinez Trujillo, J. C. Feature-based attention influences motion processing gain in macaque visual cortex. Nature 399, 575–579 (1999). Attention to a features, such as direction of movement, can increase responses of neurons that are selective for that feature.

    Article  CAS  PubMed  Google Scholar 

  51. Bulthoff, I., Bulthoff, H. & Sinha, P. Top-down influences on stereoscopic depth-perception. Nature Neurosci. 1, 254–257 (1998).

    Article  CAS  PubMed  Google Scholar 

  52. Reynolds, J. H., Pasternak, T. & Desimone, R. Attention increases sensitivity of V4 neurons. Neuron 26, 703–714 (2000).

    Article  CAS  PubMed  Google Scholar 

  53. Giesbrecht, B., Woldorff, M. G., Song, A. W. & Mangun, G. R. Neural mechanisms of top-down control during spatial and feature attention. Neuroimage 19, 496–512 (2003).

    Article  CAS  PubMed  Google Scholar 

  54. O'Craven, K. M., Downing, P.E., & Kanwisher, N. fMRI evidence for objects as the units of attentional selection. Nature 401, 584–587 (1999).

    Article  CAS  PubMed  Google Scholar 

  55. Duncan, J. Selective attention and the organization of visual information. J. Exp. Psychol. Gen. 113, 501–517 (1984).

    Article  CAS  PubMed  Google Scholar 

  56. Egly, R., Driver, J. & Rafal, R. D. Shifting visual attention between objects and locations: evidence from normal and parietal lesion subjects. J. Exp. Psychol. Gen. 123, 161–177 (1994).

    Article  CAS  PubMed  Google Scholar 

  57. Blaser, E., Pylyshyn, Z. W. & Holcombe, A. O. Tracking an object through feature space. Nature 408, 196–199 (2000).

    Article  CAS  PubMed  Google Scholar 

  58. Reynolds, J. H., Alborzian, S. & Stoner, G. R. Exogenously cued attention triggers competitive selection of surfaces. Vision Res. 43, 59–66 (2003).

    Article  PubMed  Google Scholar 

  59. Yantis, S. & Serences, J. T. Cortical mechanisms of space-based and object-based attentional control. Curr. Opin. Neurobiol. 13, 187–193 (2003).

    Article  CAS  PubMed  Google Scholar 

  60. Poort, J. et al. The role of attention in figure-ground segregation in areas V1 and V4 of the visual cortex. Neuron 75, 143–156 (2012).

    Article  CAS  PubMed  Google Scholar 

  61. Somers, D. C., Dale, A. M., Seiffert, A. E. & Tootell, R. B. Functional MRI reveals spatially specific attentional modulation in human primary visual cortex. Proc. Natl Acad. Sci. USA 96, 1663–1668 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Saenz, M., Buracas, G. T. & Boynton, G. M. Global effects of feature-based attention in human visual cortex. Nature Neurosci. 5, 631–632 (2002).

    Article  CAS  PubMed  Google Scholar 

  63. Serences, J. T. & Boynton, G. M. Feature-based attentional modulations in the absence of direct visual stimulation. Neuron 55, 301–312 (2007).

    Article  CAS  PubMed  Google Scholar 

  64. Wertheimer, M. Untersuchungen zur Lehre von der Gestalt. Psychol. Forsch. 4, 301–350 (1923).

    Article  Google Scholar 

  65. Riesenhuber, M. & Poggio, T. Hierarchical models of object recognition in cortex. Nature Neurosci. 2, 1019–1025 (1999).

    Article  CAS  PubMed  Google Scholar 

  66. Borenstein, E. & Ullman, S. Combined top-down/bottom-up segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 30, 2109–2125 (2008).

    Article  PubMed  Google Scholar 

  67. Zhou, H. & Desimone, R. Feature-based attention in the frontal eye field and area V4 during visual search. Neuron 70, 1205–1217 (2011). In a visual search paradigm, objects sharing a common feature can become salient. This study shows the influence of feature-based attention in frontal eye fields and area V4 during visual search.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Monosov, I. E., Trageser, J. C. & Thompson, K. G. Measurements of simultaneously recorded spiking activity and local field potentials suggest that spatial selection emerges in the frontal eye field. Neuron 57, 614–625 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Morishima, Y. et al. Task-specific signal transmission from prefrontal cortex in visual selective attention. Nature Neurosci. 12, 85–91 (2009).

