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Dynamic predictions: Oscillations and synchrony in top–down processing

Nature Reviews Neuroscience volume 2, pages 704–716 (2001)Cite this article

Key Points

  • Classical theories of sensory processing view the brain as a passive, stimulus-driven device. Newer theories, by contrast, view perception as an active, selective process controlled by top–down influences that include expectations and predictions, derived from experience, attention and working memory.

  • The temporal binding model assumes that synchrony between distributed neurons is required for object representation, response selection, attention and sensorimotor integration. In a 'dynamicist' view of top–down influences on perception, synchrony generated intrinsically by interactions between higher and lower cortical areas could strongly influence perception, enhancing some representations and suppressing others.

  • The firing rate and temporal response properties of neurons can be altered by attentional processes, working memory and behavioural context. For example, attention can enhance synchrony and/or gamma-band oscillations in neurons representing the attended stimulus.

  • Spontaneous fluctuations in ongoing activity could represent not noise, but rather 'bias signals' that prime certain stimulus-evoked responses to allow rapid selection among inputs. Self-generated activity fluctuations during the preparatory period can also predict, for example, the direction, latency or speed of a movement.

  • Contextual modulation can arise from networks of frontal, parietal and limbic areas as well as from sensorimotor areas. These areas can represent information related to goal definition, action planning, working memory and selective attention. Assemblies of neuronal populations implementing these aspects of top–down modulation could then entrain assemblies involved in the representation of new stimuli.

  • A new concept of 'top–down' is proposed in which large-scale dynamics, expressing contextual influences and stored knowledge, can influence local processing. Rather than an anatomical hierarchy, this model proposes that any area could, in principle, modulate activity in any other area to which it is connected.

Abstract

Classical theories of sensory processing view the brain as a passive, stimulus-driven device. By contrast, more recent approaches emphasize the constructive nature of perception, viewing it as an active and highly selective process. Indeed, there is ample evidence that the processing of stimuli is controlled by top–down influences that strongly shape the intrinsic dynamics of thalamocortical networks and constantly create predictions about forthcoming sensory events. We discuss recent experiments indicating that such predictions might be embodied in the temporal structure of both stimulus-evoked and ongoing activity, and that synchronous oscillations are particularly important in this process. Coherence among subthreshold membrane potential fluctuations could be exploited to express selective functional relationships during states of expectancy or attention, and these dynamic patterns could allow the grouping and selection of distributed neuronal responses for further processing.

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Figure 1: Expectation-related synchrony in visual cortex.
Figure 2: Synchronization during movement preparation in the monkey.
Figure 3: Predictive power of ongoing oscillations.
Figure 4: Subthreshold oscillations can control spike synchrony in a feature-specific way.

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References

  1. Varela, F. J., Thompson, E. & Rosch, E. The Embodied Mind (MIT Press, Cambridge, Massachusetts,1991).

    Google Scholar 

  2. Ullman, S. in Representations of Vision (ed. Gorea, A.) 305–317 (Cambridge Univ. Press, Cambridge, UK, 1991).

    Google Scholar 

  3. Churchland, P. S., Ramachandran, V. S. & Sejnowski, T. J. in Large-Scale Neuronal Theories of the Brain (eds Koch, C. & Davis, J. L.) 23–60 (MIT Press, Cambridge, Massachusetts, 1994).

    Google Scholar 

  4. Aloimonos, Y. & Rosenfeld, A. Computer vision. Science 253, 1249–1254 (1991).

    Article  CAS  PubMed  Google Scholar 

  5. Hubel, D. H. & Wiesel, T. N. Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. J. Neurophysiol. 28, 229–289 (1965).

    Article  CAS  PubMed  Google Scholar 

  6. Marr, D. Vision (Freeman, San Francisco, 1982).

    Google Scholar 

  7. Biederman, I. Recognition-by-components: a theory of human image understanding. Psychol. Rev. 94, 115–147 (1987).

    Article  PubMed  Google Scholar 

  8. Thorpe, S., Fize, D. & Marlot, C. Speed of processing in the human visual system. Nature 381, 520–522 (1996).

    Article  CAS  PubMed  Google Scholar 

  9. Livingstone, M. & Hubel, D. Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science 240, 740–749 (1988).

    Article  CAS  PubMed  Google Scholar 

  10. DeYoe, E. A. & Van Essen, D. C. Concurrent processing streams in monkey visual cortex. Trends Neurosci. 11, 219–226 (1988).

