List of publications

Jacquot, V., Ying, Z., & Kreiman, G. (2020). Can Deep Learning Recognize Subtle Human Activities? ArXiv:2003.13852 [Cs]. http://arxiv.org/abs/2003.13852
Zhang, M., Tseng, C., & Kreiman, G. (2020). Putting visual object recognition in context. ArXiv:1911.07349 [Cs, Eess]. http://arxiv.org/abs/1911.07349
Kreiman, G., & Serre, T. (2020). Beyond the feedforward sweep: feedback computations in the visual cortex. Annals of the New York Academy of Sciences, 1464(1), 222–241. https://doi.org/10.1111/nyas.14320
Casper, S., Boix, X., D’Amario, V., Guo, L., Schrimpf, M., Vinken, K., & Kreiman, G. (2019). Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks. ArXiv:1912.04783 [Cs, Stat]. http://arxiv.org/abs/1912.04783
Madhavan, R., Bansal, A. K., Madsen, J. R., Golby, A. J., Tierney, T. S., Eskandar, E. N., Anderson, W. S., & Kreiman, G. (2019). Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cerebral Cortex (New York, N.Y.: 1991), 29(11), 4551–4567. https://doi.org/10.1093/cercor/bhy333
Zhang, M., Tseng, C., Montejo, K., Kwon, J., & Kreiman, G. (2019). Lift-the-flap: what, where and when for context reasoning. ArXiv:1902.00163 [Cs]. http://arxiv.org/abs/1902.00163
O’Connell, T. P., Chun, M. M., & Kreiman, G. (2019). Zero-shot neural decoding of visual categories without prior exemplars. BioRxiv, 700344. https://doi.org/10.1101/700344
Vinken, K., Boix, X., & Kreiman, G. (2019). Incorporating neuronal fatigue in deep neural networks captures dynamics of adaptation in neurophysiology and perception [Preprint]. Neuroscience. https://doi.org/10.1101/642777
Xiao, W., & Kreiman, G. (2019). Gradient-free activation maximization for identifying effective stimuli. ArXiv:1905.00378 [Cs, q-Bio]. http://arxiv.org/abs/1905.00378
Kreiman, G. (2019). It’s a small dimensional world after all: Comment on “The unreasonable effectiveness of small neural ensembles in high-dimensional brain” by Alexander N. Gorban et al. Physics of Life Reviews, 29, 96–97. https://doi.org/10.1016/j.plrev.2019.03.015
Ponce, C. R., Xiao, W., Schade, P. F., Hartmann, T. S., Kreiman, G., & Livingstone, M. S. (2019). Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell, 177(4), 999-1009.e10. https://doi.org/10.1016/j.cell.2019.04.005
Kreiman, Gabriel. (2019). What do neurons really want? The role of semantics in cortical representations. In Psychology of Learning and Motivation (Kara D. Federmeier, Diane M. Beck, Vol. 70). Elsevier.
Xiao, W., Chen, H., Liao, Q., & Poggio, T. (2018). Biologically-plausible learning algorithms can scale to large datasets. ArXiv:1811.03567 [Cs, Stat]. http://arxiv.org/abs/1811.03567
Zhang, M., Feng, J., Lim, J. H., Zhao, Q., & Kreiman, G. (2018). What am I searching for? ArXiv:1807.11926 [Cs]. http://arxiv.org/abs/1807.11926
Wu, K., Wu, E., & Kreiman, G. (2018). Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition. ArXiv:1803.01967 [Cs]. http://arxiv.org/abs/1803.01967
Lotter, W., Kreiman, G., & Cox, D. (2018). A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. ArXiv:1805.10734 [Cs, q-Bio]. http://arxiv.org/abs/1805.10734
Misra, P., Marconi, A., Peterson, M., & Kreiman, G. (2018). Minimal memory for details in real life events. Scientific Reports, 8(1), 16701. https://doi.org/10.1038/s41598-018-33792-2
