• Efficient neural decoding of self-location with a deep recurrent network. ; Tampuu, A., Matiisen, T., Ólafsdóttir, H. F., Barry, C., & Vicente, R. (2019); PLoS computational biology, 15(2), e1006822
• MAXENT3D_PID: An Estimator for the Maximum-entropy Trivariate Partial Information Decomposition. Makkeh, A., Chicharro, D., Theis, D. O., & Vicente, R. (2019); arXiv preprint arXiv:1901.03352.
• A Bayesian decomposition of BAC firing as a mechanism for apical amplification in neocortical pyramidal neurons. Kay, J., Phillips, W., Aru, J., Graham, B., & Larkum, M. (2019). bioRxiv, 604066.
• Individual differences in the effects of priors on perception: a multi-paradigm approach. Tulver, K., Aru, J., Rutiku, R., & Bachmann, T. (2019).; Cognition 187 (2019): 167-177
• ViraMiner: deep learning on raw DNA sequences for identifying viral genomes in human samples. Tampuu, A., Bzhalava, Z., Dillner, J., & Vicente, R. (2019) ; PLoS ONE 14(9): e0222271. https://doi.org/10.1371/journal.pone.0222271

• BROJA-2PID: A robust estimator for bivariate partial information decomposition. Makkeh, A., Theis, D., & Vicente, R. (2018); Entropy, 20(4), 271.
• Identifying task-relevant spectral signatures of perceptual categorization in the human cortex. Kuzovkin, I., Vidal, J. R., Perrone-Bertlotti, M., Kahane, P., Rheims, S., Aru, J., … & Vicente, R. (2018) ; bioRxiv, 483487.
• What deep learning can tell us about higher cognitive functions like mindreading? Aru, J., & Vicente, R. (2018) arXiv preprint arXiv:1803.10470.
• Do deep reinforcement learning agents model intentions? Matiisen, T., Labash, A., Majoral, D., Aru, J., & Vicente, R. (2018) ; arXiv preprint arXiv:1805.06020.
• Machine Learning for detection of viral sequences in human metagenomic datasets. Bzhalava, Z., Tampuu, A., Bała, P., Vicente, R., & Dillner, J. (2018) ; BMC bioinformatics, 19(1), 336.
General markers of conscious visual perception and their timing
• A Model for Time Interval Learning in The Purkinje Cell. Majoral, D., Zemmar, A., & Vicente, R. (2018) ; bioRxiv 448027.
• Activations of deep convolutional neural network are aligned with gamma band activity of human visual cortex Kuzovkin, I., Vicente, R., Petton, M., Lachaux, J.P., Baciu, M., Kahane, P., Rheims, S., Vidal, J.R., Aru, J. (2018); Communications Biology 1 (107)
• It’s all in your head: Expectations create illusory perception in a dual-task setup. Aru, J., Tulver, K., & Bachmann, T. (2018). Consciousness and cognition, 65, 197-208.
• Direct information transfer rate optimisation for SSVEP-based BCI. Anti Ingel, Ilya Kuzovkin and Raul Vicente ; Journal of Neural Engineering

• Echo state networks with multiple read-out modules. Laan, A., Vicente, R. (2017) ; bioRxiv 017558.
• Analyzing Information Distribution in Complex Systems. Sootla, S., Theis, D.O., Vicente, R. (2017) ; Entropy, 19(12), 636.
• Bivariate Partial Information Decomposition: the optimization perspective. Makkeh, A., Theis, D.O., Vicente, R. (2017) ; Entropy, 19(10), 530. 
• Multiagent cooperation and competition with deep reinforcement learning. Tampuu, A., Matiisen T., Kodelja, D., Kuzovkin, I., Korjus, K., Aru, J., Aru, J., Vicente, R. (2017); PLoS One, 12(4), e0172395. 
• VREX: an open-source toolbox for creating 3D virtual reality experiments. Vasser, M., Kängsepp, M., Magomedkerimov, M., Kilvits, K., Stafinjak, V., Kivisik, T., Vicente, R., & Aru, J. (2017) ; BMC Psychology, 5(1), 4. [paper]
• Attention is withdrawn from the area of the visual field where the own hand is currently moving. Laak, K.J., Vasser, M., Uibopuu, O.J., & Aru, J (2017); Neurosci Conscious 2017; 3 (1): niw025. [paper]
• In and out of consciousness: how does conscious processing (d) evolve over time? Aru, J., & Bachmann, T. (2017) ; Frontiers in Psychology, 8, 128 [paper]