    Article  CAS  PubMed  Google Scholar 

  11. Barlow, H. B. Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1, 371–394 (1972).

    Article  CAS  PubMed  Google Scholar 

  12. Edelman, G. M. The Remembered Present (Basic Books, New York, 1989).

    Google Scholar 

  13. Clark, A. An embodied cognitive science? Trends Cogn. Sci. 3, 345–351 (1999).

    Article  CAS  PubMed  Google Scholar 

  14. Port, R. F. & Van Gelder, T. (eds) Mind as Motion (MIT Press, Cambridge, Massachusetts, 1995).

    Google Scholar 

  15. Beer, R. D. Dynamical approaches to cognitive science. Trends Cogn. Sci. 4, 91–99 (2000).

    Article  CAS  PubMed  Google Scholar 

  16. 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 

  17. Engel, A. K. & Singer, W. Temporal binding and the neural correlates of sensory awareness. Trends Cogn. Sci. 5, 16–25 (2001).

    Article  PubMed  Google Scholar 

  18. Varela, F., Lachaux, J.-P., Rodriguez, E. & Martinerie, J. The brainweb: phase synchronization and large-scale integration. Nature Rev. Neurosci. 2, 229–239 (2001).

    Article  CAS  Google Scholar 

  19. Markman, A. B. & Dietrich, E. Extending the classical view of representation. Trends Cogn. Sci. 4, 470–475 (2000).

    Article  CAS  PubMed  Google Scholar 

  20. Rumelhart, D. E., McClelland, J. L. & the PDP Research Group (eds) Parallel Distributed Processing Vol. 1 (MIT Press, Cambridge, Massachusetts, 1986).

    Google Scholar 

  21. Hebb, D. O. The Organization of Behavior (Wiley, New York, 1949).

    Google Scholar 

  22. Grossberg, S. The link between brain learning, attention, and consciousness. Conscious. Cogn 8, 1–44 (1999).

    Article  CAS  PubMed  Google Scholar 

  23. Mumford, D. On the computational architecture of the neocortex. Biol. Cybern. 66, 241–251 (1992).

    Article  CAS  PubMed  Google Scholar 

  24. Ullman, S. Sequence seeking and counter streams: a computational model for bidirectional information flow in the visual cortex. Cereb. Cortex 5, 1–11 (1995).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  26. Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).

    Article  CAS  PubMed  Google Scholar 

  27. Schall, J. D. Neural basis of deciding, choosing and acting. Nature Rev. Neurosci. 2, 33–42 (2001).

    Article  CAS  Google Scholar 

  28. König, P. & Luksch, H. Active sensing — closing multiple loops. Z. Naturforsch. [C] 53, 542–549 (1998).

    Article  Google Scholar 

  29. Schultz, W. Multiple reward signals in the brain. Nature Rev. Neurosci. 1, 199–207 (2000).

    Article  CAS  Google Scholar 

  30. De Oliveira, S. C., Thiele, A. & Hoffmann, K. P. Synchronization of neuronal activity during stimulus expectation in a direction discrimination task. J. Neurosci. 17, 9248–9260 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Riehle, A., Gruen, S., Diesmann, M. & Aertsen, A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278, 1950–1953 (1997).A study describing synchrony among motor neurons during anticipation of a GO cue. Temporal correlation is enhanced at time points when the animal expects the cue to appear.

    Article  CAS  PubMed  Google Scholar 

  32. 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 

  33. Shulman, G. L. et al. Areas involved in encoding and applying directional expectations to moving objects. J. Neurosci. 19, 9480–9496 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Von Stein, A., Chiang, C. & König, P. Top–down processing mediated by interareal synchronization. Proc. Natl Acad. Sci. USA 97, 14748–14753 (2000).The paper describes synchrony between visual areas in awake cats during a GO/NO-GO task. Depending on the behavioural significance of the stimulus, interareal interactions occur in different frequency bands. Phase relations are compatible with top–down processing.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Breiter, H. C., Aharon, I., Kahneman, D., Dale, A. & Shizgal, P. Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron 30, 619–639 (2001).

