Gabriel Kreiman

Gabriel Kreiman
Gabriel
Kreiman
Associate Director, Research Module Co-Leader
Harvard University, Children’s Hospital Boston

Associated Research Module: 

Associated Research Thrust: 

Gabriel Kreiman is Professor in the Department of Ophthalmology at Harvard Medical School. He is also a faculty at Children’s Hospital, the Department of Neurology at HMS, the Center for Brain Science, the Swartz Center for Theoretical Neuroscience and the Mind, Brain and Behavior Initiative at Harvard. He studied Physical Chemistry for his B.Sc. at the University of Buenos Aires, Argentina (1996). He received a M.Sc. and Ph.D. (Biology and Computational Neuroscience) from the California Institute of Technology in 2002 under Prof. Koch’s mentorship. He pursued postdoctoral work with Prof. Poggio at MIT. The Kreiman Laboratory combines computational modeling, neurophysiological recordings and psychophysical measurements to further our understanding of the neuronal circuits and mechanisms underlying perception and cognition.

Email:  gabriel.kreiman@tch.harvard.edu
Phone:  (781) 214-0756
Faculty Profile
Website: 
Lab Website:  Kreiman lab

Current Advisees

Marcelo Armendariz - Postdoc
Frederico Azevedo - Research Scientist
Trenton Bricken - Graduate Student
Chenguang Li - Graduate Student
Spandan Madan - Graduate Student
Diego Mendoza-Halliday - Research Scientist
Pranav Misra - Graduate Student
Martin Schrimpf - Research Scientist
Morgan Talbot - Graduate Student
Annabelle Tao - Graduate Student
Will Xiao - Graduate Student
Zechen Zhang - Graduate Student

Past Advisees

Katarina Bendtz - Postdoc, Human Frontiers Science Program Cross Disciplinary Fellow
Stephen Casper - Undergrad
Emma Giles - Graduate Student
Stephan Grzelkowski - Graduate Student
Eleonora Iaselli - Graduate Student
Jiye Kim - Postdoc
Emma Krause - Graduate Student
Bill Lotter - Graduate Student
Boying Meng - Graduate Student
Joseph Olson - Graduate Student
Kristofor Payer - Research Staff
Nimrod Shaham - Postdoc
Duncan Stothers - Undergraduate Student
Kasper Vinken - Postdoc
Jerry Wang - Graduate Student
Farahnaz Wick - Postdoc
Kevin Wu - Graduate Student
Eric Wu - Graduate Student
Yuchen Xiao - Graduate Student
Zihao Xu - Graduate Student
Mengmi Zhang - Postdoc

Projects

Computational architectures mediating object recognition under occlusion
Developmental dynamics of selectivity and invariance in macaque inferotemporal cortex
Investigating neural signals underlying language processing in the human brain
Neural circuits for pattern completion
Selectivity and invariance in dynamic movie stimuli
Task Dependence of Visual Representations
Temporal integration in visual recognition

