Looking for theses from the Kreiman lab?
2026
Zhang, M., Pavarino, E., Liu, X., Dellaferrera, G., Sikarwar, A., Armendariz, M., Mudrik, N., Agrawal, P., Madan, S., Barbu, A., Yang, H., Kumar, T., Sadwani, M., Dellaferrera, S., Pizzochero, M., Pfister, H., & Kreiman, G. (2026). Human or Machine? Turing Tests for Vision and Language. Nature Human Behavior. PDF | Resources
Tausani, L., Muratore, P., Talbot, M. B., Amerio, G., Kreiman, G., & Zoccolan, D. (2026). Stretching Beyond the Obvious: A Gradient-Free Framework to Unveil the Hidden Landscape of Visual Invariance. International Conference on Learning Representations (ICLR). PDF | Resources
Shang, J., Kreiman, G., & Sompolinsky, H. (2026). Unraveling the geometry of visual relational reasoning. Scientific Reports. PDF
2025
Talbot, M. B., Kreiman, G., DiCarlo, J. J., & Gaziv, G. (2025). L-WISE: Boosting Human Image Category Learning Through Model-Based Image Selection And Enhancement. International Conference on Learning Representaitons (ICLR). PDF | Resources
Seedat, A., Lepauvre, A., Jeschke, J., Gorska-Klimowska, U., Armendariz, M., Bentz, K., Henin, S., Hirschhorn, R., Brown, T., Jensen, E., Kozma, C., Mazumder, D., Montenegro, S., Yu, L., Kahraman, K., Sripad, P., Devinsky, O., Dugan, P., Doyle, W., … Melloni, L. (2025). Open multi-center intracranial electroencephalography dataset with task probing conscious visual perception. Scientific Data, 12, 854. PDF
Jain, V., Alves Feitosa, F., & Kreiman, G. (2025). HumorDB: Can AI understand graphical humor? International Conference on Computer Vision (ICCV). PDF
Ferrante, O., Gorska, U., Henin, S., Hirschhorn, R., Khalaf, A., Lepauvre, A., Liu, L., Richter, D., Vidal, Y., Bonacchi, N., Brown, T., Sripad, P., Armendariz, M., Bendtz, K., Ghafari, T., Hetenyi, D., Jeschke, J., Kozma, C., Mazumder, D., … Melloni, L. (2025). An adversarial collaboration to critically evaluate theories of consciousness. Nature, In Press. PDF
Casile, A., Cordier, A., Kim, J., Cometa, A., Madsen, J., Stone, S., Ben-Yosef, G., Ullman, S., Anderson, W., & Kreiman, G. (2025). Neural correlates of minimal recognizable configurations in the human brain. Cell Reports, 44, 115429. PDF | Resources
Bono, S., Madan, S., Grover, I., Yasueda, M., Breazeal, C., Pfister, H., & Kreiman, G. (2025). The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function. AAAI. PDF
Bechard, D., & Kreiman, G. (2025). People want AI to help artists, not be the artist. Scientific American. PDF
Armendariz, M., Blumberg, J., Singer, J., Aiple, F., Kim, J., Dominguez-Iturza, N., Brandt, A., Reinacher, P., Schulze-Bonhage, A., & Kreiman, G. (2025). Single-neuron encoding of rapidly learned visual information reshapes human perception. bioRxiv, 2025.08.04.668333. PDF | Resources
Zheng, J., Pavarino, E. C., Skelin, I., Yebra, M., Schwencke, J., Zhu, W., Reed, C. M., Kalia, S. K., Valiante, T. A., Ojemann, S. G., Kramer, D. R., Thompson, J. A., Mamelak, A. N., Kreiman, G., & Rutishauser, U. (2025). Order-Selective Cells Tile Temporal Space and Predict Order Memory in Humans. bioRxiv 2025.10.24.683575. PDF
Talbot, M. B., Zawar, R., Badkundri, R., Zhang, M., & Kreiman, G. (2023). Tuned compositional feature replays for efficient stream learning. IEEE Transactions on Neural Networks and Learning Systems, PP. PDF | Resources
2024
