Armendariz, M., Xiao, W., Vinken, K. & Kreiman, G. Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions. Cognitive Neuropsychology 1 - 3 (2022). doi:10.1080/02643294.2022.2041588

Sikarwar, A. & Kreiman, G. On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering. arXiv (2022). doi:10.48550/arXiv.2201.03965

Dellaferrera, G. & Kreiman, G. Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. Proceedings of the 39th International Conference on Machine Learning, PMLR 162, 4937-4955 (2022).

Bardon, A., Xiao, W., Ponce, C. R., Livingstone, M. S. & Kreiman, G. Face neurons encode nonsemantic features. Proceedings of the National Academy of Sciences 119, (2022).

Zheng, J. et al. Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience 25, 358 - 368 (2022).

Casper, S., Nadeau, M. & Kreiman, G. One thing to fool them all: generating interpretable, universal, and physically-realizable adversarial features. arXiv (2022). doi:10.48550/arXiv.2110.03605

Shaham, N., Chandra, J., Kreiman, G. & Sompolinsky, H. Stochastic consolidation of lifelong memoryAbstract. Scientific Reports 12, (2022).

Xiao, Y. et al. Task-specific neural processes underlying conflict resolution during cognitive control. BioRxiv (2022). doi:10.1101/2022.01.16.476535 

Zhang, M. & Kreiman, G. Beauty is in the eye of the machine. Nature Human Behaviour 5, 675 - 676 (2021).

Kreiman, G. Biological and Computer Vision. (Cambridge University Press, 2021). doi:10.1017/9781108649995

Zheng, J. et al. Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation. bioRxiv (2021).

Casper, S. et al. Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021 (2021). at <https://dblp.org/rec/conf/aaai/CasperBDGSVK21.html>

Zhang, M., Badkundri, R., Talbot, M. B., Zawar, R. & Kreiman, G. Hypothesis-driven Online Video Stream Learning with Augmented Memory. arXiv (2021). doi:10.48550/arXiv.2104.02206

Weisholtz, D. S. et al. Localized task-invariant emotional valence encoding revealed by intracranial recordingsAbstract. Social Cognitive and Affective Neuroscience (2021). doi:10.1093/scan/nsab134

Wang, J., Tao, A., Anderson, W. S., Madsen, J. R. & Kreiman, G. Mesoscopic physiological interactions in the human brain reveal small-world properties. Cell Reports 36, 109585 (2021).

Gupta, S. Kant, Zhang, M., WU, C. H. I. A. - C. H. I. E. N., Wolfe, J. & Kreiman, G. Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/Schedule?showEvent=28848>

Bomatter, P. et al. When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. International Conference on Computer Vision (ICCV) (2021). doi:10.1109/iccv48922.2021.00032

Kreiman, G. & Serre, T. Beyond the feedforward sweep: feedback computations in the visual cortex. Annals of the New York Academy of Sciences 1464, 222 - 241 (2020).

Kreiman, G. & Serre, T. Beyond the feedforward sweep: feedback computations in the visual cortex. Ann. N.Y. Acad. Sci. | Special Issue: The Year in Cognitive Neuroscience 1464, 222-241 (2020).

Jacquot, V., Ying, J. & Kreiman, G. Can Deep Learning Recognize Subtle Human Activities?. CVPR 2020 (2020).

Vinken, K., Boix, X. & Kreiman, G. Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception. Science Advances 6, eabd4205 (2020).

Ben-Yosef, G., Kreiman, G. & Ullman, S. Minimal videos: Trade-off between spatial and temporal information in human and machine vision. Cognition (2020). doi:10.1016/j.cognition.2020.104263

Lotter, W., Kreiman, G. & Cox, D. A neural network trained for prediction mimics diverse features of biological neurons and perception. Nature Machine Intelligence 2, 210 - 219 (2020).

Lotter, W., Kreiman, G. & Cox, D. A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. Nature Machine Learning (2020).

Zhang, M., Tseng, C. & Kreiman, G. Putting visual object recognition in context. CVPR 2020 (2020).

Ben-Yosef, G., Kreiman, G. & Ullman, S. What can human minimal videos tell us about dynamic recognition models?. International Conference on Learning Representations (ICLR 2020) (2020). at <https://baicsworkshop.github.io/pdf/BAICS_1.pdf>

Xiao, W. & Kreiman, G. XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLOS Computational Biology 16, e1007973 (2020).

Ponce, C. R. et al. Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell 177, 1009 (2019).

Kreiman, G. It's a small dimensional world after all. Physics of Life Reviews 29, 96 - 97 (2019).

Kreiman, G. Psychology of Learning and Motivation 70, (2019).

Palepu, A. & Kreiman, G. Development of automated interictal spike detector. 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018 (2018). at <https://embc.embs.org/2018/>

Zhang, M. et al. Finding any Waldo with zero-shot invariant and efficient visual search. Nature Communications 9, (2018).

Eric, W., Kevin, W. & Kreiman, G. Learning scene gist with convolutional neural networks to improve object recognition. 2018 52nd Annual Conference on Information Sciences and Systems (CISS) (2018). doi:10.1109/CISS.2018.8362305

Wu, K., Wu, E. & Kreiman, G. Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition.  arXiv | Cornell University  arXiv:1803.01967, (2018).

Misra, P., Marconi, A., Peterson, M. F. & Kreiman, G. Minimal memory for details in real life events. Scientific Reports 8, (2018).