    Article  CAS  PubMed  Google Scholar 

  36. Grossberg, S. How does the brain build a cognitive code? Psychol. Rev. 87, 1–51 (1980).

    Article  CAS  PubMed  Google Scholar 

  37. Fuster, J. M. The Prefrontal Cortex (Raven, New York, 1989).

    Google Scholar 

  38. Frith, C. & Dolan, R. J. Brain mechanisms associated with top–down processes in perception. Phil. Trans. R. Soc. Lond. B 352, 1221–1230 (1997).

    Article  CAS  Google Scholar 

  39. Miller, E. K. The prefrontal cortex and cognitive control. Nature Rev. Neurosci. 1, 59–65 (2000).

    Article  CAS  Google Scholar 

  40. Von der Malsburg, C. in Models of Neural Networks II (eds Domany, E., Van Hemmen, J. L. & Schulten, K.) 95–119 (Springer, Berlin, 1994).

    Book  Google Scholar 

  41. Engel, A. K., König, P., Kreiter, A. K., Schillen T. B. & Singer, W. Temporal coding in the visual cortex: new vistas on integration in the nervous system. Trends Neurosci. 15, 218–226 (1992).

    Article  CAS  PubMed  Google Scholar 

  42. Singer, W. Neuronal synchrony: a versatile code for the definition of relations? Neuron 24, 49–65 (1999).

    Article  CAS  PubMed  Google Scholar 

  43. Roelfsema, P. R., Engel, A. K., König, P. & Singer, W. The role of neuronal synchronization in response selection: a biologically plausible theory of structured representation in the visual cortex. J. Cogn. Neurosci. 8, 603–625 (1996).A model is presented of how synchrony could be relevant for dynamic response selection during sensorimotor processing.

    Article  CAS  PubMed  Google Scholar 

  44. Abeles, M. Role of the cortical neuron: integrator or coincidence detector? Isr. J. Med. Sci. 18, 83–92 (1982).

    CAS  PubMed  Google Scholar 

  45. König, P., Engel, A. K. & Singer, W. Integrator or coincidence detector? The role of the cortical neuron revisited. Trends Neurosci. 19, 130–137 (1996).

    Article  PubMed  Google Scholar 

  46. Alonso, J. M., Usrey, W. M. & Reid, R. C. Precisely correlated firing in cells of the lateral geniculate nucleus. Nature 383, 815–819 (1996).

    Article  CAS  PubMed  Google Scholar 

  47. Fries, P., Neuenschwander, S., Engel, A. K., Goebel, R. & Singer, W. Rapid feature selective neuronal synchronization through correlated latency shifting. Nature Neurosci. 4, 194–200 (2001).Study of first-spike synchrony observed between spatially separate neurons in cat visual cortex. Temporal patterning of ongoing activity in the epoch preceding the stimulus is shown to predict the latency correlation induced by stimulation.

    Article  CAS  PubMed  Google Scholar 

  48. Tallon-Baudry, C. & Bertrand, O. Oscillatory gamma activity in humans and its role in object representation. Trends Cogn. Sci. 3, 151–162 (1999).

    Article  CAS  PubMed  Google Scholar 

  49. Von Stein, A. & Sarnthein, J. Different frequencies for different scales of cortical integration: from local gamma to long-range alpha/theta synchronization. Int. J. Psychophysiol. 38, 301–313 (2001).

    Article  Google Scholar 

  50. Fries, P., Roelfsema, P. R., Engel, A. K., König, P. & Singer, W. Synchronization of oscillatory responses in visual cortex correlates with perception in interocular rivalry. Proc. Natl Acad. Sci. USA 94, 12699–12704 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Fries, P., Reynolds, J. H., Rorie, A. E. & Desimone, R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560–1563 (2001).This paper shows that in monkey V4, attention enhances the coupling between spikes and field potentials, reflecting a coherent local population, in a spatially selective manner. This effect occurs in ongoing activity during stimulus expectation.

    Article  CAS  PubMed  Google Scholar 

  52. Damasio, A. R. The brain binds entities and events by multiregional activation from convergence zones. Neural Comput. 1, 123–132 (1989).

    Article  Google Scholar 

  53. Damasio, A. R. Synchronous activation in multiple cortical regions: a mechanism for recall. Semin. Neurosci. 2, 287–296 (1990).

    Google Scholar 

  54. Tononi, G., Sporns, O. & Edelman, G. M. Reentry and the problem of integrating multiple cortical areas: simulation of dynamic integration in the visual system. Cereb. Cortex 2, 310–335 (1992).