    Article  CAS  PubMed  Google Scholar 

  70. Ninomiya, T., Sawamura, H., Inoue, K. & Takada, M. Segregated pathways carrying frontally derived top-down signals to visual areas MT and V4 in macaques. J. Neurosci. 32, 6851–6858 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Fritz, J., Elhilali, M. & Shamma, S. Active listening: task-dependent plasticity of spectrotemporal receptive fields in primary auditory cortex. Hear. Res. 206, 159–176 (2005).

    Article  PubMed  Google Scholar 

  72. Cromer, J. A., Roy, J. E. & Miller, E. K. Representation of multiple, independent categories in the primate prefrontal cortex. Neuron 66, 796–807 (2010). In the prefrontal cortex, as in early visual areas, the perceptual task alters neuronal function. In a categorization task, changing the categorical boundary alters the stimulus selectivity of neurons.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Ullman, S. Object recognition and segmentation by a fragment-based hierarchy. Trends Cogn. Sci. 11, 58–64 (2007).

    Article  PubMed  Google Scholar 

  74. Golcu, D. & Gilbert, C. D. Perceptual learning of object shape. J. Neurosci. 29, 13621–13629 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Sommer, M. A. & Wurtz, R. H. What the brain stem tells the frontal cortex. II. Role of the SC–MD–FEF pathway in corollary discharge. J. Neurophysiol. 91, 1403–1423 (2004).

    Article  PubMed  Google Scholar 

  76. Wurtz, R. H. & Sommer, M. A. Identifying corollary discharges for movement in the primate brain. Prog. Brain Res. 144, 47–60 (2004).

    Article  PubMed  Google Scholar 

  77. Duhamel, J. R., Colby, C. L. & Goldberg, M. E. The updating of the representation of visual space in parietal cortex by intended eye movements. Science 255, 90–92 (1992).

    Article  CAS  PubMed  Google Scholar 

  78. Rolfs, M., Jonikaitis, D., Deubel, H. & Cavanagh, P. Predictive remapping of attention across eye movements. Nature Neurosci. 14, 252–256 (2011).

    Article  CAS  PubMed  Google Scholar 

  79. Sommer, M. A. & Wurtz, R. H. Influence of the thalamus on spatial visual processing in frontal cortex. Nature 444, 374–377 (2006). Several studies from Sommer and Wurtz have unveiled the pathway involved in the corollary discharge or efference copy signal, showing how a motor command is sent to the sensory apparatus to maintain a stable visual scene despite continual eye movements that cause visual images to move across the retina.

    Article  CAS  PubMed  Google Scholar 

  80. Kusunoki, M. & Goldberg, M. E. The time course of perisaccadic receptive field shifts in the lateral intraparietal area of the monkey. J. Neurophysiol. 89, 1519–1527 (2003).

    Article  PubMed  Google Scholar 

  81. Umeno, M. M. & Goldberg, M. E. Spatial processing in the monkey frontal eye field. I. Predictive visual responses. J. Neurophysiol. 78, 1373–1383 (1997).

    Article  CAS  PubMed  Google Scholar 

  82. Nakamura, K. & Colby, C. L. Updating of the visual representation in monkey striate and extrastriate cortex during saccades. Proc. Natl Acad. Sci. USA 99, 4026–4031 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Colby, C. L., Duhamel, J. R. & Goldberg, M. E. Visual, presaccadic, and cognitive activation of single neurons in monkey lateral intraparietal area. J. Neurophysiol. 76, 2841–2852 (1996). Receptive fields shift in anticipation of eye movements, which may underlie the perceived stability of visual targets across eye movements.

    Article  CAS  PubMed  Google Scholar 

  84. Batista, A. P., Buneo, C. A., Snyder, L. H. & Andersen, R. A. Reach plans in eye-centered coordinates. Science 285, 257–260 (1999).