Zheng, J., Yebra, M., Schjetnan, A., Mosher, C., Kalia, A., Chung, J., Reed, C., Valiante, T., Mamelak, A., Kreiman, G., & Rutishauser, U. (2024). Hippocampal theta phase precession supports memory formation and retrieval of naturalistic experience in humans. Nature Human Behavior, 8, 2423–2436. PDF | Resources
Xiao, W., Sharma, S., Kreiman, G., & Livingstone, M. (2024). Feature-selective responses in macaque visual cortex follow eye movements during natural vision. Nature Neuroscience, 6, 1157–1166. PDF | Resources
Wang, C., Yaari, A., Singh, A., Subramaniam, V., Rosenfarb, D., Misra, P., Madsen, J., Stone, S., Kreiman, G., Katz, B., Cases, I., & Barbu, A. (2024). Brain treebank: Large-scale intracranial recordings from naturalistic language stimuli. NeurIPS. PDF | Resources
Subramaniam, V., Wang, C., Barbu, A., Kreiman, G., & Katz, B. (2024). Revealing vision-language integration in the brain with multimodal networks. International Conference on Machine Learning (ICML). PDF | Resources
Srinivasan, R., Mignacco, F., Sorbaro, M., Refinetti, M., Cooper, A., Kreiman, G., & Dellaferrera, G. (2024). Forward learning with top-down feedback: empirical and analytical characterization. International Conference on Learning Representations (ICLR). PDF | Resources
Misra, P., Shih, Y., Yu, H., Weisholtz, D., Madsen, J., Sceillig, S., & Kreiman, G. (2024). Invariant neural representation of parts of speech in the human brain. bioRxiv, 2024.01.15.575788. PDF
Madan, S., Li, Y., Zhang, M., Pfister, H., & Kreiman, G. (2024). Improving generalization by mimicking the human visual diet. bioRxiv, 2206.07802. PDF | Resources
Madan, S., Xiao, W., Cao, M., Pfister, H., Livingstone, M., & Kreiman, G. (2024). Benchmarking out-of-distribution generalization capabilities of DNN-based encoding models for the ventral visual cortex. NeurIPS. PDF | Resources
Li, C., Brenner, J. W., Boesky, A., Ramanathan, S., & Kreiman, G. (2024). Neuron-level prediction and noise can implement flexible reward-seeking behavior. bioRxiv, 2024.05.22.595306. PDF
Li, C., Kreiman, G., & Ramanathan, S. (2024). Discovering neural policies to drive behavior by integrating deep reinforcement learning agents with biological neural networks. Nature Machine Intelligence, 6, 726–738. PDF | Resources
Djambazovska, S., Zafer, A., Ramezanpour, H., Kreiman, G., & Kar, K. (2024). The impact of scene context on visual object recognition: comparing humans, monkeys, and computaitonal models. bioRxiv, 2024.05.27.596127. PDF
Hidalgo, D., Dellaferrera, G., Xiao, W., Papadopouli, M., Smirnakis, S., & Kreiman, G. (2024). Trial-by-trial inter-areal interactions in visual cortex in the presence or absence of visual stimulation. bioRxiv, 2024.12.05.626981. PDF | Resources
2023
Zhang, Y., Aghajan, Z., Ison, M., Lu, Q., Tang, H., Kalender, G., Monsoor, T., Zheng, J., Kreiman, G., Roychowdhury, V., & Fried, I. (2023). Decoding of human identity by computer vision and neuronal vision. Scientific Reports, 13, 651. PDF
Xiao, Y., Sanchez Lopez, P., Wu, R., Wei, P., Shan, Y., Weisholtz, D., Cosgrove, C., Madsen, J., Stone, S., Zhao, G., & Kreiman, G. (2023). Integration of recognition, episodic, and associative memories during complex human behavior. bioRxiv, 2023.03.27.534384. PDF
Xiao, Y., Chou, C. C., Cosgrove, G. R., Crone, N. E., Stone, S., Madsen, J. R., Reucroft, I., Shih, Y. C., Weisholtz, D., Yu, H. Y., Anderson, W. S., & Kreiman, G. (2023). Cross-task specificity and within-task invariance of cognitive control processes. Cell Rep, 42(1), 111919. PDF | Resources