    Article  CAS  PubMed  Google Scholar 

  55. Lumer, E. D., Edelman, G. M. & Tononi, G. Neural dynamics in a model of the thalamocortical system. I. Layers, loops and the emergence of fast synchronous rhythms. Cereb. Cortex 7, 207–227 (1997).

    Article  CAS  PubMed  Google Scholar 

  56. Koechlin, E., Anton, J. L. & Burnod, Y. Dynamical computational processing of local cortical networks for visual and motor processing: a bayesian framework. J. Physiol. (Paris) 90, 257–262 (1996).

    Article  CAS  Google Scholar 

  57. Kastner, S. & Ungerleider, L. G. Mechanisms of visual attention in the human cortex. Annu. Rev. Neurosci. 23, 315–341 (2000).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  59. Büchel, C. & Friston, K. J. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cereb. Cortex 7, 768–778 (1997).

    Article  PubMed  Google Scholar 

  60. Rowe, J. et al. Attention to action in Parkinson's disease: impaired effective connectivity among frontal cortical regions. Brain (in the press).

  61. Haenny, P., Maunsell, J. H. R. & Schiller, P. H. State dependent activity in monkey visual cortex. II. Retinal and extraretinal factors in V4. Exp. Brain Res. 69, 245–259 (1988).

    Article  CAS  PubMed  Google Scholar 

  62. Steinmetz, P. N. et al. Attention modulates synchronized neuronal firing in primate somatosensory cortex. Nature 404, 187–190 (2000).Among the first studies to show, at the cellular level, that attention modulates synchrony in neuronal assemblies.

    Article  CAS  PubMed  Google Scholar 

  63. Roelfsema, P. R., Engel, A. K., König, 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 

  64. Bernasconi, C., Von Stein, A., Chiang, C. & König, P. Bi-directional interactions between visual areas in the awake behaving cat. Neuroreport 11, 1–4 (2000).

    Article  Google Scholar 

  65. Tiitininen, H. et al. Selective attention enhances the auditory 40-Hz transient response in humans. Nature 364, 59–60 (1993).

    Article  Google Scholar 

  66. Tallon-Baudry, C., Bertrand, O., Peronnet, F. & Pernier, J. Oscillatory γ-band (30–70 Hz) activity induced by a visual search task in humans. J. Neurosci. 17, 722–734 (1997).The authors were among the first to show that induced (non-phase-locked) gamma-band oscillations can specifically reflect top–down processing in humans.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Gruber, T., Müller, M. M., Keil, A. & Elbert, T. Selective visual–spatial attention alters induced gamma band responses in the human EEG. Clin. Neurophysiol. 110, 2074–2085 (1999).

    Article  CAS  PubMed  Google Scholar 

  68. Müller, M. M., Gruber, T. & Keil, A. Modulation of induced gamma band activity in the human EEG by attention and visual information processing. Int. J. Psychophysiol. 38, 283–299 (2001).

    Article  Google Scholar 

  69. Rodriguez, E. et al. Perception's shadow: long-distance synchronization of human brain activity. Nature 397, 430–433 (1999).This and the next paper represent pioneering studies in which phase synchrony among EEG signals was analysed separately from changes in spectral power.

    Article  CAS  PubMed  Google Scholar 

  70. Miltner, W. H. R., Braun, C., Arnold, M., Witte, H. & Taub E. Coherence of gamma-band EEG activity as a basis for associative learning. Nature 397, 434–436 (1999).

    Article  CAS  PubMed  Google Scholar 

  71. Pulvermüller, F., Lutzenberger, W., Preissl, H. & Birbaumer, N. Spectral responses in the gamma-band: physiological signs of higher cognitive processes? Neuroreport 6, 2059–2064 (1995).

    Article  PubMed  Google Scholar 

  72. Müller, M. M., Keil, A., Gruber, T. & Elbert, T. Processing of affective picture modulates right-hemispheric gamma band EEG activity. Clin. Neurophysiol. 110, 1913–1920 (1999).

    Article  PubMed  Google Scholar 

  73. Karakas, S., Basar-Eroglu, C., Özesmi, C., Kafadar, H. & Erzengin, Ö. Ü. Gamma response of the brain: a multifunctional oscillation that represents bottom–up with top–down processing. Int. J. Psychophysiol. 39, 137–150 (2001).

