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Singer, J., Madsen, J., Anderson, W. S. & Kreiman, G. Sensitivity to Timing and Order in Human Visual Cortex. (2014).PDF icon CBMM-Memo-005.pdf (1.12 MB)
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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).PDF icon CBMM-Memo-009.pdf (4.21 MB)
Cheney, N., Schrimpf, M. & Kreiman, G. On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. (2017).PDF icon CBMM-Memo-065.pdf (687.76 KB)
Tang, H. et al. Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115PDF icon 1719397115.full_.pdf (1.1 MB)
Tang, H., Kreiman, G. & Zhao, Q. Computational and Cognitive Neuroscience of Vision (Springer Singapore, 2017). at <http://www.springer.com/us/book/9789811002113>
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Zhang, M., Tseng, C. & Kreiman, G. Putting visual object recognition in context. CVPR 2020 (2020).PDF icon gk7876.pdf (3.12 MB)
Lotter, W., Kreiman, G. & Cox, D. PredNet - "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning" [code]. (2016).
Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).
Tang, H. et al. Predicting episodic memory formation for movie events [code]. (2016).
Tang, H. et al. Predicting episodic memory formation for movie events. Scientific Reports (2016). doi:10.1038/srep30175
Kreiman, G. People, objects and interactions in movies. (2014).
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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.2110.03605PDF icon 2110.03605.pdf (6.7 MB)
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Kreiman, G. A null model for cortical representations with grandmothers galore. Language, Cognition and Neuroscience 274 - 285 (2017). doi:10.1080/23273798.2016.1218033
Miconi, T., Groomes, L. & Kreiman, G. A normalization model of visual search predicts single trial human fixations in an object search task. (2014).PDF icon CBMM-Memo-008.pdf (854.51 KB)
Kreiman, G., Rutishauser, U., Cerf, M. & Fried, I. Single neuron studies of the human brain. Probing cognition (2014).
Zheng, J. et al. Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience 25, 358 - 368 (2022).
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>PDF icon 1805.10734.pdf (9.59 MB)
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).PDF icon 1805.10734.pdf (9.59 MB)
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).
Madhavan, R. et al. Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cerebral Cortex 1–17, (2018).
Kreiman, G. Neural Information Processing Systems (NIPS) 2015 Review. (2016).PDF icon Read the Views & Review article by Gabriel Kreiman (443.87 KB)
Bansal, A. et al. Neural Dynamics Underlying Target Detection in the Human Brain. Journal of Neuroscience 34, (2014).
Kreiman, G. Cognitive Neuroscience V, (2014).
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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
Misra, P., Marconi, A., Peterson, M. F. & Kreiman, G. Minimal memory for details in real life events. Scientific Reports 8, (2018).

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