Isik, L., Singer, J., Madsen, J. R., Kanwisher, N., & Kreiman, G. (2018). What is changing when: Decoding visual information in movies from human intracranial recordings. NeuroImage, 180(Pt A), 147–159. https://doi.org/10.1016/j.neuroimage.2017.08.027
Zhang, M., Feng, J., Ma, K. T., Lim, J. H., Zhao, Q., & Kreiman, G. (2018). Finding any Waldo with zero-shot invariant and efficient visual search. Nature Communications, 9(1), 3730. https://doi.org/10.1038/s41467-018-06217-x
Tang, H., Schrimpf, M., Lotter, W., Moerman, C., Paredes, A., Ortega Caro, J., Hardesty, W., Cox, D., & Kreiman, G. (2018). Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences of the United States of America, 115(35), 8835–8840. https://doi.org/10.1073/pnas.1719397115
Palepu, A., Premanathan, S., Azhar, F., Vendrame, M., Loddenkemper, T., Reinsberger, C., Kreiman, G., Parkerson, K. A., Sarma, S., & Anderson, W. S. (2018). Automating Interictal Spike Detection: Revisiting A Simple Threshold Rule. Conference Proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2018, 299–302. https://doi.org/10.1109/EMBC.2018.8512244
Cheney, N., Schrimpf, M., & Kreiman, G. (2017). On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. ArXiv:1703.08245 [Cs]. http://arxiv.org/abs/1703.08245
Lotter, W., Kreiman, G., & Cox, D. (2017). Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ArXiv:1605.08104 [Cs, q-Bio]. http://arxiv.org/abs/1605.08104
Kreiman, G. (2017). A null model for cortical representations with grandmothers galore. Language, Cognition and Neuroscience, 32(3), 274–285. https://doi.org/10.1080/23273798.2016.1218033
Tang, H., Singer, J., Ison, M. J., Pivazyan, G., Romaine, M., Frias, R., Meller, E., Boulin, A., Carroll, J., Perron, V., Dowcett, S., Arellano, M., & Kreiman, G. (2016). Predicting episodic memory formation for movie events. Scientific Reports, 6, 30175. https://doi.org/10.1038/srep30175
Gómez-Laberge, C., Smolyanskaya, A., Nassi, J. J., Kreiman, G., & Born, R. T. (2016). Bottom-Up and Top-Down Input Augment the Variability of Cortical Neurons. Neuron, 91(3), 540–547. https://doi.org/10.1016/j.neuron.2016.06.028
Tang, H., Yu, H.-Y., Chou, C.-C., Crone, N. E., Madsen, J. R., Anderson, W. S., & Kreiman, G. (2016). Cascade of neural processing orchestrates cognitive control in human frontal cortex. ELife, 5. https://doi.org/10.7554/eLife.12352
Lotter, W., Kreiman, G., & Cox, D. (2016). Unsupervised Learning of Visual Structure using Predictive Generative Networks. ArXiv:1511.06380 [Cs, q-Bio]. http://arxiv.org/abs/1511.06380
Miconi, T., Groomes, L., & Kreiman, G. (2016). There’s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task. Cerebral Cortex (New York, N.Y.: 1991), 26(7), 3064–3082. https://doi.org/10.1093/cercor/bhv129
Tang, S., Hemberg, M., Cansizoglu, E., Belin, S., Kosik, K., Kreiman, G., Steen, H., & Steen, J. (2016). f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome. Nucleic Acids Research, 44(10), e97. https://doi.org/10.1093/nar/gkw157
Hanlin Tang, & Gabriel Kreiman. (2016). Recognition of Occluded Objects. In Computational and cognitive neuroscience of vision. Springer Berlin Heidelberg.