    Article  CAS  PubMed  Google Scholar 

  74. Sarnthein, J., Rappelsberger, P., Shaw, G. L. & Von Stein, A. Synchronization between prefrontal and posterior association cortex during working memory tasks in humans. Proc. Natl Acad. Sci. USA 95, 7092–7096 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Goebel, R., Khorram-Sefat, D., Muckli, L., Hacker, H. & Singer, W. The constructive nature of vision: direct evidence from functional magnetic resonance imaging studies of apparent motion and motion imagery. Eur. J. Neurosci. 10, 1563–1573 (1998).

    Article  CAS  PubMed  Google Scholar 

  76. Dierks, T. et al. Activation of Heschl's gyrus during auditory hallucinations. Neuron 22, 615–621 (1999).

    Article  CAS  PubMed  Google Scholar 

  77. Shadlen, M. & Newsome, W. T. Noise, neural codes and cortical organization. Curr. Opin. Neurobiol. 4, 569–579 (1994).

    Article  CAS  PubMed  Google Scholar 

  78. Arieli, A., Shoham, D., Hildesheim, R. & Grinvald A. Coherent spatiotemporal patterns of ongoing activity revealed by real-time optical imaging coupled with single-unit recording in the cat visual cortex. J. Neurophysiol. 73, 2072–2093 (1995).

    Article  CAS  PubMed  Google Scholar 

  79. Arieli, A., Sterkin, A., Grinvald, A. & Aertsen, A. Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273, 1868–1871 (1996).

    Article  CAS  PubMed  Google Scholar 

  80. Tsodyks, M., Kenet, T., Grinvald, A. & Arieli, A. Linking spontaneous activity of single cortical neurons and the underlying functional architecture. Science 286, 1943–1946 (1999).A very important paper showing that patterning of ongoing activity is related to the functional architecture of the cortical network.

    Article  CAS  PubMed  Google Scholar 

  81. Bouyer, J. J., Montaron, M. F. & Rougeul, A. Fast fronto-parietal rhythms during combined focused attentive behaviour and immobility in the cat: cortical and thalamic localizations. Electroencephalogr. Clin. Neurophysiol. 51, 244–252 (1981).

    Article  CAS  PubMed  Google Scholar 

  82. Sheer, D. E. in Springer Series in Brain Dynamics 2 (eds Basar, E. & Bullock, T. M.) 339–374 (Springer, Berlin, 1989).

    Book  Google Scholar 

  83. Steriade, M., Amzica, F. & Contreras, D. Synchronization of fast (30–40 Hz) spontaneous cortical rhythms during brain activation. J. Neurosci. 16, 392–417 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Steriade, M., Contreras, D., Amzica, F. & Timofeev, I. Synchronization of fast (30–40 Hz) spontaneous oscillations in intrathalamic and thalamocortical networks. J. Neurosci. 16, 2788–2808 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Steriade, M. & Amzica, F. Intracortical and corticothalamic coherency of fast spontaneous oscillations. Proc. Natl Acad. Sci. USA 93, 2533–2538 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Munk, M. H. J., Roelfsema, P. R., König, P., Engel, A. K. & Singer, W. Role of reticular activation in the modulation of intracortical synchronization. Science 272, 271–274 (1996).

    Article  CAS  PubMed  Google Scholar 

  87. Bressler, S. L., Liang, H. & Ding, M. Top–down influence on early visual processing by an anticipatory large-scale network in macaque prefrontal cortex. Soc. Neurosci. Abstr. 27, 533.1 (2001).

    Google Scholar 

  88. Nelson, J. I., Salin, P. A., Munk, M. H. J., Arzi, M. & Bullier, J. Spatial and temporal coherence in cortico-cortical connections: a cross-correlation study in areas 17 and 18 in the cat. Vis. Neurosci. 9, 21–37 (1992).

    Article  CAS  PubMed  Google Scholar 

  89. Siegel, M., Körding, K. & König, P. Integrating top–down and bottom–up sensory processing by somato-dendritic interactions. J. Comput. Neurosci. 8, 161–173 (2000).A computational model that relates top–down processing to neural synchrony. It is suggested that neuronal bursting behaviour can implement the matching of bottom–up and top–down influences.

    Article  CAS  PubMed  Google Scholar 

  90. Lampl, I., Reichova, I. & Ferster, D. Synchronous membrane potential fluctuations in neurons of the cat visual cortex. Neuron 22, 361–374 (1999).