• Early effects of previous experience on conscious perception. Aru, J., Rutiku, R., Wibral, M., Singer, W., & Melloni, L. (2016); Neuroscience of consciousness 2016 (1), niw004.
• Visual masking with frontally applied pre-stimulus TMS and its subject-specific neural correlates. Rutiku, R., Tulver, K., Aru, J., & Bachmann, T. (2016); Brain research, 1642, 136-145.
• General Markers of Conscious Visual Perception and Their Timing .Rutiku, R., Aru, J., & Bachmann, T. (2016); Frontiers in human neuroscience, 10.
• When expectation confounds iconic memory .Bachmann, T., & Aru, J. (2016); Consciousness and Cognition, 45, 198-199.
• An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable. Korjus K, Hebart MN, Vicente R.; PloS one. 2016 Aug 26; 11(8):e0161788.
• Adaptive Interactive Learning for Brain-Computer Interface. Kuzovkin, I., Tretyakov, K., Uusberg, A, Vicente, R. (2016); Brain-Computer Interface Meeting 2016.

• Untangling cross-frequency coupling in neuroscience. Aru, J., Aru, J., Priesemann, V., Wibral, M., Lana, L., Pipa, G., Singer, W., & Vicente, R. (2015) ; Current Opinion in Neurobiology, 31, 51-61.
• Occipital long-interval paired pulse TMS leads to slow wave components in NREM sleep. Stamm, M, Aru, J., Rutiku, R., & Bachmann, T. (2015) ; Consciousness and cognition, in press.
• Does the P300 Reflect Conscious Perception Or Its Consequences? Rutiku, R., Martin, M., Bachmann, T., & Aru, J. (2015) ; Neuroscience, in press.
• Still wanted – neural mechanisms of consciousness! Aru, J., & Bachmann, T. (2015) ; Frontiers in Consciousness Research, 10.3389/fpsyg.2015.00005.
• On why the unconscious prerequisites and consequences of consciousness might derail us from unravelling the neural correlates of consciousness. Aru J., Bachmann T., Singer W., Melloni L ; The Constitution of Phenomenal Consciousness: Toward a Science and Theory,Vol. 92, Advances in Consciousness Research, ed. Miller S. M., editor. (Amsterdam: John Benjamins Publishing Company; ). 10.1075/aicr.92
• Personality cannot be predicted from the power of resting state EEG.  Korjus K., Uusberg A., Uibo H., Kuldkepp N., Kreegipuu K., Allik J., Vicente R., & Aru J. (2015) ; Frontiers in Human Neuroscience, 9:63. 10.3389/fnhum.2015.00063.
• Assessing coupling dynamics from an ensemble of time series. Gomez-Herrero G., Wu W., Rutanen K., Soriano, M.C., Pipa, G. & Vicente R. (2015) ; Entropy, 17, 1958-1970 ; doi:10.3390/e17041958

• Sancristóbal, B., Vicente, R., & Garcia-Ojalvo, J. (2014). Role of frequency mismatch in neuronal communication through coherence. Journal of Computational Neuroscience, 1-16.
  
• Wibral, M., Vicente, R., & Lizier, J.T. (Eds.) (2014). Directed Information Measures in Neuroscience. Springer. [url]
• Wibral, M., Vicente, R., & Lindner, M. (2014) Transfer entropy in neuroscience. Directed Information Measures in Neuroscience, 3-36. Springer.
• Vicente, R., & Wibral, M. (2014) Efficient estimation of information transfer. Directed Information Measures in Neuroscience, 37-58. Springer.
• Wollstadt, P., Martinez-Zarzuela, M., Vicente, R., Diaz-Pernas, F.J., & Wibral, M. (2014). Efficient transfer entropy analysis of non-stationary neural time series. PLoS ONE, 9(7), e102833.
• Castellano, M., Plöchl, M., Vicente, R., & Pipa, G. (2014) Neuronal oscillations form fronto-parietal networks during contour integration of dynamic visual stimuli, Frontiers in Integrative Neuroscience, 8, 64.
• Zemmar, A., Weinmann, O., Kellner, Y., Yu, X., Vicente, R., Gullo, M., Kasper, H., Lussi, K., Ristic, Z., Luft, A., Rioult-Pedotti, M., Zuo, Y., Zagrebelsky, M., & Schwab, M.E. (2014) Neutralization of Nogo-A enhances synaptic plasticity in the rat motor cortex and improves motor learning in-vivo, Journal of Neuroscience, 34(26), 8685-8698.
• Aru, J. (2014). Distilling the neural correlates of conscious perception. Goethe University Frankfurt. (PhD-thesis). PDF