Madhavan, R. et al. Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cerebral Cortex 1?17, (2018).

Lotter, W., Kreiman, G. & Cox, D. A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception. ( arXiv | Cornell University, 2018). at <https://arxiv.org/pdf/1805.10734.pdf>

Tang, H. et al. Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115

Ben-Yosef, G., Kreiman, G. & Ullman, S. Spatiotemporal interpretation features in the recognition of dynamic images. (2018).

Zhang, M., Feng, J., Lim, J. Hwee, Zhao, Q. & Kreiman, G. What am I searching for?. (2018).

Isik, L. et al. What is changing when: decoding visual information in movies from human intracranial recordings. NeuroImage 180, Part A, 147-159 (2018).

Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (2017).

Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. (2017).

Kreiman, G. A null model for cortical representations with grandmothers galore. Language, Cognition and Neuroscience 274 - 285 (2017). doi:10.1080/23273798.2016.1218033

Tang, H., Kreiman, G. & Zhao, Q. Computational and Cognitive Neuroscience of Vision (Springer Singapore, 2017). at <http://www.springer.com/us/book/9789811002113>

Cheney, N., Schrimpf, M. & Kreiman, G. On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. (2017).

Isik, L., Singer, J., Madsen, J., Kanwisher, N. & Kreiman, G. What is changing when: Decoding visual information in movies from human intracranial recordings. Neuroimage (2017). doi:https://doi.org/10.1016/j.neuroimage.2017.08.027

Gómez-Laberge, C., Smolyanskaya, A., Nassi, J. J., Kreiman, G. & Born, R. T. Bottom-up and Top-down Input Augment the Variability of Cortical Neurons. Neuron 91(3), 540-547 (2016).

Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>

Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>

Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352

Tang, H. et al. A machine learning approach to predict episodic memory formation. 2016 Annual Conference on Information Science and Systems (CISS) 539 - 544  (2016). doi:10.1109/CISS.2016.7460560

Kreiman, G. Neural Information Processing Systems (NIPS) 2015 Review. (2016).

Tang, H. et al. Predicting episodic memory formation for movie events. Scientific Reports (2016). doi:10.1038/srep30175

Tang, H. et al. Predicting episodic memory formation for movie events [code]. (2016).

Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).

Lotter, W., Kreiman, G. & Cox, D. PredNet - "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning" [code]. (2016).

Miconi, T., Groomes, L. & Kreiman, G. There?s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [code]. (2016).

Miconi, T., Groomes, L. & Kreiman, G. There?s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [dataset]. (2016).

Miconi, T., Groomes, L. & Kreiman, G. There's Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task. Cerebral Cortex 26(7), 26:3064-3082 (2016).

Lotter, W., Kreiman, G. & Cox, D. Unsupervised Learning of Visual Structure using Predictive Generative Networks. International Conference on Learning Representations (ICLR) (2016). at <http://arxiv.org/pdf/1511.06380v2.pdf>

Madhavan, R. et al. Decrease in gamma-band activity tracks sequence learning. Frontiers in Systems Neuroscience 8, (2015).

Singer, J., Madsen, J., Anderson, W. S. & Kreiman, G. Sensitivity to timing and order in human visual cortex. Journal of Neurophysiology 113, 1656 - 1669 (2015).

Lotter, W., Kreiman, G. & Cox, D. UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS. (2015).

Nassi, J. J., Gomez-Laberge, C., Kreiman, G. & Born, R. T. Corticocortical feedback increases the spatial extent of normalization. Frontiers in Systems Neuroscience 8, 105 (2014).

Rutishauser, U., Cerf, M. & Kreiman, G. Single Neuron Studies of the Brain: Probing Cognition (2014).

Kreiman, G. Cognitive Neuroscience V, (2014).

Bansal, A. et al. Neural Dynamics Underlying Target Detection in the Human Brain. Journal of Neuroscience 34, (2014).

Kreiman, G., Rutishauser, U., Cerf, M. & Fried, I. Single neuron studies of the human brain. Probing cognition (2014).

Miconi, T., Groomes, L. & Kreiman, G. A normalization model of visual search predicts single trial human fixations in an object search task. (2014).

Kreiman, G. People, objects and interactions in movies. (2014).

Tang, H., Buia, C., Madsen, J., Anderson, W. S. & Kreiman, G. A role for recurrent processing in object completion: neurophysiological, psychophysical and computational evidence. (2014).

Singer, J., Madsen, J., Anderson, W. S. & Kreiman, G. Sensitivity to Timing and Order in Human Visual Cortex. (2014).

Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. Journal of Vision 12, (2014).

Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. J Vis 14, 7 (2014).

Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. (2014). at <http://klab.tch.harvard.edu/resources/singer_asynchrony.html>

Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. (2014). at <http://klab.tch.harvard.edu/resources/singer_asynchrony.html>

Fried, I., Rutishauser, U., Cerf, M. & Kreiman, G. Single Neuron Studies of the Human Brain. Probing Cognition. Probing cognition (MIT Press, 2014).

Tang, H. et al. Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex. Neuron 83, 736 - 748 (2014).

Mormann, F. et al. Single neuron studies of the human brain. Probing cognition (2014).

Kreiman, G. Principles of neural coding (2013).

Liu, H., Agam, Y., Madsen, J. & Kreiman, G. Timing, timing, timing: Fast decoding of object inforrmation from intracranial field potentials in human visual cortex. (2009). at <http://klab.tch.harvard.edu/resources/liuetal_timing3.html>