    Article  CAS  PubMed  Google Scholar 

  91. 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 

  92. 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 

  93. Wang, X.-J. Synaptic reverberations underlying mnemonic persistent activity. Trends Neurosci. 24, 455–463 (2001).

    Article  CAS  PubMed  Google Scholar 

  94. Hikosaka, O., Sakamoto, M. & Usui, S. Functional properties of monkey caudate neurons. III. Activities related to expectation of target and reward. J. Neurophysiol. 61, 814–832 (1989).

    Article  CAS  PubMed  Google Scholar 

  95. Riehle, A. & Requin, J. The predictive value for performance speed of preparatory changes in activity of the monkey motor and premotor cortex. Behav. Brain Res. 53, 35–49 (1993).

    Article  CAS  PubMed  Google Scholar 

  96. Vaadia, E., Kurata, K. & Wise, S. P. Neuronal activity preceding directional and nondirectional cues in the premotor cortex of rhesus monkeys. Somatosens. Mot. Res. 6, 207–230 (1988).

    Article  CAS  PubMed  Google Scholar 

  97. Crammond, D. J. & Kalaska, J. F. Prior information in motor and premotor cortex: activity during the delay period and effect on pre-movement activity. J. Neurophysiol. 84, 986–1005 (2000).

    Article  CAS  PubMed  Google Scholar 

  98. Bussaoud, D. Attention versus intention in the primate premotor cortex. Neuroimage 14, S40–45 (2001).

    Article  Google Scholar 

  99. Bruce, C. J. & Goldberg, M. E. Primate frontal eye fields. I. Single neurons discharging before saccades. J. Neurophysiol. 53, 603–635 (1985).

    Article  CAS  PubMed  Google Scholar 

  100. Dorris, M. C., Pare, M. & Munoz, D. P. Immediate neural plasticity shapes motor performance. J. Neurosci. 20, RC52, 1–5 (2000).

    Article  Google Scholar 

  101. Riehle, A., Grammont, F., Diesmann, M. & Grün, S. Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation. J. Physiol. (Paris) 94, 569–582 (2000).

    Article  CAS  Google Scholar 

  102. Sanes, J. N. & Donoghue, J. P. Oscillations in local field potentials of the primate motor cortex during voluntary movement. Proc. Natl Acad. Sci. USA 90, 4470–4474 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Donoghue, J. P., Sanes, J. N., Hatsopoulos, N. G. & Gaal, G. Neural discharge and local field potential oscillations in primate motor cortex during voluntary movements. J. Neurophysiol. 79, 159–173 (1998).

    Article  CAS  PubMed  Google Scholar 

  104. MacKay, W. A. & Mendonca, A. J. Field potential oscillatory bursts in parietal cortex before and during reach. Brain Res. 704, 167–174 (1995).

    Article  CAS  PubMed  Google Scholar 

  105. Bressler, S. L. & Kelso, J. A. S. Cortical coordination dynamics and cognition. Trends Cogn. Sci. 5, 26–36 (2001).

    Article  PubMed  Google Scholar 

  106. Jokeit, H. & Makeig, S. Different event-related patterns of γ-band power in brain waves of fast- and slow-reacting subjects. Proc. Natl Acad. Sci. USA 91, 6339–6343 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Adler, J. D. & Sifft, J. Alpha EEG and simple reaction time. Percept. Mot. Skills 52, 306 (1981).

    Article  CAS  PubMed  Google Scholar 

  108. Lampl, I. & Yarom, Y. Subthreshold oscillations of the membrane potential: a functional synchronizing and timing device. J. Neurophysiol. 70, 2181–2186 (1993).This and the subsequent paper are important in vitro studies showing that subthreshold oscillations of the membrane potential of a cell lead to latency shifts of inputs and, hence, provide precise temporal windows for creating synchronized patterns in neural activity.

    Article  CAS  PubMed  Google Scholar 

  109. Volgushev, M., Chistiakova, M. & Singer, W. Modification of discharge patterns of neocortical neurons by induced oscillations of the membrane potential. Neuroscience 83, 15–25 (1998).

    Article  CAS  PubMed  Google Scholar 

  110. Azouz, R. & Gray, C. M. Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo. Proc. Natl Acad. Sci. USA 97, 8110–8115 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Phillips, W. A. & Singer, W. In search of common foundations for cortical computation. Behav. Brain Sci. 20, 657–722 (1997).

    Article  CAS  PubMed  Google Scholar 

  112. Frith, C. D., Blakemore, S.-J. & Wolpert, D. M. Explaining the symptoms of schizophrenia: abnomalities in the awareness of action. Brain Res. Rev. 31, 357–363 (2000).

    Article  CAS  PubMed  Google Scholar 

  113. Driver, J. & Frith, C. Shifting baselines in attention research. Nature Rev. Neurosci. 1, 147–148 (2000).

    Article  CAS  Google Scholar 

  114. Siapas, A. G. & Wilson, M. A. Coordinated interactions between hippocampal ripples and cortical spindles during slow-wave sleep. Neuron 21, 1123–1128 (1998).

    Article  CAS  PubMed  Google Scholar 

  115. Palmer, S. E. Vision Science. Photons to Phenomenology (MIT Press, Cambridge, Massachusetts, 1999).

    Google Scholar 

  116. Han, S., He, X., Yund, W. & Woods, D. L. Attentional selection in the processing of hierarchical patterns: an ERP study. Biol. Psychol. 56, 113–130 (2001).

    Article  CAS  PubMed  Google Scholar 

  117. Rock, I. The Logic of Perception (MIT Press, Cambridge, Massachusetts, 1983).

    Google Scholar 

  118. Henle, M. Some new Gestalt psychologies. Psychol. Res. 51, 81–85 (1989).

    Article  Google Scholar 

  119. Haken, H. in Synergetics of Cognition (eds Haken, H. & Stadler, M.) 2–31 (Springer, Berlin, 1990).

    Google Scholar 

  120. Shepard, R. N. Mind Sights (Palgrave, London, 1990).

    Google Scholar 

Download references

Acknowledgements

We dedicate this article to the memory of Francisco Varela, whose work profoundly shaped our ideas about the dynamics of the embodied brain and continues to be a source of inspiration.

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Author notes

  1. Pascal Fries

    Present address: University of Nijmegen and F. C. Donders Centre for Cognitive Neuroimaging, Adelbertusplein 1, 6525EK, Nijmegen, The Netherlands

Authors and Affiliations

  1. Cellular Neurobiology Group, Institute for Medicine, Research Centre Jülich, Jülich, 52425, Germany

    Andreas K. Engel

  2. Laboratory of Neuropsychology, National Institute of Mental Health, 9000 Rockville Pike, Bethesda, 20892-4415, Maryland, USA

    Pascal Fries

  3. Department of Neurophysiology, Max-Planck-Institute for Brain Research, Deutschordenstrasse 46, Frankfurt, 60528, Germany

    Wolf Singer

Authors
  1. Andreas K. Engel
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  2. Pascal Fries
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  3. Wolf Singer
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Corresponding author

Correspondence to Andreas K. Engel.

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FURTHER INFORMATION

MIT Encyclopedia of Cognitive Sciences

attention

binding by neural synchrony

dynamic approaches to cognition

Gestalt perception

situatedness/embeddedness

top–down processing in vision

Max-Planck-Institute for Brain Research

Research Centre Jülich

Cognitive Science Network 2000

Glossary

HIERARCHY

A system of interconnected modules, in which 'higher' centres are activated later and contain more abstract representations than 'lower' areas.

ASSEMBLY

A spatially distributed set of cells that are activated in a coherent fashion and are part of the same representation.

BAYESIAN OPERATION

The estimation of conditional probabilities that can be used to quantify inferences about hypotheses, given certain input data. When implemented in a neural network, this can mean, for instance, that a neuron responds to feedforward input signals only if it previously received lateral inputs that convey an expectational bias from other neurons in the network.

VISUOMOTOR GO/NO-GO TASK

A task involving the control of behaviour by two alternative visual stimuli, one allowing and the other preventing a trained motor response.

INDUCED RHYTHMS

Oscillatory signals that are not phase-locked to the stimulus that is presented to the subject.

OPTICAL IMAGING

Recording of neural activity by measuring the optical properties of brain tissue, using either voltage-sensitive dyes or intrinsic signals related to the oxygen saturation of haemoglobin.

CORRELOGRAM

A histogram describing the time relation between two signals, in which a centre peak indicates synchrony and side peaks reflect oscillations.

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Engel, A., Fries, P. & Singer, W. Dynamic predictions: Oscillations and synchrony in top–down processing. Nat Rev Neurosci 2, 704–716 (2001). https://doi.org/10.1038/35094565

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