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Gabriel Kreiman

Ophthalmology The Children's Hospital and Harvard Medical School, Boston, MA, United States 
Computational Neuroscience, Visual Object Recognition, Visual Cortex
"Gabriel Kreiman"

Mean distance: 11.95 (cluster 29)
Cross-listing: Chemistry Tree


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Christof Koch grad student 2002 Caltech
 (On the neuronal activity in the human brain during visual recognition, imagery and binocular rivalry.)
Mark Konishi grad student 2002 Caltech
 (On the neuronal activity in the human brain during visual recognition, imagery and binocular rivalry)
Tomaso A. Poggio post-doc MIT
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Xiao W, Kreiman G. (2020) XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. Plos Computational Biology. 16: e1007973
Ben-Yosef G, Kreiman G, Ullman S. (2020) Minimal videos: Trade-off between spatial and temporal information in human and machine vision. Cognition. 201: 104263
Kreiman G, Serre T. (2020) Beyond the feedforward sweep: feedback computations in the visual cortex. Annals of the New York Academy of Sciences
Ponce CR, Xiao W, Schade PF, et al. (2019) Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell. 177: 999-1009.e10
Madhavan R, Bansal AK, Madsen JR, et al. (2018) Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cerebral Cortex (New York, N.Y. : 1991)
Zhang M, Feng J, Ma KT, et al. (2018) Finding any Waldo with zero-shot invariant and efficient visual search. Nature Communications. 9: 3730
Tang H, Schrimpf M, Lotter W, et al. (2018) Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences of the United States of America
Isik L, Singer J, Madsen JR, et al. (2017) What is changing when: Decoding visual information in movies from human intracranial recordings. Neuroimage
Tang H, Singer J, Ison MJ, et al. (2016) Predicting episodic memory formation for movie events. Scientific Reports. 6: 30175
Gómez-Laberge C, Smolyanskaya A, Nassi JJ, et al. (2016) Bottom-Up and Top-Down Input Augment the Variability of Cortical Neurons. Neuron
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