Xiao, W., Zhang, M., & Kreiman, G. (2023). Artificial Intelligence in Neuroscience. In F. Akter, N. Emptage, F. Engert, & M. Berger (Eds.), Neuroscience for neurosurgeons. Cambridge University Press. PDF
Wang, C., Subramaniam, V., Yaari, A., Kreiman, G., Katz, B., Cases, I., & Barbu, A. (2023). BrainBERT: Self-supervised representation learning for Intracranial Electrodes. International Conference on Learning Representations (ICLR). PDF | Resources
Triggiani, A. I., Kreiman, G., Lewis, C., Maoz, U., Mele, A., Mudrik, L., Roskies, A., Schurger, A., & Hallett, M. (2023). What is the intention to move and when does it occur? Neurosci Biobehav Rev, 105199. PDF
Singh, P., Li, Y., Sikarwar, A., Lei, W., Gao, D., Talbot, M., Sun, Y., Shou, M., Kreiman, G., & Zhang, M. (2023). Learning to Learn: How to Continuously Teach Humans and Machines. International Conference on Computer Vision (ICCV). PDF | Resources
Melloni, L., Mudrik, L., Pitts, M., Bendtz, K., Ferrante, O., Gorska, U., Hirschhorn, R., Khalaf, A., Kozma, C., Lepauvre, A., Liu, L., Mazumder, D., Richter, D., Zhou, H., Blumenfeld, H., Boly, M., Chalmers, D. J., Devore, S., Fallon, F., … Tononi, G. (2023). An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLoS One, 18(2), e0268577. PDF
Kreiman, G. (2023). Neural coding: Stimulating cortex to alter visual perception. Current Biology, 33, R117–R118. PDF
Casper, S., Killian, T., Kreiman, G., & Hadfield-Mennell, D. (2023). White-box adversarial policies against RL agents. arXiv, 2209.02167. PDF | Resources
Bricken, T., Davies, A., Singh, D., Krotov, D., & Kreiman, G. (2023). Sparse distributed memory is a continual learner. International Conference on Learning Representations (ICLR). PDF
Bricken, T., Schaeffer, R., Olshausen, B. A., & Kreiman, G. (2023). Emergence of sparse representations from noise. International Conferenece on Machine Learning (ICML). PDF
Aghajan, Z., Kreiman, G., & Fried, I. (2023). Minute-scale periodicity of neuronal firing in the human entorhinal cortex. Cell Reports, 42, 113271. PDF | Resources
2022
Zheng, J., Schjetnan, A., Yebra, M., Mosher, C., Kalia, S., Valiante, T., Mamelak, A., Kreiman, G., & Rutishauser, U. (2022). Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience, 25, 358–368. PDF | Resources
Zhang, M., Armendariz, M., Xiao, W., Rose, O., Bendtz, K., Livingstone, M., Ponce, C., & Kreiman, G. (2022). Look twice: A generalist computational model predicts return fixations across tasks and species. PLoS Comput Biol, 18(11), e1010654. PDF | Resources
Sikarwar, A., & Kreiman, G. (2022). On the efficacy of co-attention transformer layers in visual question answering. arXiv, 2201.03965. PDF
Shaham, N., Chandra, J., Kreiman, G., & Sompolinsky, H. (2022). Stochastic consolidation of lifelong memory. Scientific Reports, 12, 13107. PDF | Resources
Murugan, R., & Kreiman, G. (2022). Multiple transcription auto regulatory loops can act as robust oscillators and decision-making motifs. Computational and Structural Biotechnology Journal, 20, 5155–5135. PDF
Liu, X., Sikarwar, A., Lim, J., Kreiman, G., Shi, Z., & Zhang, M. (2022). Reason from context with self-supervised learning. arXiv, 2211.12817. PDF
Hoogsteen, K., Szpiro, S., Kreiman, G., & Peli, E. (2022). Beyond the cane: describing urban scenes to blind people for mobility tasks. ACM Transactions on Accessible Computing. PDF