    Article  CAS  PubMed  Google Scholar 

  85. Schlack, A. & Albright, T. D. Remembering visual motion: neural correlates of associative plasticity and motion recall in cortical area MT. Neuron 53, 881–890 (2007). A cortical area can serve as a scratch pad for representing learned associations between disparate stimuli. By cognitively linking the image of an arrow with a pattern of moving dots, area MT, which ordinarily responds only to moving stimuli, can be induced to respond to the stationary arrow.

    Article  CAS  PubMed  Google Scholar 

  86. Umeno, M. M. & Goldberg, M. E. Spatial processing in the monkey frontal eye field. II. Memory responses. J. Neurophysiol. 86, 2344–2352 (2001).

    Article  CAS  PubMed  Google Scholar 

  87. Armstrong, K. M., Chang, M. H. & Moore, T. Selection and maintenance of spatial information by frontal eye field neurons. J. Neurosci. 29, 15621–15629 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Zohary, E., Shadlen, M. N. & Newsome, W. T. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370, 140–143 (1994).

    Article  CAS  PubMed  Google Scholar 

  89. Bair, W., Zohary, E. & Newsome, W. T. Correlated firing in macaque visual area MT: time scales and relationship to behavior. J. Neurosci. 21, 1676–1697 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Gawne, T. J., Kjaer, T. W., Hertz, J. A. & Richmond, B. J. Adjacent visual cortical complex cells share about 20% of their stimulus-related information. Cereb. Cortex 6, 482–489 (1996).

    Article  CAS  PubMed  Google Scholar 

  91. Lee, D., Port, N. L., Kruse, W. & Georgopoulos, A. P. Variability and correlated noise in the discharge of neurons in motor and parietal areas of the primate cortex. J. Neurosci. 18, 1161–1170 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Abbott, L. F. & Dayan, P. The effect of correlated variability on the accuracy of a population code. Neural Comput. 11, 91–101 (1999). This study provides a theoretical framework for understanding the conditions under which changes in the structure of correlated activity over a neuronal population can increase the amount of information carried by the population.

    Article  CAS  PubMed  Google Scholar 

  93. Averbeck, B. B., Latham, P. E. & Pouget, A. Neural correlations, population coding and computation. Nature Rev. Neurosci. 7, 358–366 (2006).

    Article  CAS  Google Scholar 

  94. Oram, M. W., Foldiak, P., Perrett, D. I. & Sengpiel, F. The 'Ideal Homunculus': decoding neural population signals. Trends Neurosci. 21, 259–265 (1998).

    Article  CAS  PubMed  Google Scholar 

  95. Panzeri, S., Schultz, S. R., Treves, A. & Rolls, E. T. Correlations and the encoding of information in the nervous system. Proc. Biol. Sci. 266, 1001–1012 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Romo, R., Hernandez, A., Zainos, A. & Salinas, E. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38, 649–657 (2003).

    Article  CAS  PubMed  Google Scholar 

  97. Gu, Y. et al. Perceptual learning reduces interneuronal correlations in macaque visual cortex. Neuron 71, 750–761 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Cohen, M. R. & Maunsell, J. H. Attention improves performance primarily by reducing interneuronal correlations. Nature Neurosci. 12, 1594–1600 (2009). This study provides experimental evidence showing the effects of attention on correlations between neurons and the consequent improvement on the amount of information carried by neuronal ensembles.

    Article  CAS  PubMed  Google Scholar 

  99. Mitchell, J. F., Sundberg, K. A. & Reynolds, J. H. Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. Neuron 63, 879–888 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Poort, J. & Roelfsema, P. R. Noise correlations have little influence on the coding of selective attention in area V1. Cereb. Cortex 19, 543–553 (2009).