CBMM Publications

A. Sikarwar and Kreiman, G., “On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering”, arXiv, 2022.
Y. Xiao, Chou, C. - C., Cosgrove, G. Rees, Crone, N. E., Stone, S., Madsen, J. R., Reucroft, I., Shih, Y. - C., Weisholtz, D., Yu, H. - Y., Anderson, W. S., and Kreiman, G., “Task-specific neural processes underlying conflict resolution during cognitive control”, BioRxiv, 2022.
S. Casper, Nadeau, M., and Kreiman, G., “One thing to fool them all: generating interpretable, universal, and physically-realizable adversarial features”, arXiv, 2022.
A. Bardon, Xiao, W., Ponce, C. R., Livingstone, M. S., and Kreiman, G., “Face neurons encode nonsemantic features”, Proceedings of the National Academy of Sciences, vol. 119, no. 16, 2022.
M. Armendariz, Xiao, W., Vinken, K., and Kreiman, G., “Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions”, Cognitive Neuropsychology, pp. 1 - 3, 2022.
J. Zheng, Schjetnan, A. G. P., Yebra, M., Gomes, B. A., Mosher, C. P., Kalia, S. K., Valiante, T. A., Mamelak, A. N., Kreiman, G., and Rutishauser, U., “Neurons detect cognitive boundaries to structure episodic memories in humans”, Nature Neuroscience, vol. 25, no. 3, pp. 358 - 368, 2022.
N. Shaham, Chandra, J., Kreiman, G., and Sompolinsky, H., “Stochastic consolidation of lifelong memoryAbstract”, Scientific Reports, vol. 12, no. 1, 2022.
G. Dellaferrera and 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, vol. 162. pp. 4937-4955, 2022.
J. Zheng, Schjetnan, A. Gómez Pal, Yebra, M., Mosher, C., Kalia, S., Valiante, T. A., Mamelak, A. N., Kreiman, G., and Rutishauser, U., “Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation”, bioRxiv, 2021.
G. Kreiman, Biological and Computer Vision. Cambridge, UK: Cambridge University Press, 2021.
M. Zhang, Badkundri, R., Talbot, M. B., Zawar, R., and Kreiman, G., “Hypothesis-driven Online Video Stream Learning with Augmented Memory”, arXiv, 2021.
M. Zhang and Kreiman, G., “Beauty is in the eye of the machine”, Nature Human Behaviour, vol. 5, no. 6, pp. 675 - 676, 2021.
S. Casper, Boix, X., D'Amario, V., Guo, L., Schrimpf, M., Vinken, K., and Kreiman, G., “Frivolous Units: Wider Networks Are Not Really That Wide”, in AAAI 2021, 2021.
P. Bomatter, Zhang, M., Karev, D., Madan, S., Tseng, C., and Kreiman, G., “When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes”, International Conference on Computer Vision (ICCV) . 2021.
J. Wang, Tao, A., Anderson, W. S., Madsen, J. R., and Kreiman, G., “Mesoscopic physiological interactions in the human brain reveal small-world properties”, Cell Reports, vol. 36, no. 8, p. 109585, 2021.
S. Kant Gupta, Zhang, M., WU, C. H. I. A. - C. H. I. E. N., Wolfe, J., and Kreiman, G., “Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases”, NeurIPS 2021. 2021.
D. S. Weisholtz, Kreiman, G., Silbersweig, D. A., Stern, E., Cha, B., and Butler, T., “Localized task-invariant emotional valence encoding revealed by intracranial recordingsAbstract”, Social Cognitive and Affective Neuroscience, 2021.
V. Jacquot, Ying, J., and Kreiman, G., “Can Deep Learning Recognize Subtle Human Activities?”, CVPR 2020, 2020.
M. Zhang, Tseng, C., and Kreiman, G., “Putting visual object recognition in context”, CVPR 2020, 2020.
G. Kreiman and Serre, T., “Beyond the feedforward sweep: feedback computations in the visual cortex”, Ann. N.Y. Acad. Sci. | Special Issue: The Year in Cognitive Neuroscience, vol. 1464, no. 1, pp. 222-241, 2020.
G. Kreiman and Serre, T., “Beyond the feedforward sweep: feedback computations in the visual cortex”, Annals of the New York Academy of Sciences, vol. 1464, no. 1, pp. 222 - 241, 2020.
W. Lotter, Kreiman, G., and Cox, D., “A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception.”, Nature Machine Learning, 2020.
G. Ben-Yosef, Kreiman, G., and Ullman, S., “What can human minimal videos tell us about dynamic recognition models?”, in International Conference on Learning Representations (ICLR 2020), Virtual Conference, 2020.
W. Lotter, Kreiman, G., and Cox, D., “A neural network trained for prediction mimics diverse features of biological neurons and perception”, Nature Machine Intelligence, vol. 2, no. 4, pp. 210 - 219, 2020.
W. Xiao and Kreiman, G., “XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization”, PLOS Computational Biology, vol. 16, no. 6, p. e1007973, 2020.
G. Ben-Yosef, Kreiman, G., and Ullman, S., “Minimal videos: Trade-off between spatial and temporal information in human and machine vision.”, Cognition, 2020.
