All Publications

2020

S. J. Gershman, “Origin of perseveration in the trade-off between reward and complexity”, Cognition, vol. 204, p. 104394, 2020.
CBMM Related
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.PDF icon gk7967.pdf (3.07 MB)
CBMM Funded
G. Ben-Yosef, Kreiman, G., and Ullman, S., “Minimal videos: Trade-off between spatial and temporal information in human and machine vision.”, Cognition, 2020.
CBMM Funded
H. Sanders, Wilson, M. A., and Gershman, S. J., “Hippocampal remapping as hidden state inference”, eLife, vol. 9, 2020.
CBMM Funded
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.PDF icon gk7791.pdf (2.39 MB)
CBMM Funded
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.PDF icon 1805.10734.pdf (9.59 MB)
CBMM Funded
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.PDF icon gk7812.pdf (1.93 MB)
CBMM Funded
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.
CBMM Funded
V. Jacquot, Ying, J., and Kreiman, G., “Can Deep Learning Recognize Subtle Human Activities?”, CVPR 2020, 2020.
CBMM Funded
M. Zhang, Tseng, C., and Kreiman, G., “Putting visual object recognition in context”, CVPR 2020, 2020.PDF icon gk7876.pdf (3.12 MB)
CBMM Funded

2019

A. Barbu, Mayo, D., Alverio, J., Luo, W., Wang, C., Gutfreund, D., Tenenbaum, J. B., and Katz, B., “ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf (16.31 MB)
CBMM Funded
L. A. Becker, Penagos, H., Manoach, D. S., Wilson, M. A., and Varela, C., “Disruption of CA1 Sharp-Wave Ripples by the nonbenzodiazepine hypnotic eszopiclone ”, in Society for Neuroscience, Chicago, IL, USA, 2019.
CBMM Funded
I. Dalla Betta, Cassara, A., Boyden, E. S., Brown, E. N., francisco,, and Flores, F. J., “In silico modeling of temporally interfering electric fields for deep brain stimulation ”, in Society for Neuroscience, Chicago, IL, USA, 2019.
CBMM Funded
J. Harrod, Purdon, P. L., Brown, E. N., and Flores, F. J., “Identification of vigilance states in freely behaving animals using thalamocortical activity and Deep Belief networks”, in Society for Neuroscience, 2019.
CBMM Funded
G. Kreiman, “It's a small dimensional world after all”, Physics of Life Reviews, vol. 29, pp. 96 - 97, 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.PDF icon Author's last draft (20.26 MB)
CBMM Funded
E. H. Patzelt, Kool, W., Millner, A. J., and Gershman, S. J., “Incentives Boost Model-Based Control Across a Range of Severity on Several Psychiatric Constructs”, Biological Psychiatry, vol. 85, no. 5, pp. 425 - 433, 2019.
CBMM Funded
E. H. Patzelt, Kool, W., Millner, A. J., and Gershman, S. J., “The transdiagnostic structure of mental effort avoidance”, Scientific Reports, vol. 9, no. 1, 2019.
CBMM Related
G. Kreiman, “What do neurons really want? The role of semantics in cortical representations.”, in Psychology of Learning and Motivation, vol. 70, 2019.
CBMM Funded
W. Xiao, Chen, H., Liao, Q., and Poggio, T., “Biologically-plausible learning algorithms can scale to large datasets.”, in International Conference on Learning Representations, (ICLR 2019), 2019.PDF icon gk7779.pdf (721.53 KB)
CBMM Funded

2018

W. Kool and Botvinick, M., “Mental labour”, Nature Human Behaviour, vol. 2, no. 12, pp. 899 - 908, 2018.
CBMM Funded
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.
CBMM Funded
W. Kool, Gershman, S. J., and Cushman, F. A., “Planning Complexity Registers as a Cost in Metacontrol”, Journal of Cognitive Neuroscience, vol. 30, no. 10, pp. 1391 - 1404, 2018.
CBMM Funded

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