Singer, J. M., Madsen, J. R., Anderson, W. S., & Kreiman, G. (2015). Sensitivity to timing and order in human visual cortex. Journal of Neurophysiology, 113(5), 1656–1669. https://doi.org/10.1152/jn.00556.2014
Kim, T.-K., Hemberg, M., & Gray, J. M. (2015). Enhancer RNAs: a class of long noncoding RNAs synthesized at enhancers. Cold Spring Harbor Perspectives in Biology, 7(1), a018622. https://doi.org/10.1101/cshperspect.a018622
Prabakaran, S., Hemberg, M., Chauhan, R., Winter, D., Tweedie-Cullen, R. Y., Dittrich, C., Hong, E., Gunawardena, J., Steen, H., Kreiman, G., & Steen, J. A. (2014). Quantitative profiling of peptides from RNAs classified as noncoding. Nature Communications, 5, 5429. https://doi.org/10.1038/ncomms6429
Malik, A. N., Vierbuchen, T., Hemberg, M., Rubin, A. A., Ling, E., Couch, C. H., Stroud, H., Spiegel, I., Farh, K. K.-H., Harmin, D. A., & Greenberg, M. E. (2014). Genome-wide identification and characterization of functional neuronal activity-dependent enhancers. Nature Neuroscience, 17(10), 1330–1339. https://doi.org/10.1038/nn.3808
Tang, H., Buia, C., Madhavan, R., Crone, N. E., Madsen, J. R., Anderson, W. S., & Kreiman, G. (2014). Spatiotemporal dynamics underlying object completion in human ventral visual cortex. Neuron, 83(3), 736–748. https://doi.org/10.1016/j.neuron.2014.06.017
Pinto, A. L. R., Fernández, I. S., Peters, J. M., Manganaro, S., Singer, J. M., Vendrame, M., Prabhu, S. P., Loddenkemper, T., & Kothare, S. V. (2014). Localization of sleep spindles, k-complexes, and vertex waves with subdural electrodes in children. Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society, 31(4), 367–374. https://doi.org/10.1097/WNP.0000000000000071
Singer, J. M., & Kreiman, G. (2014). Short temporal asynchrony disrupts visual object recognition. Journal of Vision, 14(5), 7. https://doi.org/10.1167/14.5.7
Frost, B., Hemberg, M., Lewis, J., & Feany, M. B. (2014). Tau promotes neurodegeneration through global chromatin relaxation. Nature Neuroscience, 17(3), 357–366. https://doi.org/10.1038/nn.3639
Bansal, A. K., Madhavan, R., Agam, Y., Golby, A., Madsen, J. R., & Kreiman, G. (2014). Neural dynamics underlying target detection in the human brain. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 34(8), 3042–3055. https://doi.org/10.1523/JNEUROSCI.3781-13.2014
Nassi, J. J., Gómez-Laberge, C., Kreiman, G., & Born, R. T. (2014). Corticocortical feedback increases the spatial extent of normalization. Frontiers in Systems Neuroscience, 8, 105. https://doi.org/10.3389/fnsys.2014.00105
Kreiman, Gabriel, Rutishauser, Ueli. (2014). Kreiman G, Rutishauser U, Cerf M, Fried I. (2014). The next ten years and beyond. In Single neuron studies of the human brain. Probing cognition. In Single Neuron Studies of the Human Brain. MIT Press.
Ueli Rutishauser, Moran Cerf, Gabriel Kreiman. (2014). Data analysis techniques for human microwire recordings: spike detection and sorting, decoding, relation between units and local field potentials. In Single Neuron Studies of the Human Brain. MIT Press.
Arjun Bansal. (2014). Human Single Unit Activity for Reach and Grasp Motor Prostheses. In Single Neuron Studies of the Human Brain. MIT Press.
Fried, I., Rutishauser, U., Cerf, M., & Kreiman, G. (Eds.). (2014). Single neuron studies of the human brain: probing cognition. The MIT Press.
Madhavan, R., Millman, D., Tang, H., Crone, N. E., Lenz, F. A., Tierney, T. S., Madsen, J. R., Kreiman, G., & Anderson, W. S. (2014). Decrease in gamma-band activity tracks sequence learning. Frontiers in Systems Neuroscience, 8, 222. https://doi.org/10.3389/fnsys.2014.00222
Kreiman, Gabriel. (2013). Computational Models of Visual Object Recognition. In Principles of Neural Coding. CRC Press.
Kreiman, Gabriel. (2013). Mind the quantum? Trends in Cognitive Science, 17(3), 109.
Bansal, A. K., Singer, J. M., Anderson, W. S., Golby, A., Madsen, J. R., & Kreiman, G. (2012). Temporal stability of visually selective responses in intracranial field potentials recorded from human occipital and temporal lobes. Journal of Neurophysiology, 108(11), 3073–3086. https://doi.org/10.1152/jn.00458.2012
Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E., & Donoghue, J. P. (2012). Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials. Journal of Neurophysiology, 107(5), 1337–1355. https://doi.org/10.1152/jn.00781.2011

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