K. Vinken, Boix, X., and Kreiman, G., “Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception”, Science Advances, vol. 6, no. 42, p. eabd4205, 2020.
G. Kreiman, “What do neurons really want? The role of semantics in cortical representations.”, in Psychology of Learning and Motivation, vol. 70, 2019.
C. R. Ponce, Xiao, W., Schade, P. F., Hartmann, T. S., Kreiman, G., and Livingstone, M. S., “Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences”, Cell , vol. 177, p. 1009, 2019.
G. Kreiman, “It's a small dimensional world after all”, Physics of Life Reviews, vol. 29, pp. 96 - 97, 2019.
K. Wu, Wu, E., and Kreiman, G., “Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition”, arXiv | Cornell University, vol. arXiv:1803.01967, 2018.
W. Lotter, Kreiman, G., and Cox, D., “A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception”, arXiv | Cornell University, arXiv:1805.10734v2, 2018.
W. Eric, Kevin, W., and Kreiman, G., “Learning scene gist with convolutional neural networks to improve object recognition”, 2018 52nd Annual Conference on Information Sciences and Systems (CISS). Princeton, NJ, 2018.
A. Palepu and Kreiman, G., “Development of automated interictal spike detector”, 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018. Honolulu, HI, 2018.
M. Zhang, Feng, J., Lim, J. Hwee, Zhao, Q., and Kreiman, G., “What am I searching for?”. 2018.
H. Tang, Schrimpf, M., Lotter, W., Moerman, C., Paredes, A., Caro, J. Ortega, Hardesty, W., Cox, D., and Kreiman, G., “Recurrent computations for visual pattern completion”, Proceedings of the National Academy of Sciences, 2018.
M. Zhang, Feng, J., Ma, K. Teck, Lim, J. Hwee, Zhao, Q., and Kreiman, G., “Finding any Waldo with zero-shot invariant and efficient visual search”, Nature Communications, vol. 9, 2018.
L. Isik, Singer, J., Kanwisher, N., ,, WS, A., and Kreiman, G., “What is changing when: decoding visual information in movies from human intracranial recordings”, NeuroImage, vol. 180, Part A, pp. 147-159, 2018.
G. Ben-Yosef, Kreiman, G., and Ullman, S., “Spatiotemporal interpretation features in the recognition of dynamic images”. 2018.
R. Madhavan, Bansal, A. K., Madsen, J., Golby, A. J., Tierney, T. S., Eskandar, E., Anderson, W. S., and Kreiman, G., “Neural Interactions Underlying Visuomotor Associations in the Human Brain”, Cerebral Cortex, vol. 1–17, 2018.
P. Misra, Marconi, A., Peterson, M. F., and Kreiman, G., “Minimal memory for details in real life events”, Scientific Reports, vol. 8, no. 1, 2018.
H. Tang, Kreiman, G., and Zhao, Q., “Recognition of occluded objects”, in Computational and Cognitive Neuroscience of Vision, Springer Singapore, 2017.
L. Isik, Singer, J., Madsen, J., Kanwisher, N., and Kreiman, G., “What is changing when: Decoding visual information in movies from human intracranial recordings”, Neuroimage, 2017.
W. Lotter, Kreiman, G., and Cox, D., “Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning”, in ICLR, 2017.
N. Cheney, Schrimpf, M., and Kreiman, G., “On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations”. 2017.
W. Lotter, Kreiman, G., and Cox, D., “Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning”. 2017.
G. Kreiman, “A null model for cortical representations with grandmothers galore”, Language, Cognition and Neuroscience, pp. 274 - 285, 2017.
H. Tang, Singer, J., Ison, M. J., Pivazyan, G., Romaine, M., Meller, E., Perron, V., Arlellano, M., Kreiman, G., Romaine, M., Boulin, A., Frias, R., Carroll, J., and Dowcett, S., “A machine learning approach to predict episodic memory formation”, 2016 Annual Conference on Information Science and Systems (CISS) . Princeton, NJ , pp. 539 - 544 , 2016.
T. Miconi, Groomes, L., and Kreiman, G., “There’s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [dataset]”. 2016.
T. Miconi, Groomes, L., and Kreiman, G., “There’s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task [code]”. 2016.
W. Lotter, Kreiman, G., and Cox, D., “PredNet - "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning" [code]”. 2016.
C. Gómez-Laberge, Smolyanskaya, A., Nassi, J. J., Kreiman, G., and Born, R. T., “Bottom-up and Top-down Input Augment the Variability of Cortical Neurons.”, Neuron, vol. 91(3), pp. 540-547, 2016.
H. Tang, Singer, J., Ison, M., Pivazyan, G., Romaine, M., Frias, R., Meller, E., Boulin, A., Carroll, J., Perron, V., Dowcett, S., Arlellano, M., and Kreiman, G., “Predicting episodic memory formation for movie events [dataset]”. 2016.
H. Tang, Yu, H. - Y., Chou, C. - C., Crone, N. E., Madsen, J., Anderson, W. S., and Kreiman, G., “Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]”. eLife, 2016.