    Article  PubMed  Google Scholar 

  101. Ramalingam, N., McManus, J. N. J., Li, W. & Gilbert, C. D. Top-down modulation of lateral interactions in visual cortex. J. Neurosci. 33, 1773–1789 (2013). The effective connectivity within a network of neurons in area V1 changes as the animal performs different perceptual tasks, therefore enabling neurons to select task relevant inputs. The contributions to the amount of task-relevant information come from the alteration in neuronal tuning and from changes in noise correlation over the population.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Cohen, M. R. & Newsome, W. T. Context-dependent changes in functional circuitry in visual area MT. Neuron 60, 162–173 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Jack, A. I., Shulman, G. L., Snyder, A. Z., McAvoy, M. & Corbetta, M. Separate modulations of human v1 associated with spatial attention and task structure. Neuron 51, 135–147 (2006).

    Article  CAS  PubMed  Google Scholar 

  104. Herrmann, K., Montaser-Kouhsari, L., Carrasco, M. & Heeger, D. J. When size matters: attention affects performance by contrast or response gain. Nature Neurosci. 13, 1554–1559 (2010).

    Article  CAS  PubMed  Google Scholar 

  105. Williford, T. & Maunsell, J. H. Effects of spatial attention on contrast response functions in macaque area V4. J. Neurophysiol. 96, 40–54 (2006).

    Article  PubMed  Google Scholar 

  106. Lee, J. & Maunsell, J. H. A normalization model of attentional modulation of single unit responses. PLoS ONE 4, e4651 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Reynolds, J. H. & Heeger, D. J. The normalization model of attention. Neuron 61, 168–185 (2009). Attentional effects are described here in terms of a process of normalization of responses to multiple stimuli within the visual field, which is also characterized in reference 37 as a bias in competitive interactions between stimuli.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Murray, S. O. & Wojciulik, E. Attention increases neural selectivity in the human lateral occipital complex. Nature Neurosci. 7, 70–74 (2004).

    Article  CAS  PubMed  Google Scholar 

  109. Gilbert, C. D. & Sigman, M. Brain states: top-down influences in sensory processing. Neuron 54, 677–696 (2007).

    Article  CAS  PubMed  Google Scholar 

  110. Field, D. J., Hayes, A. & Hess, R. F. Contour integration by the human visual system: evidence for a local “association field”. Vision Res. 33, 173–193 (1993). Reflecting the Gestalt rule of good continuation, psychophysical studies demonstrate the existence of an association field that mediates the linkage of contour elements and confers contours with perceptual saliency. The substrate for this association field may be found in area V1 (see references 6, 11, 12, 14 and 115).

    Article  CAS  PubMed  Google Scholar 

  111. Gilbert, C. D. & Wiesel, T. N. Morphology and intracortical projections of functionally characterised neurones in the cat visual cortex. Nature 280, 120–125 (1979).

    Article  CAS  PubMed  Google Scholar 

  112. Gilbert, C. D. & Wiesel, T. N. Clustered intrinsic connections in cat visual cortex. J. Neurosci. 3, 1116–1133 (1983).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Rockland, K. S. & Lund, J. S. Widespread periodic intrinsic connections in the tree shrew visual cortex. Science 215, 1532–1534 (1982).

    Article  CAS  PubMed  Google Scholar 

  114. Rockland, K. S. & Lund, J. S. Intrinsic laminar lattice connections in primate visual cortex. J. Comp. Neurol. 216, 303–318 (1983).

    Article  CAS  PubMed  Google Scholar 

  115. Ts'o, D. Y., Gilbert, C. D. & Wiesel, T. N. Relationships between horizontal interactions and functional architecture in cat striate cortex as revealed by cross-correlation analysis. J. Neurosci. 6, 1160–1170 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Bosking, W. H., Zhang, Y., Schofield, B. & Fitzpatrick, D. Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. J. Neurosci. 17, 2112–2127 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Chalk, M. et al. Attention reduces stimulus-driven gamma frequency oscillations and spike field coherence in V1. Neuron 66, 114–125 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Haynes, J. D., Tregellas, J. & Rees, G. Attentional integration between anatomically distinct stimulus representations in early visual cortex. Proc. Natl Acad. Sci. USA 102, 14925–14930 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Castelo-Branco, M., Goebel, R., Neuenschwander, S. & Singer, W. Neural synchrony correlates with surface segregation rules. Nature 405, 685–689 (2000).