Ding, Z., Ren, X., David, E., Vo, M., Kreiman, G., & Zhang, M. (2022). Efficient zero-shot visual wearch via target and context-aware transformer. arXiv, 2211.13470. PDF
Dellaferrera, G., & Kreiman, G. (2022). Error-driven input modulation: solving the credit assignment problem without a backward pass. Proceedings of Machine Learning Research (International Conference on Machine Learning (ICML)), 162, 4937–4955. PDF | Resources
Casper, S., Nadeau, M., & Kreiman, G. (2022). Robust feature-level adversaries are interpretability tools. Neural Information Processing Systems (NeurIPS), 36. PDF | Resources
Bardon, A., Xiao, W., Ponce, C. R., Livingstone, M. S., & Kreiman, G. (2022). Face neurons encode nonsemantic features. Proc Natl Acad Sci U S A, 119(16), e2118705119. PDF | Resources
Armendariz, M., W, Xiao., Vinken, K., & Kreiman, G. (2022). Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions. Cognitive Neuropsychology, 38, 75–77. PDF
2021
Zhang, M., & Kreiman, G. (2021). Beauty is in the eye of the machine. Nature Human Behaviour, 5 PDF
Weisholtz, D. S., Kreiman, G., Silbersweig, D. A., Stern, E., Cha, B., & Butler, T. (2021). Localized Task-Invariant Emotional Valence Encoding Revealed by Intracranial Recordings. Soc Cogn Affect Neurosci. PDF
Wang, J., Tao, A., Anderson, W., Madsen, J., & Kreiman, G. (2021). Mesoscopic physiological interactions in the human brain reveal small world properties. Cell Reports, 36(8). PDF | Resources
Li, C., & Dezza, A. (2021). What matters in branch specialization? Using a toy task to make predictions. Shared Visual Representations in Human and Machine Intelligence (SVRHM). Workshop at NeurIPS. PDF
Bricken, T., & Pehlevan, C. (2021). Attention approximates sparse distributed memory. 1172, 15301–15315. PDF
Bomatter, P., Zhang, M., Karev, D., Madan, S., Tseng, C., & Kreiman, G. (2021). When pigs fly: contextual reasoning in synthetic and natural scenes. International Conference on Computer Vision (ICCV). PDF | Resources
Gupta, S. K., Zhang, M., Wu, C.-C., Wolfe, J. M., & Kreiman, G. (2021). Visual search asymmetry: deep nets and humans share similar inherent biases. Advances in Neural Information Processing Systems, 34, 6946–6959. PDF | Resources
Casper, S., Boix, X., D’Amario, V., Guo, L., Schrimpf, M., Vinken, K., & Kreiman, G. (2021, May 31). Frivolous units: wider networks are not really that wide. PDF
2020
Zhang, M., Tseng, C., & Kreiman, G. (2020). Putting visual object recognition in context. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12985–12994. PDF | Resources
Yuan, L., Xiao, W., Kreiman, G., Tay, F., Feng, J., & Livingstone, M. (2020). Adversarial images for the primate brain. arXiv, 2011.05623. PDF
Xiao, W., & Kreiman, G. (2020). XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLoS Computational Biology, 16(6), e1007973. PDF | Resources
Vinken, K., Boix, X., & Kreiman, G. (2020). Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception. Science Advances, 6, eabd4205. PDF | Resources
Olson, J., & Kreiman, G. (2020). Simple learning rules generate complex cannonical circuits. arXiv, 2009.06118. PDF | Resources
Lotter, W., Kreiman, G., & Cox, D. (2020). A neural network trained for prediction mimics diverse features of biological neurons and perception. Nature Machine Learning, 2, 210–219. PDF