T. Miconi, Groomes, L., and Kreiman, G., “There's Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task”, Cerebral Cortex, vol. 26(7), pp. 26:3064-3082, 2016.
H. Tang, Singer, J., Ison, M., Pivazyan, G., Romaine, M., Frias, R., Meller, E., Boulin, A., Carroll, J., Perron, V., Dowcett, S., Arlellano, M., and Kreiman, G., “Predicting episodic memory formation for movie events [code]”. 2016.
H. Tang, Yu, H. - Y., Chou, C. - C., Crone, N. E., Madsen, J., Anderson, W. S., and Kreiman, G., “Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]”. eLife, 2016.
G. Kreiman, “Neural Information Processing Systems (NIPS) 2015 Review”. 2016.
H. Tang, Yu, H. Y., Chou, C. C., Crone, N. E., Madsen, J., Anderson, W. S., and Kreiman, G., “Cascade of neural processing orchestrates cognitive control in human frontal cortex”, eLIFE, 2016.
H. Tang, Singer, J., Ison, M. J., Pivazyan, G., Romaine, M., Frias, R., Meller, E., Boulin, A., Carroll, J., Perron, V., Dowcett, S., Arellano, M., and Kreiman, G., “Predicting episodic memory formation for movie events”, Scientific Reports, no. 1, 2016.
W. Lotter, Kreiman, G., and Cox, D., “Unsupervised Learning of Visual Structure using Predictive Generative Networks”, in International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, 2016.
R. Madhavan, Millman, D., Tang, H., Crone, N. E., Lenz, F. A., Tierney, T. S., Madsen, J., Kreiman, G., and Anderson, W. S., “Decrease in gamma-band activity tracks sequence learning”, Frontiers in Systems Neuroscience, vol. 8, 2015.
W. Lotter, Kreiman, G., and Cox, D., “UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS”. 2015.
J. Singer, Madsen, J., Anderson, W. S., and Kreiman, G., “Sensitivity to timing and order in human visual cortex”, Journal of Neurophysiology, vol. 113, no. 5, pp. 1656 - 1669, 2015.
G. Kreiman, “Neural correlates of consciousness: perception and volition”, in Cognitive Neuroscience, vol. V, 2014.
G. Kreiman, Rutishauser, U., Cerf, M., and Fried, I., “The next ten years and beyond”, in Single neuron studies of the human brain. Probing cognition, 2014.
F. Mormann, Ison, M. J., Quiroga, R. Q., Koch, C., Fried, I., and Kreiman, G., “Visual cognitive adventures of single neurons in the human medial temporal lobe”, in Single neuron studies of the human brain. Probing cognition, 2014.
U. Rutishauser, Cerf, M., and Kreiman, G., “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 Brain: Probing Cognition, 2014.
I. Fried, Rutishauser, U., Cerf, M., and Kreiman, G., Single Neuron Studies of the Human Brain. Probing Cognition. Cambridge, MA: MIT Press, 2014.
J. Singer and Kreiman, G., “Short temporal asynchrony disrupts visual object recognition”. Journal of Vision, 2014.
J. Singer and Kreiman, G., “Short temporal asynchrony disrupts visual object recognition”. Journal of Vision, 2014.
A. Bansal, Madhavan, R., Agam, Y., Golby, A., Madsen, J., and Kreiman, G., “Neural Dynamics Underlying Target Detection in the Human Brain”, Journal of Neuroscience, vol. 34, 2014.
J. Singer and Kreiman, G., “Short temporal asynchrony disrupts visual object recognition”, Journal of Vision, vol. 12, no. 14, 2014.
H. Tang, Buia, C., Madsen, J., Anderson, W. S., and Kreiman, G., “A role for recurrent processing in object completion: neurophysiological, psychophysical and computational evidence.”. 2014.
T. Miconi, Groomes, L., and Kreiman, G., “A normalization model of visual search predicts single trial human fixations in an object search task.”. 2014.
J. Singer, Madsen, J., Anderson, W. S., and Kreiman, G., “Sensitivity to Timing and Order in Human Visual Cortex.”. 2014.
J. J. Nassi, Gomez-Laberge, C., Kreiman, G., and Born, R. T., “Corticocortical feedback increases the spatial extent of normalization.”, Frontiers in Systems Neuroscience, vol. 8, p. 105, 2014.
G. Kreiman, “People, objects and interactions in movies”. 2014.
H. Tang, Buia, C., Madhavan, R., Crone, N. E., Madsen, J., Anderson, W. S., and Kreiman, G., “Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex”, Neuron, vol. 83, no. 3, pp. 736 - 748, 2014.
J. Singer and Kreiman, G., “Short temporal asynchrony disrupts visual object recognition.”, J Vis, vol. 14, no. 5, p. 7, 2014.
G. Kreiman, “Computational models of visual object recognition”, in Principles of neural coding, 2013.
H. Liu, Agam, Y., Madsen, J., and Kreiman, G., “Timing, timing, timing: Fast decoding of object inforrmation from intracranial field potentials in human visual cortex”. Neuron, 2009.