    Article  CAS  PubMed  Google Scholar 

  120. Gail, A., Brinksmeyer, H. J. & Eckhorn, R. Contour decouples gamma activity across texture representation in monkey striate cortex. Cereb. Cortex 10, 840–850 (2000).

    Article  CAS  PubMed  Google Scholar 

  121. Kreiter, A. K. & Singer, W. Stimulus-dependent synchronization of neuronal responses in the visual cortex of the awake macaque monkey. J. Neurosci. 16, 2381–2396 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Singer, W. & Gray, C. M. Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci. 18, 555–586 (1995).

    Article  CAS  PubMed  Google Scholar 

  123. von der Malsburg, C. & Schneider, W. A neural cocktail-party processor. Biol. Cybern. 54, 29–40 (1986).

    Article  CAS  PubMed  Google Scholar 

  124. Eckhorn, R. et al. Coherent oscillations: a mechanism of feature linking in the visual cortex? Multiple electrode and correlation analyses in the cat. Biol. Cybern. 60, 121–130 (1988).

    Article  CAS  PubMed  Google Scholar 

  125. Gray, C. M., Konig, P., Engel, A. K. & Singer, W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989).

    Article  CAS  PubMed  Google Scholar 

  126. Roelfsema, P. R., Lamme, V. A. & Spekreijse, H. Synchrony and covariation of firing rates in the primary visual cortex during contour grouping. Nature Neurosci. 7, 982–991 (2004).

    Article  CAS  PubMed  Google Scholar 

  127. Palanca, B. J. & DeAngelis, G. C. Does neuronal synchrony underlie visual feature grouping? Neuron 46, 333–346 (2005).

    Article  CAS  PubMed  Google Scholar 

  128. Dong, Y., Mihalas, S., Qiu, F., von der Heydt, R. & Niebur, E. Synchrony and the binding problem in macaque visual cortex. J. Vis. 8, 30 (2008).

    Article  PubMed  Google Scholar 

  129. Lamme, V. A. & Spekreijse, H. Neuronal synchrony does not represent texture segregation. Nature 396, 362–366 (1998).

    Article  CAS  PubMed  Google Scholar 

  130. Fries, P., Reynolds, J. H., Rorie, A. E. & Desimone, R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560–1563 (2001).

    Article  CAS  PubMed  Google Scholar 

  131. Steinmetz, P. N. et al. Attention modulates synchronized neuronal firing in primate somatosensory cortex. Nature 404, 187–190 (2000).

    Article  CAS  PubMed  Google Scholar 

  132. Bland, B. H. & Oddie, S. D. Theta band oscillation and synchrony in the hippocampal formation and associated structures: the case for its role in sensorimotor integration. Behav. Brain Res. 127, 119–136 (2001).

    Article  CAS  PubMed  Google Scholar 

  133. Riehle, A., Grun, S., Diesmann, M. & Aertsen, A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278, 1950–1953 (1997).

    Article  CAS  PubMed  Google Scholar 

  134. Roelfsema, P. R., Engel, A. K., Konig, P. & Singer, W. Visuomotor integration is associated with zero time-lag synchronization among cortical areas. Nature 385, 157–161 (1997).

    Article  CAS  PubMed  Google Scholar 

  135. Womelsdorf, T., Fries, P., Mitra, P. P. & Desimone, R. Gamma-band synchronization in visual cortex predicts speed of change detection. Nature 439, 733–736 (2006).

    Article  CAS  PubMed  Google Scholar 

  136. Gregoriou, G. G., Gotts, S. J., Zhou, H. & Desimone, R. High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 324, 1207–1210 (2009). Attention affects long-range coupling between cortical areas. Also see reference 69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Fries, P., Womelsdorf, T., Oostenveld, R. & Desimone, R. The effects of visual stimulation and selective visual attention on rhythmic neuronal synchronization in macaque area V4. J. Neurosci. 28, 4823–4835 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Al-Aidroos, N., Said, C. P. & Turk-Browne, N. B. Top-down attention switches coupling between low-level and high-level areas of human visual cortex. Proc. Natl Acad. Sci. USA 109, 14675–14680 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Ress, D., Backus, B. T. & Heeger, D. J. Activity in primary visual cortex predicts performance in a visual detection task. Nature Neurosci. 3, 940–945 (2000).

