List of publications

Jacquot, V., Ying, Z., & Kreiman, G. (2020). Can Deep Learning Recognize Subtle Human Activities? ArXiv:2003.13852 [Cs].
Zhang, M., Tseng, C., & Kreiman, G. (2020). Putting visual object recognition in context. ArXiv:1911.07349 [Cs, Eess].
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.
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].
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.
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].
O’Connell, T. P., Chun, M. M., & Kreiman, G. (2019). Zero-shot neural decoding of visual categories without prior exemplars. BioRxiv, 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.
Xiao, W., & Kreiman, G. (2019). Gradient-free activation maximization for identifying effective stimuli. ArXiv:1905.00378 [Cs, q-Bio].
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.
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.
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].
Zhang, M., Feng, J., Lim, J. H., Zhao, Q., & Kreiman, G. (2018). What am I searching for? ArXiv:1807.11926 [Cs].
Wu, K., Wu, E., & Kreiman, G. (2018). Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition. ArXiv:1803.01967 [Cs].
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].
Misra, P., Marconi, A., Peterson, M., & Kreiman, G. (2018). Minimal memory for details in real life events. Scientific Reports, 8(1), 16701.
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.
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.
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.
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.
Cheney, N., Schrimpf, M., & Kreiman, G. (2017). On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. ArXiv:1703.08245 [Cs].
Lotter, W., Kreiman, G., & Cox, D. (2017). Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ArXiv:1605.08104 [Cs, q-Bio].
Kreiman, G. (2017). A null model for cortical representations with grandmothers galore. Language, Cognition and Neuroscience, 32(3), 274–285.
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.
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.
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.
Lotter, W., Kreiman, G., & Cox, D. (2016). Unsupervised Learning of Visual Structure using Predictive Generative Networks. ArXiv:1511.06380 [Cs, q-Bio].
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.
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.
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.
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.
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.
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.
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.
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.
Singer, J. M., & Kreiman, G. (2014). Short temporal asynchrony disrupts visual object recognition. Journal of Vision, 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.
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.
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.
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.
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.
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.

zotpress style=”harvard1″ sortby=”date” sort=”DESC” showimage=”yes” download=”yes” title=”yes”

zotpress sortby=”date” sort=”desc”