• Kuzovkin, I. (2013). Adaptive Interactive Learning: a Novel Approach to Training Brain-Computer Interface Systems. University of Tartu [pdf] (Master Thesis)
• Wibral, M., Pampu, N., Priesemann, V., Siebenhühner F., Seiwert H., Lindner, M., Lizier, J.T., & Vicente, R. (2013). Measuring information-transfer delays. PLoS ONE, 8(2), e55809. [url]
• Sancristóbal, B., Vicente, R., Sancho, J.M., & Garcia-Ojalvo, J. (2013). Emerging bimodal firing patterns implement different encoding strategies during gamma-band oscillations. Frontiers in Computational Neuroscience 7:18. [url]
• Roux, F., Wibral, M., Singer, W., Aru, J., & Uhlhaas, P.J. (2013). The Phase of Thalamic Alpha Activity Modulates Cortical Gamma-Band Activity: Evidence from Resting-State MEG Recordings. Journal of Neuroscience, 33(45), 17827-35. [pdf]
• Garces, P., Vicente, R., Wibral, M., Pineda Pardo, J.A., Lopez, M.E., Aurtenetxe, S., Marcos, A., de Andres, M.E., Yus, M., Sancho, M., Maestu, F., & Fernandez, A. (2013). Brain-wide slowing of spontaneous alpha rhythms in mild cognitive impairment. Frontiers in Aging Neuroscience, 5, 100. [url]

• Aru, J., Bachmann, T, Singer, W. & Melloni, L. (2012). Distilling the neural correlates of consciousness. Neuroscience & Biobehavioral Reviews, 36, 737-746. PDF
• Aru, J., Korjus, K., Murd, C., & Bachmann, T. (2012). Spectral Signatures of the Effects of Caffeine and Occipitally Applied Transcranial Magnetic Stimulation in a Task-Free Experimental Setup. Journal of Caffeine Research, 2(1), 23-30. [url]
• Aru, J., Axmacher, N., Do Lam, A.T.A., Fell, J., Elger, C.R., Singer, W., & Melloni, L. (2012). Local Category-Specific Gamma Band Responses in the Visual Cortex Do Not Reflect Conscious Perception. Journal of Neuroscience, 32(43), 14909-14914. [pdf]
• D’Huys, O., Fischer, I., Danckaert, J., & Vicente, R. (2012). Spectral and correlation properties of rings of delay-coupled elements: Comparing linear and nonlinear systems. Physical Review E, 85(5), 056209. [url]
• Vicente, R. & Mirasso, C.R. (2012). Cuando las neuronas sincronizan sus relojes. Mente y Cerebro, 53, 62-71. [pdf]

• Korjus, K. (2011). Causality Measures in Neuroscience: Wiener-Granger causality and transfer entropy applied to intracranial EEG data. University of Manchester [pdf] (Master Thesis)
• Lindner, M., Vicente, R., Priesemann, V., & Wibral, M. (2011). TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy. BMC Neuroscience, 12(1), 119. [url]
• Perez, T., Garcia, G.C., Eguiluz, V., Vicente, R., Pipa, G., & Mirasso C.R. (2011). Effect of the topology and delayed interactions in neuronal networks synchronization. PLoS ONE, 6(5), e19900. [url]
• D’Huys, O., Fischer, I., Danckaert, J., & Vicente, R. (2011). Role of the delay for the symmetry in the dynamics of networks. Physical Review E, 83(4), 046223. [url]
• Wibral, M., Rahm, B., Rieder, M., Lindner, M., Vicente, R., & Kaiser, J. (2011). Transfer entropy in magnetoencephalographic data: Quantifying information flow in cortical and cerebellar networks. Progress in Biophysics and Molecular Biology, 105(1), 80-97. [url]
• Vicente, R., Wibral, M., Lindner, M., & Pipa, G. (2011). Transfer entropy –a model-free measure of effective connectivity for the neurosciences. Journal of Computational Neuroscience, 30(1), 45-67. [url]
• Eriksson, D., Vicente, R., & Schmidt, K. (2011). A linear model of phase-dependent power correlations in neuronal oscillations. Frontiers in Computational Neuroscience 5:34. [url]
• Scheller, B., Castellano, M., Vicente, R., & Pipa, G. (2011). Spike train auto-structure impacts post-synaptic firing and time-dependent plasticity. Frontiers in Computational Neuroscience 5:60. [url]