    Article  CAS  PubMed  Google Scholar 

  140. Kastner, S., Pinsk, M. A., De Weerd, P., Desimone, R. & Ungerleider, L. G. Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Neuron 22, 751–761 (1999).

    Article  CAS  PubMed  Google Scholar 

  141. Rockland, K. S., Saleem, K. S. & Tanaka, K. Divergent feedback connections from areas V4 and TEO in the macaque. Vis. Neurosci. 11, 579–600 (1994).

    Article  CAS  PubMed  Google Scholar 

  142. Rockland, K. S. & Knutson, T. Feedback connections from area MT of the squirrel monkey to areas V1 and V2. J. Comp. Neurol. 425, 345–368 (2000).

    Article  CAS  PubMed  Google Scholar 

  143. Rockland, K. S. & Van Hoesen, G. W. Direct temporal-occipital feedback connections to striate cortex (V1) in the macaque monkey. Cereb. Cortex 4, 300–313 (1994).

    Article  CAS  PubMed  Google Scholar 

  144. Pascual-Leone, A. & Walsh, V. Fast backprojections from the motion to the primary visual area necessary for visual awareness. Science 292, 510–512 (2001).

    Article  CAS  PubMed  Google Scholar 

  145. Saalmann, Y. B., Pinsk, M. A., Wang, L., Li, X. & Kastner, S. The pulvinar regulates information transmission between cortical areas based on attention demands. Science 337, 753–756 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Sherman, S. M. & Guillery, R. W. Distinct functions for direct and transthalamic corticocortical connections. J. Neurophysiol. 106, 1068–1077 (2011).

    Article  PubMed  Google Scholar 

  147. Gilbert, C. D. in Principles of Neural Science 5th edn (eds Kandel, E. R., Schwartz, J., Jessel, T., Siegelbaum, S. A. & Hudspeth, A. J.) Ch. 25 (McGraw-Hill Companies, 2012).

    Google Scholar 

Download references

Acknowledgements

This work was supported by US National Institutes of Health grant EY007968 (C.D.G.), a grant from the James S. McDonnell Foundation (C.D.G.), the National Natural Science Foundation of China grant 31125014 (W.L.) and the 111 Project B07008 (W.L.).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Charles D. Gilbert.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

Related links

FURTHER INFORMATION

Charles D. Gilbert's homepage

Glossary

Re-entrant or feedback pathways

Processing strategy in which the product of an ongoing computation at one cortical level is analysed by the next level. The resultant information is then sent back to the initial level to influence its further computation. This is also sometimes referred to as countercurrent processing streams.

Visual cortical hierarchy

The hierarchy of cortical areas in the classical model of the cortical representation of visual information beginning with the primary visual cortex and ascending through two pathways: a ventral pathway extending into the temporal lobe, which is involved with object recognition, and a dorsal pathway extending into the parietal lobe, which is involved with visually directed movement and spatial attention.

Intermediate-level vision

Visual processing that involves contour integration and surface segmentation.

Distracters

In a complex visual scene, some objects are attended (the targets) and others (the distracters) are unattended, but the distracters can compete with the target for attentional resources.

Hemifield

One-half of the visual field.

Line label

The property or information represented by a neuron. Different neurons represent different values, and the strength of their firing indicates how close the stimulus is to that value.

Noise

The variability in neurons' responses to a given stimulus. If different neurons with similar functional properties have independent noise, an ensemble of such neurons can carry more information about a stimulus than a single neuron.

Local field potentials

(LFPs). The electrical fields generated by a population of neurons, with signals having components spanning a spectrum of frequencies. LFPs originate from the integrated currents coming from synaptic activation and from action potentials in dendrites, cell somata and axons.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gilbert, C., Li, W. Top-down influences on visual processing. Nat Rev Neurosci 14, 350–363 (2013). https://doi.org/10.1038/nrn3476

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrn3476

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing