The role of alpha-rhythm states in perceptual learning: Insights from experiments and computational models

Frontiers in Computational Neuroscience

Volumn 8, Issue 1 APR, 2014, Pages

  Sigala, Rodrigo ^a,b   Haufe, Sebastian ^a,b   Roy, Dipanjan ^a,b   Dinse, Hubert R ^c   Ritter, Petra ^a,b,d,e  

^a CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

^b BERNSTEIN CENTER FOR COMPUTATIONAL NEUROSCIENCE   (Germany)

^c RUHR UNIVERSITY BOCHUM   (Germany)

^d MAX PLANCK INSTITUTE FOR HUMAN COGNITIVE AND BRAIN SCIENCES   (Germany)

^e HUMBOLDT UNIVERSITY   (Germany)

Author keywords

Alpha rhythm; Attention; Cognition; Large scale modeling; Learning; Memory; Oscillations

Indexed keywords

BIOELECTRIC POTENTIALS; DATA PROCESSING; DATA STORAGE EQUIPMENT;

ATTENTION; COGNITION; LARGE-SCALE MODELING; LEARNING; OSCILLATIONS;

BRAIN;

ALPHA RHYTHM; ATTENTION; BOLD SIGNAL; ELECTROENCEPHALOGRAM; HUMAN; INFORMATION PROCESSING; LEARNING; LONG TERM DEPRESSION; LONG TERM POTENTIATION; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NERVE CELL PLASTICITY; NONHUMAN; OSCILLATION; PERCEPTION; PERCEPTUAL LEARNING; REVIEW; SENSORY STIMULATION; SYNAPTIC TRANSMISSION;

EID: 84897930950     PISSN: 16625188     EISSN: None     Source Type: Journal    
DOI: 10.3389/fncom.2014.00036     Document Type: Review

References (265)

1. Aberg, K. C., and Herzog, M. H. (2012). About similar characteristics of visual perceptual learning and LTP. Vision Res. 61, 100-106. doi: 10.1016/j.visres.2011.12.013
2. Adini, Y., Sagi, D., and Tsodyks, M. (2002). Context-enabled learning in the human visual system. Nature 415, 790-793. doi: 10.1038/415790a
3. Ahissar, M., and Hochstein, S. (1993). Attentional control of early perceptual learning. Proc. Natl. Acad. Sci. U.S.A. 90, 5718-5722. doi: 10.1073/pnas.90.12.5718
4. Ahissar, M., and Hochstein, S. (2004). The reverse hierarchy theory of visual perceptual learning. Trends Cogn. Sci. 8, 457-464. doi: 10.1016/j.tics.2004.08.011
5. Albert, N. B., Robertson, E. M., and Miall, R. C. (2009). The resting human brain and motor learning. Curr. Biol. 19, 1023-1027. doi: 10.1016/j.cub.2009.04.028
6. Andersen, P., and Andersson, S. A. (1968). Physiological Basis of the Alpha Rhythm. New York, NY: Appleton-Century-Crofts.
7. Babiloni, C., Vecchio, F., Bultrini, A., Luca Romani, G., and Rossini, P. M. (2006). Pre- and poststimulus alpha rhythms are related to conscious visual perception: a high-resolution EEG study. Cereb. Cortex 16, 1690-1700. doi: 10.1093/cer-cor/bhj104
8. Baldassarre, A., Lewis, C. M., Committeri, G., Snyder, A. Z., Romani, G. L., and Corbetta, M. (2012). Individual variability in functional connectivity predicts performance of a perceptual task. Proc. Natl. Acad. Sci. U.S.A. 109, 3516-3521. doi: 10.1073/pnas.1113148109
9. Ball, K., and Sekuler, R. (1982). A specific and enduring improvement in visual motion discrimination. Science 218, 697-698. doi: 10.1126/science.7134968
10. Ball, K., and Sekuler, R. (1987). Direction-specific improvement in motion discrimination. Vision Res. 27, 953-965. doi: 10.1016/0042-6989(87)90011-3
11. Becker, R., Reinacher, M., Freyer, F., Villringer, A., and Ritter, P. (2011). How ongoing neuronal oscillations account for evoked fMRI variability. J. Neurosci. 31, 11016-11027. doi: 10.1523/JNEUROSCI.0210-11.2011
12. Becker, R., Ritter, P., and Villringer, A. (2008). Influence of ongoing alpha rhythm on the visual evoked potential. Neuroimage 39, 707-716. doi: 10.1016/j.neuroimage.2007.09.016
13. Becker, R., Ritter, P., and Villringer, A. (2009). "Visual system," in EEG-fMRI Physiology, Technique and Application, eds C. Mulert and L. Lemieux (Berlin, Heidelberg: Springer), 401-417.
14. Berger, H. (1929). Ueber das elektrenkephalogramm des menschen. Arch. Psychiatr. Nervenkr. 87, 527-570. doi: 10.1007/BF01797193
15. Beste, C., and Dinse, H. R. (2013). Learning without training. Curr. Biol. 23, 489-499. doi: 10.1016/j.cub.2013.04.044
16. Beste, C., Wascher, E., Güntürkün, O., and Dinse, H. R. (2011). Improvement and impairment of visually guided behavior through LTP- and LTD-like exposure-based visual learning. Curr. Biol. 21, 876-882. doi: 10.1016/j.cub.2011.03.065
17. Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kubler, A., et al. (1999). A spelling device for the paralysed. Nature 398, 297-298. doi: 10.1038/18581
18. Biswal, B., Yetkin, F. Z., Haughton, V. M., and Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537-541. doi: 10.1002/mrm.1910340409
19. Bliss, T. V., and Gardner-Medwin, A. R. (1973). Long-lasting potentiation of synap-tic transmission in the dentate area of the unanaestetized rabbit following stimulation of the perforant path. J. Physiol. 232, 357-374.
20. Bliss, T. V., and Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 232, 331-356.
21. Bollimunta, A., Chen, Y., Schroeder, C. E., and Ding, M. (2008). Neuronal mechanisms of cortical alpha oscillations in awake-behaving macaques. J. Neurosci. 28, 9976-9988. doi: 10.1523/JNEUROSCI.2699-08.2008
22. Bollimunta, A., Mo, J., Schroeder, C. E., and Ding, M. (2011). Neuronal mechanisms and attentional modulation of corticothalamic alpha oscillations. J. Neurosci. 31, 4935-4943. doi: 10.1523/JNEUROSCI.5580-10.2011
23. Braun, J. (1998). Vision and attention: the role of training. Nature 393, 424-425. doi: 10.1038/30875
24. Busch, N. A., Dubois, J., and Vanrullen, R. (2009). The phase of ongoing EEG oscillations predicts visual perception. J. Neurosci. 29, 7869-7876. doi: 10.1523/JNEUROSCI.0113-09.2009
25. Busch, N. A., and Vanrullen, R. (2010). Spontaneous EEG oscillations reveal periodic sampling of visual attention. Proc. Natl. Acad. Sci. U.S.A. 107, 16048-16053. doi: 10.1073/pnas.1004801107
26. Buzsaki, G. (2006). Rhythms of the Brain. New York, NY: Oxford University Press. doi: 10.1093/acprof:oso/9780195301069.001.0001
27. Callaway, E. M. (1998). Local circuits in primary visual cortex of the macaque monkey. Annu. Rev. Neurosci. 21, 47-74. doi: 10.1146/annurev.neuro.21.1.47
28. Callaway, E., and Yeager, C. L. (1960). Relationship between reaction time and electroencephalographic alpha phase. Science 132, 1765-1766. doi: 10.1126/sci-ence.132.3441.1765
29. Caporale, N., and Dan, Y. (2008). Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25-46. doi: 10.1146/annurev.neuro.31.060407.125639
30. Carmel, D., and Carrasco, M. (2008). Perceptual learning and dynamic changes in primary visual cortex. Neuron 57, 799-801. doi: 10.1016/j.neuron.2008.03.009
31. Caroni, P., Donato, F., and Muller, D. (2012). Structural plasticity upon learning: regulation and functions. Nat. Rev. Neurosci. 13, 478-490. doi: 10.1038/nrn3258
32. Casco, C., Campana, G., Grieco, A., and Fuggetta, G. (2004). Perceptual learning modulates electrophysiological and psychophysical response to visual texture segmentation in humans. Neurosci. Lett. 371, 18-23. doi: 10.1016/j.neulet.2004.08.005
33. Castro-Alamancos, M. A., and Rigas, P. (2002). Synchronized oscillations caused by disinhibition in rodent neocortex are generated by recurrent synaptic activity mediated by AMPA receptors. J. Physiol. 542, 567-581. doi: 10.1113/jphys-iol.2002.019059
34. Citri, A., and Malenka, R. C. (2008). Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacol 33, 18-41. doi: 10.1038/sj.npp.1301559
35. Da Silva, F. H., Van Lierop, T. H., Schrijer, C. F., and Van Leeuwen, W. S. (1973). Organization of thalamic and cortical alpha rhythms: spectra and coherences. Electroencephalogr. Clin. Neurophysiol. 35, 627-639. doi: 10.1016/0013-4694(73)90216-2
36. David, O., Cosmelli, D., and Friston, K. J. (2004). Evaluation of different measures of functional connectivity using a neural mass model. Neuroimage 21, 659-673. doi: 10.1016/j.neuroimage.2003.10.006
37. David, O., and Friston, K. J. (2003). A neural mass model for MEG/EEG: coupling and neuronal dynamics. Neuroimage 20, 1743-1755. doi: 10.1016/j.neuroimage.2003.07.015
38. Decharms, R. C. (2008). Applications of real-time fMRI. Nat. Rev. Neurosci. 9, 720-729. doi: 10.1038/nrn2414
39. Deco, G., and Jirsa, V. K. (2012). Ongoing cortical activity at rest: criti-cality, multistability, and ghost attractors. J. Neurosci. 32, 3366-3375. doi: 10.1523/JNEUROSCI.2523-11.2012
40. Deco, G., Jirsa, V., McIntosh, A., Sporns, O., and Kötter, R. (2009). Key role of coupling, delay, and noise in resting brain fluctuations. Proc. Natl. Acad. Sci. U.S.A. 106, 10302-10307. doi: 10.1073/pnas.0901831106
41. De Munck, J. C., Goncalves, S. I., Huijboom, L., Kuijer, J. P., Pouwels, P. J., Heethaar, R. M., et al. (2007). The hemodynamic response of the alpha rhythm: an EEG/fMRI study. Neuroimage 35, 1142-1151. doi: 10.1016/j.neuroimage.2007.01.022
42. Difrancesco, M. W. (2008). Simultaneous EEG/functional magnetic resonance imaging at 4 Tesla: correlates of brain activity to spontaneous alpha rhythm during relaxation. J. Clin. Neurophysiol. 25, 255-264. doi: 10.1097/WNP.0b013e3181879d56
43. Dinse, H. R., Kattenstroth, J. C., Gatica Tossi, M. A., Tegenthoff, M., and Kalisch, K. (2011). "Sensory stimulation for augmenting perception, sensorimotor behaviour and cognition," in Augmenting Cognition, eds H. Markram and I. Segev (Lausanne: EPFL Press), 11-39.
44. Ditye, T., Kanai, R., Bahrami, B., Muggleton, N. G., Rees, G., and Walsh, V. (2013). Rapid changes in brain structure predict improvements induced by perceptual learning. Neuroimage 81, 205-212. doi: 10.1016/j.neuroimage.2013. 05.058
45. Dorais, A., and Sagi, D. (1997). Contrast masking effects change with practice. Vision Res. 37, 1725-1733. doi: 10.1016/S0042-6989(96)00329-X
46. Dosher, B. A., and Lu, Z. L. (1998). Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proc. Natl. Acad. Sci. U.S.A. 95, 13988-13993. doi: 10.1073/pnas.95.23.13988
47. Dosher, B. A., and Lu, Z. L. (1999). Mechanisms of perceptual learning. Vision Res. 39, 3197-3221. doi: 10.1016/S0042-6989(99)00059-0
48. Dugue, L., Marque, P., and Vanrullen, R. (2011). The phase of ongoing oscillations mediates the causal relation between brain excitation and visual perception. J. Neurosci. 31, 11889-11893. doi: 10.1523/JNEUROSCI.1161-11.2011
49. Dustman, R. E., and Beck, E. C. (1965). Phase of alpha brain waves, reaction time and visually evoked potentials. Electroencephalogr. Clin. Neurophysiol. 18, 433-440. doi: 10.1016/0013-4694(65)90123-9
50. Eckstein, M. P., Abbey, C. K., Pham, B. T., and Shimozaki, S. S. (2004). Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner. J. Vis. 4, 1006-1019. doi: 10.1167/4.12.3
51. Engel, A. K., Fries, P., and Singer, W. (2001). Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704-716. doi: 10.1038/35094565
52. Ergenoglu, T., Demiralp, T., Bayraktaroglu, Z., Ergen, M., Beydagi, H., and Uresin, Y. (2004). Alpha rhythm of the EEG modulates visual detection performance in humans. Brain Res. Cogn. Brain Res. 20, 376-383. doi: 10.1016/j.cogbrainres.2004.03.009
53. Fahle, M., and Morgan, M. (1996). No transfer of perceptual learning between similar stimuli in the same retinal position. Curr. Biol. 6, 292-297. doi: 10.1016/S0960-9822(02)00479-7
54. Fahle, M., and Poggio, T. (2002). Perceptual Learning. Cambridge, MA: The MIT Press.
55. Feige, B., Scheffler, K., Esposito, F., Di Salle, F., Hennig, J., and Seifritz, E. (2005). Cortical and subcortical correlates of electroencephalographic alpha rhythm modulation. J. Neurophysiol. 93, 2864-2872. doi: 10.1152/jn.00721.2004
56. Flint, A. C., and Connors, B. W. (1996). Two types of network oscillations in neocortex mediated by distinct glutamate receptor subtypes and neuronal populations. J. Neurophysiol. 75, 951-957.
57. Fox, M. D., and Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700-711. doi: 10.1038/nrn2201
58. Fox, M. D., Snyder, A. Z., Vincent, J. L., and Raichle, M. E. (2007). Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior. Neuron 56, 171-184. doi: 10.1016/j.neuron.2007.08.023
59. Foxe, J. J., Simpson, G. V., and Ahlfors, S. P. (1998). Parietooccipital ~10Hz activity reflects anticipatory state of visual attention mechanisms. Neuroreport 9, 3929-3933. doi: 10.1097/00001756-199812010-00030
60. Freeman, W. (1977). Mass Action in the Nervous System: Examination of the Neurophysiological Basis of Adaptive Behavior Through the EEG. New York, NY: Academic Press.
61. Freeman, W. J. (1978). Models of the dynamics of neural populations. Electroencephalogr. Clin. Neurophysiol. Suppl. 34, 9-18.
62. Freyer, F., Aquino, K., Robinson, P. A., Ritter, P., and Breakspear, M. (2009a). Bistability and non-Gaussian fluctuations in spontaneous cortical activity. J. Neurosci. 29, 8512-8524. doi: 10.1523/JNEUROSCI.0754-09.2009
63. Freyer, F., Becker, R., Anami, K., Curio, G., Villringer, A., and Ritter, P. (2009b). Ultrahigh-frequency EEG during fMRI: pushing the limits of imaging-artifact correction. Neuroimage 48, 94-108. doi: 10.1016/j.neuroimage.2009.06.022
64. Freyer, F., Becker, R., Dinse, H. R., and Ritter, P. (2013). State-dependent perceptual learning. J. Neurosci. 33, 2900-2907. doi: 10.1523/JNEUROSCI.4039-12.2013
65. Freyer, F., Reinacher, M., Nolte, G., Dinse, H. R., and Ritter, P. (2012a). Repetitive tactile stimulation changes resting-state functional connectivity-implications for treatment of sensorimotor decline. Front. Hum. Neurosci. 6:144. doi: 10.3389/fnhum.2012.00144
66. Freyer, F., Roberts, J. A., Becker, R., Robinson, P. A., Ritter, P., and Breakspear, M. (2011). Biophysical mechanisms of multistability in resting-state cortical rhythms. J. Neurosci. 31, 6353-6361. doi: 10.1523/JNEUROSCI.6693-10.2011
67. Freyer, F., Roberts, J. A., Ritter, P., and Breakspear, M. (2012b). A canonical model of multistability and scale-invariance in biological systems. PLoS Comput. Biol. 8:e1002634. doi: 10.1371/journal.pcbi.1002634
68. Furmanski, C. S., Schluppeck, D., and Engel, S. A. (2004). Learning strengthens the response of primary visual cortex to simple patterns. Curr. Biol. 14, 573-578. doi: 10.1016/j.cub.2004.03.032
69. Gastaut, H. (1952). Etude electrocorticographique de la reactivite des rhythms rolandiques. Rev. Neurol. (Paris) 87, 176-182.
70. Ghosh, A., Rho, Y., McIntosh, A. R., Kotter, R., and Jirsa, V. K. (2008). Noise during rest enables the exploration of the brain's dynamic repertoire. PLoS Comput. Biol. 4:e1000196. doi: 10.1371/journal.pcbi.1000196
71. Gilbert, C. D., Li, W., and Piech, V. (2009). Perceptual learning and adult cortical plasticity. J. Physiol. 587, 2743-2751. doi: 10.1113/jphysiol.2009.171488
72. Gilbert, C. D., Sigman, M., and Crist, R. E. (2001). The neural basis of perceptual learning. Neuron 31, 681-697. doi: 10.1016/S0896-6273(01)00424-X
73. Goldman, R. I., Stern, J. M., Engel, J. Jr., and Cohen, M. S. (2002). Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport 13, 2487-2492. doi: 10.1097/00001756-200212200-00022
74. Goncalves, S. I., De Munck, J. C., Pouwels, P. J., Schoonhoven, R., Kuijer, J. P., Maurits, N. M., et al. (2006). Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: inter-subject variability. Neuroimage 30, 203-213. doi: 10.1016/j.neuroimage.2005.09.062
75. Gregoriou, G. G., Gotts, S. J., Zhou, H., and Desimone, R. (2009). High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 324, 1207-1210. doi: 10.1126/science.1171402
76. Gruzelier, J., Hardman, E., Wild, J., and Zaman, R. (1999). Learned control of slow potential interhemispheric asymmetry in schizophrenia. Int. J. Psychophysiol. 34, 341-348. doi: 10.1016/S0167-8760(99)00091-4
77. Gutnisky, D. A., Hansen, B. J., Iliescu, B. F., and Dragoi, V. (2009). Attention alters visual plasticity during exposure-based learning. Curr. Biol. 19, 555-560. doi: 10.1016/j.cub.2009.01.063
78. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., et al. (2008). Mapping the structural core of human cerebral cortex. PLoS Biol. 6:e159. doi: 10.1371/journal.pbio.0060159
79. Haider, B., Duque, A., Hasenstaub, A. R., and McCormick, D. A. (2006). Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. J. Neurosci. 26, 4535-4545. doi: 10.1523/JNEUROSCI.5297-05.2006
80. Hamame, C. M., Cosmelli, D., Henriquez, R., and Aboitiz, F. (2011). Neural mechanisms of human perceptual learning: electrophysiological evidence for a two-stage process. PLoS ONE 6:e19221. doi: 10.1371/journal.pone.0019221
81. Hampson, M., Driesen, N. R., Skudlarski, P., Gore, J. C., and Constable, R. T. (2006). Brain connectivity related to working memory performance. J. Neurosci. 26, 13338-13343. doi: 10.1523/JNEUROSCI.3408-06.2006
82. Handel, B. F., Haarmeier, T., and Jensen, O. (2011). Alpha oscillations correlate with the successful inhibition of unattended stimuli. J. Cogn. Neurosci. 23, 2494-2502. doi: 10.1162/jocn.2010.21557
83. Hanslmayr, S., Aslan, A., Staudigl, T., Klimesch, W., Herrmann, C. S., and Bäuml, K.-H. (2007). Prestimulus oscillations predict visual perception performance between and within subjects. Neuroimage 37, 1465-1473. doi: 10.1016/j.neuroimage.2007.07.011
84. Hanslmayr, S., Gross, J., Klimesch, W., and Shapiro, K. L. (2011). The role of alpha oscillations in temporal attention. Brain Res. Rev. 67, 331-343. doi: 10.1016/j.brainresrev.2011.04.002
85. Hanslmayr, S., Sauseng, P., Doppelmayr, M., Schabus, M., and Klimesch, W. (2005). Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Appl. Psychophysiol. Biofeedback 30, 1-10. doi: 10.1007/s10484-005-2169-8
86. Hardt, J. V. (1978). Anxiety change through electroencephalographic alpha feedback seen only in high anxiety subjects. Science 201, 79-81. doi: 10.1126/sci-ence.663641
87. Harmelech, T., and Malach, R. (2013). Neurocognitive biases and the patterns of spontaneous correlations in the human cortex. Trends Cogn. Sci. 17, 606-615. doi: 10.1016/j.tics.2013.09.014
88. Herzog, M. H., Aberg, K. C., Fremaux, N., Gerstner, W., and Sprekeler, H. (2012). Perceptual learning, roving and the unsupervised bias. Vision Res. 61, 95-99. doi: 10.1016/j.visres.2011.11.001
89. Herzog, M. H., and Fahle, M. (1997). The role of feedback in learning a vernier discrimination task. Vision Res. 37, 2133-2141. doi: 10.1016/S0042-6989(97)00043-6
90. Herzog, M. H., and Fahle, M. (1999). Effects of biased feedback on learning and deciding in a vernier discrimination task. Vision Res. 39, 4232-4243. doi: 10.1016/S0042-6989(99)00138-8
91. Honey, C. J., Kotter, R., Breakspear, M., and Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. U.S.A. 104, 10240-10245. doi: 10.1073/pnas.0701519104
92. Hosaka, R., Araki, O., and Ikeguchi, T. (2008). STDP provides the substrate for igniting synfire chains by spatiotemporal input patterns. Neural Comput. 20, 415-435. doi: 10.1162/neco.2007.11-05-043
93. Huber, R., Ghilardi, M. F., Massimini, M., and Tononi, G. (2004). Local sleep and learning. Nature 430, 78-81. doi: 10.1038/nature02663
94. Hughes, S. W., and Crunelli, V. (2005). Thalamic mechanisms of EEG alpha rhythms and their pathological implications. Neuroscientist 11, 357-372. doi: 10.1177/1073858405277450
95. Hughes, S. W., Lorincz, M. L., Blethyn, K., Kekesi, K. A., Juhasz, G., Turmaine, M., et al. (2011). Thalamic gap junctions control local neuronal synchrony and influence macroscopic oscillation amplitude during EEG alpha rhythms. Front. Psychol. 2:193. doi: 10.3389/fpsyg.2011.00193
96. Hussain, Z., McGraw, P. V., Sekuler, A. B., and Bennett, P. J. (2012). The rapid emergence of stimulus specific perceptual learning. Front. Psychol. 3:226. doi: 10.3389/fpsyg.2012.00226
97. Hussain, Z., Sekuler, A. B., and Bennett, P. J. (2011). Superior identification of familiar visual patterns a year after learning. Psychol. Sci. 22, 724-730. doi: 10.1177/0956797611409591
98. Isoglu-Alkac, U., Basar-Eroglu, C., Ademoglu, A., Demiralp, T., Miener, M., and Stadler, M. (2000). Alpha activity decreases during the perception of Necker cube reversals: an application of wavelet transform. Biol. Cybern. 82, 313-320. doi: 10.1007/s004220050585
99. Isoglu-Alkac, U., and Struber, D. (2006). Necker cube reversals during long-term EEG recordings: sub-bands of alpha activity. Int. J. Psychophysiol. 59, 179-189. doi: 10.1016/j.ijpsycho.2005.05.002
100. Ito, M., Westheimer, G., and Gilbert, C. D. (1998). Attention and perceptual learning modulate contextual influences on visual perception. Neuron 20, 1191-1197. doi: 10.1016/S0896-6273(00)80499-7
101. Jansen, B. H., and Rit, V. G. (1995). Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol. Cybern. 73, 357-366. doi: 10.1007/BF00199471
102. Jensen, O., and Mazaheri, A. (2010). Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front. Hum. Neurosci. 4:186. doi: 10.3389/fnhum.2010.00186
103. Jiang, X., Wang, G., Lee, A. J., Stornetta, R. L., and Zhu, J. J. (2013). The organization of two new cortical interneuronal circuits. Nat. Neurosci. 16, 210-218. doi: 10.1038/nn.3305
104. Jirsa, V. K., Sporns, O., Breakspear, M., Deco, G., and McIntosh, A. R. (2010). Towards the virtual brain: network modeling of the intact and the damaged brain. Arch. Ital. Biol. 148, 189-205.
105. Jones, E. G. (2002). Thalamic circuitry and thalamocortical synchrony. Philos. Trans. R. Soc. Lond. B Biol. Sci. 357, 1659-1673. doi: 10.1098/rstb.2002.1168
106. Jones, S. R., Pinto, D. J., Kaper, T. J., and Kopell, N. (2000). Alpha-frequency rhythms desynchronize over long cortical distances: a modeling study. J. Comput. Neurosci. 9, 271-291. doi: 10.1023/A:1026539805445
107. Jones, S. R., Pritchett, D. L., Stufflebeam, S. M., Hamalainen, M., and Moore, C. I. (2007). Neural correlates of tactile detection: a combined magnetoencephalog-raphy and biophysically based computational modeling study. J. Neurosci. 27, 10751-10764. doi: 10.1523/JNEUROSCI.0482-07.2007
108. Karameh, F. N., Dahleh, M. A., Brown, E. N., and Massaquoi, S. G. (2006). Modeling the contribution of lamina 5 neuronal and network dynamics to low frequency EEG phenomena. Biol. Cybern. 95, 289-310. doi: 10.1007/s00422-006-0090-8
109. Kayser, C., Montemurro, M. A., Logothetis, N. K., and Panzeri, S. (2009). Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron 61, 597-608. doi: 10.1016/j.neuron.2009. 01.008
110. Kelly, S. P., Lalor, E. C., Reilly, R. B., and Foxe, J. J. (2006). Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention. J. Neurophysiol. 95, 3844-3851. doi: 10.1152/jn.01234.2005
111. Klimesch, W. (2012). alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn. Sci. 16, 606-617. doi: 10.1016/j.tics.2012.10.007
112. Klimesch, W., Doppelmayr, M., Russegger, H., Pachinger, T., and Schwaiger, J. (1998). Induced alpha band power changes in the human EEG and attention. Neurosci. Lett. 244, 73-76. doi: 10.1016/S0304-3940(98)00122-0
113. Klimesch, W., Sauseng, P., and Hanslmayr, S. (2007). EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res. Rev. 53, 63-88. doi: 10.1016/j.brainresrev.2006.06.003
114. Klimesch, W., Schimke, H., and Pfurtscheller, G. (1993). Alpha frequency, cognitive load and memory performance. Brain Topogr. 5, 241-251. doi: 10.1007/BF01128991
115. Klimesch, W., Schimke, H., and Schwaiger, J. (1994). Episodic and semantic memory: an analysis in the EEG theta and alpha band. Electroencephalogr. Clin. Neurophysiol. 91, 428-441. doi: 10.1016/0013-4694(94)90164-3
116. Kotter, R. (2004). Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. Neuroinformatics 2, 127-144. doi: 10.1385/NI:2:2:127
117. Kuhlman, W. N. (1978). Functional topography of the human mu rhythm. Electroencephalogr. Clin. Neurophysiol. 44, 83-93. doi: 10.1016/0013-4694(78) 90107-4
118. Laconte, S. M. (2011). Decoding fMRI brain states in real-time. Neuroimage 56, 440-454. doi: 10.1016/j.neuroimage.2010.06.052
119. Landsness, E., Ferrarelli, F., Sarasso, S., Goldstein, M., Riedner, B., Cirelli, C., et al. (2011). Electrophysiological traces of visuomotor learning and their renormalization after sleep. Clin. Neurophysiol. 122, 2418-2425. doi: 10.1016/j.clinph.2011.05.001
120. Lange, J., Oostenveld, R., and Fries, P. (2013). Reduced occipital alpha power indexes enhanced excitability rather than improved visual perception. J. Neurosci. 33, 3212-3220. doi: 10.1523/JNEUROSCI.3755-12.2013
121. Laufs, H., Holt, J. L., Elfont, R., Krams, M., Paul, J. S., Krakow, K., et al. (2006). Where the BOLD signal goes when alpha EEG leaves. Neuroimage 31, 1408-1418. doi: 10.1016/j.neuroimage.2006.02.002
122. Laufs, H., Kleinschmidt, A., Beyerle, A., Eger, E., Salek-Haddadi, A., Preibisch, C., et al. (2003a). EEG-correlated fMRI of human alpha activity. Neuroimage 19, 1463-1476. doi: 10.1016/S1053-8119(03)00286-6
123. Laufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., Salek-Haddadi, A., et al. (2003b). Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. Proc. Natl. Acad. Sci. U.S.A. 100, 11053-11058. doi: 10.1073/pnas.1831638100
124. Law, C. T., and Gold, J. I. (2008). Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 11, 505-513. doi: 10.1038/nn2070
125. Lehtela, L., Salmelin, R., and Hari, R. (1997). Evidence for reactive magnetic 10Hz rhythm in the human auditory cortex. Neurosci. Lett. 222, 111-114. doi: 10.1016/S0304-3940(97)13361-4
126. Lewis, C. M., Baldassarre, A., Committeri, G., Romani, G. L., and Corbetta, M. (2009). Learning sculpts the spontaneous activity of the resting human brain. Proc. Natl. Acad. Sci. U.S.A. 106, 17558-17563. doi: 10.1073/pnas.0902455106
127. Linkenkaer-Hansen, K., Nikulin, V. V., Palva, S., Ilmoniemi, R. J., and Palva, J. M. (2004). Prestimulus oscillations enhance psychophysical performance in humans. J. Neurosci. 24, 10186-10190. doi: 10.1523/JNEUROSCI.2584-04.2004
128. Lopes Da Silva, F. H., Hoeks, A., Smits, H., and Zetterberg, L. H. (1974). Model of brain rhythmic activity. The alpha-rhythm of the thalamus. Kybernetik 15, 27-37. doi: 10.1007/BF00270757
129. Lopes Da Silva, F. H., and Storm Van Leeuwen, W. (1977). The cortical source of the alpha rhythm. Neurosci. Lett. 6, 237-241. doi: 10.1016/0304-3940(77)90024-6
130. Lopes Da Silva, F. H., Vos, J. E., Mooibroek, J., and Van Rotterdam, A. (1980). Relative contributions of intracortical and thalamo-cortical processes in the generation of alpha rhythms, revealed by partial coherence analysis. Electroencephalogr. Clin. Neurophysiol. 50, 449-456. doi: 10.1016/0013-4694(80)90011-5
131. Lorincz, M. L., Kekesi, K. A., Juhasz, G., Crunelli, V., and Hughes, S. W. (2009). Temporal framing of thalamic relay-mode firing by phasic inhibition during the alpha rhythm. Neuron 63, 683-696. doi: 10.1016/j.neuron.2009.08.012
132. Lundqvist, M., Herman, P., and Lansner, A. (2013). Effect of prestimulus alpha power, phase, and synchronization on stimulus detection rates in a biophysical attractor network model. J. Neurosci. 33, 11817-11824. doi: 10.1523/JNEUROSCI.5155-12.2013
133. Lu, Z. L., Liu, J., and Dosher, B. A. (2010). Modeling mechanisms of perceptual learning with augmented Hebbian re-weighting. Vision Res. 50, 375-390. doi: 10.1016/j.visres.2009.08.027
134. Lynch, M. A. (2004). Long-term potentiation and memory. Physiol. Rev. 84, 87-136. doi: 10.1152/physrev.00014.2003
135. Ma, L., Narayana, S., Robin, D. A., Fox, P. T., and Xiong, J. (2011). Changes occur in resting state network of motor system during 4 weeks of motor skill learning. Neuroimage 58, 226-233. doi: 10.1016/j.neuroimage.2011.06.014
136. Maatta, S., Landsness, E., Sarasso, S., Ferrarelli, F., Ferreri, F., Ghilardi, M. F., et al. (2010). The effects of morning training on night sleep: a behavioral and EEG study. Brain Res. Bull. 82, 118-123. doi: 10.1016/j.brainresbull.2010.01.006
137. Malenka, R. C., and Bear, M. F. (2004). LTP and LTD: an embarrassment of riches. Neuron 44, 5-21. doi: 10.1016/j.neuron.2004.09.012
138. Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., and Corbetta, M. (2007). Electrophysiological signatures of resting state networks in the human brain. Proc. Natl. Acad. Sci. U.S.A. 104, 13170-13175. doi: 10.1073/pnas.0700668104
139. Masquelier, T., Guyonneau, R., and Thorpe, S. J. (2008). Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS ONE 3:e1377. doi: 10.1371/journal.pone.0001377
140. Masquelier, T., Hugues, E., Deco, G., and Thorpe, S. J. (2009). Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme. J. Neurosci. 29, 13484-13493. doi: 10.1523/JNEUROSCI.2207-09.2009
141. Mathes, B., Pomper, U., Walla, P., and Basar-Eroglu, C. (2010). Dissociation of reversal- and motor-related delta- and alpha-band responses during visual mul-tistable perception. Neurosci. Lett. 478, 14-18. doi: 10.1016/j.neulet.2010.04.057
142. Mathewson, K. E., Gratton, G., Fabiani, M., Beck, D. M., and Ro, T. (2009). To see or not to see: prestimulus alpha phase predicts visual awareness. J. Neurosci. 29, 2725-2732. doi: 10.1523/JNEUROSCI.3963-08.2009
143. Mathewson, K. E., Lleras, A., Beck, D. M., Fabiani, M., Ro, T., and Gratton, G. (2011). Pulsed out of awareness: EEG alpha oscillations represent a pulsed-inhibition of ongoing cortical processing. Front. Psychol. 2:99. doi: 10.3389/fpsyg.2011.00099
144. Mayhew, S. D., Li, S., and Kourtzi, Z. (2012). Learning acts on distinct processes for visual form perception in the human brain. J. Neurosci. 32, 775-786. doi: 10.1523/JNEUROSCI.2033-11.2012
145. McMahon, D. B., and Leopold, D. A. (2012). Stimulus timing-dependent plasticity in high-level vision. Curr. Biol. 22, 332-337. doi: 10.1016/j.cub.2012.01.003
146. Mennes, M., Zuo, X. N., Kelly, C., Di Martino, A., Zang, Y. F., Biswal, B., et al. (2011). Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics. Neuroimage 54, 2950-2959. doi: 10.1016/j.neuroimage.2010.10.046
147. Mo, J., Schroeder, C. E., and Ding, M. (2011). Attentional modulation of alpha oscillations in macaque inferotemporal cortex. J. Neurosci. 31, 878-882. doi: 10.1523/JNEUROSCI.5295-10.2011
148. Montemurro, M. A., Rasch, M. J., Murayama, Y., Logothetis, N. K., and Panzeri, S. (2008). Phase-of-firing coding of natural visual stimuli in primary visual cortex. Curr. Biol. 18, 375-380. doi: 10.1016/j.cub.2008.02.023
149. Moosmann, M., Ritter, P., Krastel, I., Brink, A., Thees, S., Blankenburg, F., et al. (2003). Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy. Neuroimage 20, 145-158. doi: 10.1016/S1053-8119(03)00344-6
150. Mukai, I., Kim, D., Fukunaga, M., Japee, S., Marrett, S., and Ungerleider, L. G. (2007). Activations in visual and attention-related areas predict and correlate with the degree of perceptual learning. J. Neurosci. 27, 11401-11411. doi: 10.1523/JNEUROSCI.3002-07.2007
151. Murphy, M., Huber, R., Esser, S., Riedner, B. A., Massimini, M., Ferrarelli, F., et al. (2011). The cortical topography of local sleep. Curr. Top. Med. Chem. 11, 2438-2446. doi: 10.2174/156802611797470303
152. Naruse, Y., Matani, A., Miyawaki, Y., and Okada, M. (2010). Influence of coherence between multiple cortical columns on alpha rhythm: a computational modeling study. Hum. Brain Mapp. 31, 703-715. doi: 10.1002/hbm.20899
153. Neuling, T., Rach, S., Wagner, S., Wolters, C. H., and Herrmann, C. S. (2012). Good vibrations: oscillatory phase shapes perception. Neuroimage 63, 771-778. doi: 10.1016/j.neuroimage.2012.07.024
154. Neymotin, S. A., Lee, H., Park, E., Fenton, A. A., and Lytton, W. W. (2011). Emergence of physiological oscillation frequencies in a computer model of neocortex. Front. Comput. Neurosci. 5:19. doi: 10.3389/fncom.2011.00019
155. Ng, B. S., Logothetis, N. K., and Kayser, C. (2013). EEG phase patterns reflect the selectivity of neural firing. Cereb. Cortex 23, 389-398. doi: 10.1093/cercor/bhs031
156. Nicoll, R. A., Kauer, J. A., and Malenka, R. C. (1988). The current excitement in long-term potentiation. Neuron 1, 97-103. doi: 10.1016/0896-6273(88)90193-6
157. Niedermeyer, E., and Da Silva, F. (2004). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Philadelphia, PA: Lippincott Williams & Wilkins.
158. Nikouline, V. V., Linkenkaer-Hansen, K., Wikstrom, H., Kesaniemi, M., Antonova, E. V., Ilmoniemi, R. J., et al. (2000). Dynamics of mu-rhythm suppression caused by median nerve stimulation: a magnetoencephalographic study in human subjects. Neurosci. Lett. 294, 163-166. doi: 10.1016/S0304-3940(00)01562-7
159. Nishina, S., Seitz, A. R., Kawato, M., and Watanabe, T. (2007). Effect of spatial distance to the task stimulus on task-irrelevant perceptual learning of static Gabors. J. Vis. 7, 2.1-2.10. doi: 10.1167/7.13.2
160. Nunez, P. L., and Silberstein, R. B. (2000). On the relationship of synaptic activity to macroscopic measurements: does co-registration of EEG with fMRI make sense? Brain Topogr. 13, 79-96. doi: 10.1023/A:1026683200895
161. O'connell, R. G., Dockree, P. M., Robertson, I. H., Bellgrove, M. A., Foxe, J. J., and Kelly, S. P. (2009). Uncovering the neural signature of lapsing attention: electrophysiological signals predict errors up to 20 s before they occur. J. Neurosci. 29, 8604-8611. doi: 10.1523/JNEUROSCI.5967-08.2009
162. Palva, S., Linkenkaer-Hansen, K., Naatanen, R., and Palva, J. M. (2005). Early neural correlates of conscious somatosensory perception. J. Neurosci. 25, 5248-5258. doi: 10.1523/JNEUROSCI.0141-05.2005
163. Pfurtscheller, G., and Berghold, A. (1989). Patterns of cortical activation during planning of voluntary movement. Electroencephalogr. Clin. Neurophysiol. 72, 250-258. doi: 10.1016/0013-4694(89)90250-2
164. Pfurtscheller, G., Stancák, A., and Neuper, C. (1996). Event-related synchronization (ERS) in the alpha band-an electrophysiological correlate of cortical idling: a review. Int. J. Psychophysiol. 24, 39-46. doi: 10.1016/S0167-8760(96)00066-9
165. Pincham, H. L., and Szucs, D. (2012). Conscious access is linked to ongoing brain state: electrophysiological evidence from the attentional blink. Cereb. Cortex 22, 2346-2353. doi: 10.1093/cercor/bhr314
166. Pleger, B., Dinse, H. R., Ragert, P., Schwenkreis, P., Malin, J. P., and Tegenthoff, M. (2001). Shifts in cortical representations predict human discrimination improvement. Proc. Natl. Acad. Sci. U.S.A. 98, 12255-12260. doi: 10.1073/pnas.191176298
167. Pleger, B., Foerster, A. F., Ragert, P., Dinse, H. R., Schwenkreis, P., Malin, J. P., et al. (2003). Functional imaging of perceptual learning in human primary and secondary somatosensory cortex. Neuron 40, 643-653. doi: 10.1016/S0896-6273(03)00677-9
168. Poggio, T., Fahle, M., and Edelman, S. (1992). Fast perceptual learning in visual hyperacuity. Science 256, 1018-1021. doi: 10.1126/science.1589770
169. Pourtois, G., Rauss, K. S., Vuilleumier, P., and Schwartz, S. (2008). Effects of perceptual learning on primary visual cortex activity in humans. Vision Res. 48, 55-62. doi: 10.1016/j.visres.2007.10.027
170. Ragert, P., Franzkowiak, S., Schwenkreis, P., Tegenthoff, M., and Dinse, H. R. (2008). Improvement of tactile perception and enhancement of cortical excitability through intermittent theta burst rTMS over human primary somatosensory cortex. Exp. Brain Res. 184, 1-11. doi: 10.1007/s00221-007-1073-2
171. Raichle, M. E. (2006). Neuroscience. The brain's dark energy Science 314, 1249-1250. doi: 10.1126/science. 1134405
172. Reinacher, M., Becker, R., Villringer, A., and Ritter, P. (2009). Oscillatory brain states interact with late cognitive components of the somatosensory evoked potential. J. Neurosci. Methods 183, 49-56. doi: 10.1016/j.jneumeth.2009.06.036
173. Rihs, T. A., Michel, C. M., and Thut, G. (2007). Mechanisms of selective inhibition in visual spatial attention are indexed by alpha-band EEG synchronization. Eur. J. Neurosci. 25, 603-610. doi: 10.1111/j.1460-9568.2007.05278.x
174. Rihs, T. A., Michel, C. M., and Thut, G. (2009). A bias for posterior alpha-band power suppression versus enhancement during shifting versus maintenance of spatial attention. Neuroimage 44, 190-199. doi: 10.1016/j.neuroimage.2008.08.022
175. Ritter, P., and Becker, R. (2009). Detecting alpha rhythm phase reset by phase sorting: caveats to consider. Neuroimage 47, 1-4. doi: 10.1016/j.neuroimage.2009.04.031
176. Ritter, P., Becker, R., Freyer, F., and Villringer, A. (2010). "EEG quality: the image acquisition artefact," in EEG-fMRI (Berlin, Heidelberg: Springer), 153-171.
177. Ritter, P., Freyer, F., Curio, G., and Villringer, A. (2008). High-frequency (600 Hz) population spikes in human EEG delineate thalamic and cortical fMRI activation sites. Neuroimage 42, 483-490. doi: 10.1016/j.neuroimage.2008. 05.026
178. Ritter, P., Moosmann, M., and Villringer, A. (2009). Rolandic alpha and beta EEG rhythms' strengths are inversely related to fMRI-BOLD signal in primary somatosensory and motor cortex. Hum. Brain Mapp. 30, 1168-1187. doi: 10.1002/hbm.20585
179. Ritter, P., Schirner, M., McIntosh, A. R., and Jirsa, V. K. (2013). The virtual brain integrates computational modeling and multimodal neuroimaging. Brain Connect. 3, 121-145. doi: 10.1089/brain.2012.0120
180. Ritter, P., and Villringer, A. (2002). Inhibition and functional magnetic resonance imaging. Int. Congr. Ser. 1235, 213-222. doi: 10.1016/S0531-5131(02)00189-9
181. Ritter, P., and Villringer, A. (2006). Simultaneous EEG-fMRI. Neurosci. Biobehav. Rev. 30, 823-838. doi: 10.1016/j.neubiorev.2006.06.008
182. Robinson, P. A., Rennie, C. J., and Wright, J. J. (1997). Propagation and stability of waves of electrical activity in the cerebral cortex. Phys. Rev. 56, 826-840.
183. Robinson, P. A., Rennie, C. J., Wright, J. J., Bahramali, H., Gordon, E., and Rowe, D. L. (2001). Prediction of electroencephalographic spectra from neurophysiology. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 63:021903. doi: 10.1103/PhysRevE. 63.021903
184. Roelfsema, P. R., Van Ooyen, A., and Watanabe, T. (2010). Perceptual learning rules based on reinforcers and attention. Trends Cogn. Sci. 14, 64-71. doi: 10.1016/j.tics.2009.11.005
185. Rohenkohl, G., and Nobre, A. C. (2011). Alpha oscillations related to anticipatory attention follow temporal expectations. J. Neurosci. 31, 14076-14084. doi: 10.1523/JNEUROSCI.3387-11.2011
186. Romei, V., Gross, J., and Thut, G. (2010). On the role of prestimulus alpha rhythms over occipito-parietal areas in visual input regulation: correlation or causation? J. Neurosci. 30, 8692-8697. doi: 10.1523/JNEUROSCI.0160-10.2010
187. Romei, V., Gross, J., and Thut, G. (2012). Sounds reset rhythms of visual cortex and corresponding human visual perception. Curr. Biol. 22, 807-813. doi: 10.1016/j.cub.2012.03.025
188. Ronnqvist, K. C., McAllister, C. J., Woodhall, G. L., Stanford, I. M., and Hall, S. D. (2013). A multimodal perspective on the composition of cortical oscillations. Front. Hum. Neurosci. 7:132. doi: 10.3389/fnhum.2013.00132
189. Ros, T., Munneke, M. A., Ruge, D., Gruzelier, J. H., and Rothwell, J. C. (2010). Endogenous control of waking brain rhythms induces neuroplasticity in humans. Eur. J. Neurosci. 31, 770-778. doi: 10.1111/j.1460-9568.2010.07100.x
190. Rosenthal, O., and Humphreys, G. W. (2010). Perceptual organization without perception. The subliminal learning of global contour. Psychol. Sci. 21, 1751-1758. doi: 10.1177/0956797610389188
191. Saalmann, Y. B., Pinsk, M. A., Wang, L., Li, X., and Kastner, S. (2012). The pulvinar regulates information transmission between cortical areas based on attention demands. Science 337, 753-756. doi: 10.1126/science.1223082
192. Sadaghiani, S., Scheeringa, R., Lehongre, K., Morillon, B., Giraud, A. L., and Kleinschmidt, A. (2010). Intrinsic connectivity networks, alpha oscillations, and tonic alertness: a simultaneous electroencephalography/functional magnetic resonance imaging study. J. Neurosci. 30, 10243-10250. doi: 10.1523/JNEUROSCI.1004-10.2010
193. Sagi, D. (2011). Perceptual learning in vision research. Vision Res. 51, 1552-1566. doi: 10.1016/j.visres.2010.10.019
194. Sagi, Y., Tavor, I., Hofstetter, S., Tzur-Moryosef, S., Blumenfeld-Katzir, T., and Assaf, Y. (2012). Learning in the fast lane: new insights into neuroplasticity. Neuron 73, 1195-1203. doi: 10.1016/j.neuron.2012.01.025
195. Sale, A., De Pasquale, R., Bonaccorsi, J., Pietra, G., Olivieri, D., Berardi, N., et al. (2011). Visual perceptual learning induces long-term potentiation in the visual cortex. Neuroscience 172, 219-225. doi: 10.1016/j.neuroscience.2010.10.078
196. Salinas, E., and Sejnowski, T. J. (2001). Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2, 539-550. doi: 10.1038/35086012
197. Salinas, E., and Thier, P. (2000). Gain modulation: a major computational principle of the central nervous system. Neuron 27, 15-21. doi: 10.1016/S0896-6273(00)00004-0
198. Salmelin, R., and Hari, R. (1994). Spatiotemporal characteristics of sensorimotor neuromagnetic rhythms related to thumb movement. Neuroscience 60, 537-550. doi: 10.1016/0306-4522(94)90263-1
199. Sanz Leon, P., Knock, S. A., Woodman, M. M., Domide, L., Mersmann, J., McIntosh, A. R., et al. (2013). The virtual brain: a simulator of primate brain network dynamics. Front. Neuroinform. 7:10. doi: 10.3389/fninf.2013.00010
200. Sasaki, Y., Nanez, J. E., and Watanabe, T. (2010). Advances in visual perceptual learning and plasticity. Nat. Rev. Neurosci. 11, 53-60. doi: 10.1038/nrn2737
201. Saxby, E., and Peniston, E. G. (1995). Alpha-theta brainwave neurofeedback training: an effective treatment for male and female alcoholics with depressive symptoms. J. Clin. Psychol. 51, 685-693.
202. Scheeringa, R., Mazaheri, A., Bojak, I., Norris, D. G., and Kleinschmidt, A. (2011). Modulation of visually evoked cortical FMRI responses by phase of ongoing occipital alpha oscillations. J. Neurosci. 31, 3813-3820. doi: 10.1523/JNEUROSCI.4697-10.2011
203. Schneider, W., and Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychol. Rev. 84, 1-66. doi: 10.1037/0033-295X.84.1.1
204. Schubert, R., Haufe, S., Blankenburg, F., Villringer, A., and Curio, G. (2009). Now you'll feel it, now you won't: EEG rhythms predict the effectiveness of perceptual masking. J. Cogn. Neurosci. 21, 2407-2419. doi: 10.1162/jocn.2008.21174
205. Schultze-Kraft, M., Becker, R., Breakspear, M., and Ritter, P. (2011). Exploiting the potential of three dimensional spatial wavelet analysis to explore nesting of temporal oscillations and spatial variance in simultaneous EEG-fMRI data. Prog. Biophys. Mol. Biol. 105, 67-79. doi: 10.1016/j.pbiomolbio.2010.11.003
206. Schwartz, S., Maquet, P., and Frith, C. (2002). Neural correlates of perceptual learning: a functional MRI study of visual texture discrimination. Proc. Natl. Acad. Sci. U.S.A. 99, 17137-17142. doi: 10.1073/pnas.242414599
207. Seitz, A. R., Kim, D., and Watanabe, T. (2009). Rewards evoke learning of unconsciously processed visual stimuli in adult humans. Neuron 61, 700-707. doi: 10.1016/j.neuron.2009.01.016
208. Seitz, A. R., and Watanabe, T. (2003). Psychophysics: is subliminal learning really passive? Nature 422, 36-36. doi: 10.1038/422036a
209. Serre, T., Oliva, A., and Poggio, T. (2007). A feedforward architecture accounts for rapid categorization. Proc. Natl. Acad. Sci. U.S.A. 104, 6424-6429. doi: 10.1073/pnas.0700622104
210. Shibata, K., Watanabe, T., Sasaki, Y., and Kawato, M. (2011). Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 334, 1413-1415. doi: 10.1126/science.1212003
211. Shiffrin, R. M., and Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychol. Rev. 84, 127-190. doi: 10.1037/0033-295X. 84.2.127
212. Shiu, L.-P., and Pashler, H. (1992). Improvement in line orientation discrimination is retinally local but dependent on cognitive set. Percept. Psychophys. 52, 582-588. doi: 10.3758/BF03206720
213. Siegel, M., Donner, T. H., and Engel, A. K. (2012). Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13, 121-134. doi: 10.1038/nrn3137
214. Sigala, R., Serre, T., Poggio, T., and Giese, M. (2005). "Learning features of intermediate complexity for the recognition of biological motion," in Proceedings of the 15th International Conference on Artificial Neural Networks: Biological Inspirations-Volume Part I SE-ICANN'05 (Berlin, Heidelberg: Springer), 241-246.
215. Sigman, M., Pan, H., Yang, Y., Stern, E., Silbersweig, D., and Gilbert, C. D. (2005). Top-down reorganization of activity in the visual pathway after learning a shape identification task. Neuron 46, 823-835. doi: 10.1016/j.neuron.2005.05.014
216. Silva, L. R., Amitai, Y., and Connors, B. W. (1991). Intrinsic oscillations of neo-cortex generated by layer 5 pyramidal neurons. Science 251, 432-435. doi: 10.1126/science.1824881
217. Skrandies, W., and Fahle, M. (1994). Neurophysiological correlates of perceptual learning in the human brain. Brain Topogr. 7, 163-168. doi: 10.1007/BF01186774
218. Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., et al. (2009). Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. U.S.A. 106, 13040-13045. doi: 10.1073/pnas.0905267106
219. Snyder, A. C., and Foxe, J. J. (2010). Anticipatory attentional suppression of visual features indexed by oscillatory alpha-band power increases: a high-density electrical mapping study. J. Neurosci. 30, 4024-4032. doi: 10.1523/JNEUROSCI.5684-09.2010
220. Solgi, M., Liu, T., and Weng, J. (2013). A computational developmental model for specificity and transfer in perceptual learning. J. Vis. 13, 7. doi: 10.1167/13.1.7
221. Spaak, E., Bonnefond, M., Maier, A., Leopold, D. A., and Jensen, O. (2012). Layer-specific entrainment of gamma-band neural activity by the alpha rhythm in monkey visual cortex. Curr. Biol. 22, 2313-2318. doi: 10.1016/j.cub.2012. 10.020
222. Squire, L. R., and Zola, S. M. (1996). Structure and function of declarative and nondeclarative memory systems. Proc. Natl. Acad. Sci. U.S.A. 93, 13515-13522. doi: 10.1073/pnas.93.24.13515
223. Stam, C. J., Pijn, J. P., Suffczynski, P., and Lopes Da Silva, F. H. (1999). Dynamics of the human alpha rhythm: evidence for non-linearity? Clin. Neurophysiol. 110, 1801-1813. doi: 10.1016/S1388-2457(99)00099-1
224. Steriade, M., Gloor, P., Llinas, R. R., Lopes De Silva, F. H., and Mesulam, M. M. (1990). Report of IFCN committee on basic mechanisms. Basic mechanisms of cerebral rhythmic activities. Electroencephalogr. Clin. Neurophysiol. 76, 481-508. doi: 10.1016/0013-4694(90)90001-Z
225. Sterman, M. B. (2000). Basic concepts and clinical findings in the treatment of seizure disorders with EEG operant conditioning. Clin. Electroencephalogr. 31, 45-55.
226. Stevens, W. D., Buckner, R. L., and Schacter, D. L. (2010). Correlated low-frequency BOLD fluctuations in the resting human brain are modulated by recent experience in category-preferential visual regions. Cereb. Cortex 20, 1997-2006. doi: 10.1093/cercor/bhp270
227. Tambini, A., Ketz, N., and Davachi, L. (2010). Enhanced brain correlations during rest are related to memory for recent experiences. Neuron 65, 280-290. doi: 10.1016/j.neuron.2010.01.001
228. Taubert, M., Lohmann, G., Margulies, D. S., Villringer, A., and Ragert, P. (2011). Long-term effects of motor training on resting-state networks and underlying brain structure. Neuroimage 57, 1492-1498. doi: 10.1016/j.neuroimage.2011.05.078
229. Teich, A. F., and Qian, N. (2003). Learning and adaptation in a recurrent model of V1 orientation selectivity. J. Neurophysiol. 89, 2086-2100. doi: 10.1152/jn.00970.2002
230. Thut, G., Miniussi, C., and Gross, J. (2012). The functional importance of rhythmic activity in the brain. Curr. Biol. 22, R658-663. doi: 10.1016/j.cub.2012.06.061
231. Thut, G., Nietzel, A., Brandt, S. A., and Pascual-Leone, A. (2006). Alpha-band elec-troencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection. J. Neurosci. 26, 9494-9502. doi: 10.1523/JNEUROSCI.0875-06.2006
232. Thut, G., Veniero, D., Romei, V., Miniussi, C., Schyns, P., and Gross, J. (2011). Rhythmic TMS causes local entrainment of natural oscillatory signatures. Curr. Biol. 21, 1176-1185. doi: 10.1016/j.cub.2011.05.049
233. Tsodyks, M., and Gilbert, C. (2004). Neural networks and perceptual learning. Nature 431, 775-781. doi: 10.1038/nature03013
234. Vahdat, S., Darainy, M., Milner, T. E., and Ostry, D. J. (2011). Functionally specific changes in resting-state sensorimotor networks after motor learning. J. Neurosci. 31, 16907-16915. doi: 10.1523/JNEUROSCI.2737-11.2011
235. Valdes, P. A., Jimenez, J. C., Riera, J., Biscay, R., and Ozaki, T. (1999). Nonlinear EEG analysis based on a neural mass model. Biol. Cybern. 81, 415-424. doi: 10.1007/s004220050572
236. Van Dijk, H., Schoffelen, J. M., Oostenveld, R., and Jensen, O. (2008). Prestimulus oscillatory activity in the alpha band predicts visual discrimination ability. J. Neurosci. 28, 1816-1823. doi: 10.1523/JNEUROSCI.1853-07.2008
237. Vanrullen, R., Busch, N. A., Drewes, J., and Dubois, J. (2011). Ongoing EEG phase as a trial-by-trial predictor of perceptual and attentional variability. Front. Psychol. 2:60. doi: 10.3389/fpsyg.2011.00060
238. Varela, F. J., Toro, A., John, E. R., and Schwartz, E. L. (1981). Perceptual framing and cortical alpha rhythm. Neuropsychologia 19, 675-686. doi: 10.1016/0028-3932(81)90005-1
239. Varela, F., Lachaux, J. P., Rodriguez, E., and Martinerie, J. (2001). The brain-web: phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2, 229-239. doi: 10.1038/35067550
240. Ventura-Campos, N., Sanjuan, A., Gonzalez, J., Palomar-Garcia, M. A., Rodriguez-Pujadas, A., Sebastian-Galles, N., et al. (2013). Spontaneous brain activity predicts learning ability of foreign sounds. J. Neurosci. 33, 9295-9305. doi: 10.1523/JNEUROSCI.4655-12.2013
241. Vernon, D., Egner, T., Cooper, N., Compton, T., Neilands, C., Sheri, A., et al. (2003). The effect of training distinct neurofeedback protocols on aspects of cognitive performance. Int. J. Psychophysiol. 47, 75-85. doi: 10.1016/S0167-8760(02)00091-0
242. Vernon, D. J. (2005). Can neurofeedback training enhance performance? An evaluation of the evidence with implications for future research. Appl. Psychophysiol. Biofeedback 30, 347-364. doi: 10.1007/s10484-005-8421-4
243. Vijayan, S., and Kopell, N. J. (2012). Thalamic model of awake alpha oscillations and implications for stimulus processing. Proc. Natl. Acad. Sci. U.S.A. 109, 18553-18558. doi: 10.1073/pnas.1215385109
244. Vogels, R., and Orban, G. A. (1994). Activity of inferior temporal neurons during orientation discrimination with successively presented gratings. J. Neurophysiol. 71, 1428-1451.
245. Von Stein, A., Chiang, C., and Konig, P. (2000). Top-down processing mediated by interareal synchronization. Proc. Natl. Acad. Sci. U.S.A. 97, 14748-14753. doi: 10.1073/pnas.97.26.14748
246. Von Stein, A., and Sarnthein, J. (2000). Different frequencies for different scales of cortical integration: from local gamma to long range alpha/theta synchronization. Int. J. Psychophysiol. 38, 301-313. doi: 10.1016/S0167-8760(00)00172-0
247. Walker, M. P., Stickgold, R., Jolesz, F. A., and Yoo, S. S. (2005). The functional anatomy of sleep-dependent visual skill learning. Cereb. Cortex 15, 1666-1675. doi: 10.1093/cercor/bhi043
248. Wang, Z., Liu, J., Zhong, N., Qin, Y., Zhou, H., and Li, K. (2012). Changes in the brain intrinsic organization in both on-task state and post-task resting state. Neuroimage 62, 394-407. doi: 10.1016/j.neuroimage.2012.04.051
249. Watanabe, T., Nanez, J. E., and Sasaki, Y. (2001). Perceptual learning without perception. Nature 413, 844-848. doi: 10.1038/35101601
250. Watanabe, T., Nanez, J. E. Sr., Koyama, S., Mukai, I., Liederman, J., and Sasaki, Y. (2002). Greater plasticity in lower-level than higher-level visual motion processing in a passive perceptual learning task. Nat. Neurosci. 5, 1003-1009. doi: 10.1038/nn915
251. Weiskopf, N. (2012). Real-time fMRI and its application to neurofeedback. Neuroimage 62, 682-692. doi: 10.1016/j.neuroimage.2011.10.009
252. Wendling, F., Bellanger, J. J., Bartolomei, F., and Chauvel, P. (2000). Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. Biol. Cybern. 83, 367-378. doi: 10.1007/s004220000160
253. Wilson, H. R., and Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12, 1-24. doi: 10.1016/S0006-3495(72)86068-5
254. Worden, M. S., Foxe, J. J., Wang, N., and Simpson, G. V. (2000). Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex. J. Neurosci. 20, RC63.
255. Wyart, V., and Tallon-Baudry, C. (2008). Neural dissociation between visual awareness and spatial attention. J. Neurosci. 28, 2667-2679. doi: 10.1523/JNEUROSCI.4748-07.2008
256. Yamagishi, N., Callan, D. E., Anderson, S. J., and Kawato, M. (2008). Attentional changes in pre-stimulus oscillatory activity within early visual cortex are predictive of human visual performance. Brain Res. 1197, 115-122. doi: 10.1016/j.brainres.2007.12.063
257. Yang, T., and Maunsell, J. H. (2004). The effect of perceptual learning on neu-ronal responses in monkey visual area V4. J. Neurosci. 24, 1617-1626. doi: 10.1523/JNEUROSCI.4442-03.2004
258. Yotsumoto, Y., Chang, L. H., Ni, R., Salat, D., Andersen, G., Watanabe, T., et al. (2010). Perceptual learning and changes in white matter in the aged brain revealed by diffusion-tensor imaging (DTI). J. Vis. 10, 912-912. doi: 10.1167/10.7.912
259. Yotsumoto, Y., Sasaki, Y., Chan, P., Vasios, C. E., Bonmassar, G., Ito, N., et al. (2009). Location-specific cortical activation changes during sleep after training for perceptual learning. Curr. Biol. 19, 1278-1282. doi: 10.1016/j.cub.2009.06.011
260. Yu, C., Klein, S. A., and Levi, D. M. (2004). Perceptual learning in contrast discrimination and the (minimal) role of context. J. Vis. 4, 169-182. doi: 10.1167/4.3.4
261. Zajonc, R. B. (1968). Attitudinal effects of mere exposure. J. Pers. Soc. Psychol. 9, 1-1. doi: 10.1037/h0025848
262. Zhang, J., and Kourtzi, Z. (2010). Learning-dependent plasticity with and without training in the human brain. Proc. Natl. Acad. Sci. U.S.A. 107, 13503-13508. doi: 10.1073/pnas.1002506107
263. Zhaoping, L., Herzog, M. H., and Dayan, P. (2003). Nonlinear ideal observation and recurrent preprocessing in perceptual learning. Network 14, 233-247. doi: 10.1088/0954-898X/14/2/304
264. Zoefel, B., Huster, R. J., and Herrmann, C. S. (2011). Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. Neuroimage 54, 1427-1431. doi: 10.1016/j.neuroimage.2010.08.078
265. Zou, Q., Ross, T. J., Gu, H., Geng, X., Zuo, X. N., Hong, L. E., et al. (2013). Intrinsic resting-state activity predicts working memory brain activation and behavioral performance. Hum. Brain Mapp. 34, 3204-3215. doi: 10.1002/ hbm.22136