Jacquot, V., Ying, Z., & Kreiman, G. (2020). Can deep learning recognize subtle human activities? Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),14244–14253. PDF | Resources
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. PDF | Resources
Kreiman, G., & Serre, T. (2020). Beyond the feedforward sweep: feedback computations in the visual cortex. Annals of the New York Academy of Sciences, 1464(1), 222–241. PDF
O’Connell, T. P., Chun, M. M., & Kreiman, G. (2019). Zero-shot neural decoding of visual categories without prior exemplars. bioRxiv, 700344. PDF
Ben-Yosef, G., Kreiman, G., & Ullman, S. (n.d.). What can human minimal videos tells us about dynamic recognition models. Workshop at International Conference on Learning Representations (ICLR). Retrieved PDF
2019
Zhang, M., Tseng, C., Montejo, K., Kwon, J., & Kreiman, G. (2019). Lift-the-flap: what, where and when for context reasoning. arXiv:1902.00163 [Cs]. PDF | Resources
Xiao, W., Chen, H., Liao, Q., & Poggio, T. (2019). Biologically-plausible learning algorithms can scale to large datasets. International Conference on Learning Representations (ICLR). PDF
Ponce, C. R., Xiao, W., Schade, P. F., Hartmann, T. S., Kreiman, G., & Livingstone, M. (2019). Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences. Cell. PDF | Resources
Madhavan, R., Bansal, A. K., Madsen, J. R., Golby, A. J., Tierney, T. S., Eskandar, E. N., Anderson, W. S., & Kreiman, G. (2019). Neural interactions underlying visuomotor associations in the human brain. Cerebral Cortex, 29(11), 4551–4567. PDF | Resources
Kreiman, Gabriel. (2019). What do neurons really want? The role of semantics in cortical representations. In Psychology of Learning and Motivation (Kara D. Federmeier, Diane M. Beck, Vol. 70). Elsevier. PDF | Resources
Kreiman, G. (2019). It’s a small dimensional world after all: Comment on “The unreasonable effectiveness of small neural ensembles in high-dimensional brain” by Alexander N. Gorban et al. Physics of Life Reviews, 29, 96–97. PDF
2018
Zhang, M., Feng, J., Ma, K. T., Lim, J. H., Zhao, Q., & Kreiman, G. (2018). Finding any Waldo with zero-shot invariant and efficient visual search. Nature Communications, 9(1), 3730. PDF | Resources
Tang, H., Schrimpf, M., Lotter, W., Moerman, C., Paredes, A., Ortega Caro, J., Hardesty, W., Cox, D., & Kreiman, G. (2018). Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences of the United States of America, 115(35), 8835–8840. PDF | Resources
Palepu, A., Premanathan, S., Azhar, F., Vendrame, M., Loddenkemper, T., Reinsberger, C., Kreiman, G., Parkerson, K. A., Sarma, S., & Anderson, W. S. (2018). Automating interictal spike detection: revisiting a simple threshold rule. Conference Proceedings: … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2018, 299–302. PDF
Misra, P., Marconi, A., Peterson, M., & Kreiman, G. (2018). Minimal memory for details in real life events. Scientific Reports, 8(1), 16701. PDF | Resources
Isik, L., Singer, J., Madsen, J. R., Kanwisher, N., & Kreiman, G. (2018). What is changing when: Decoding visual information in movies from human intracranial recordings. NeuroImage, 180(Pt A), 147–159. PDF | Resources
Zhang, M., Feng, J., Lim, J. H., Zhao, Q., & Kreiman, G. (2018). What am I searching for? arXiv:1807.11926 [Cs]. PDF | Resources
Wu, E., Wu, K., & Kreiman, G. (n.d.). Learning scene gist with convolutional neural networks to improve object recognition. Information Sciences and Systems (CISS). Retrieved. PDF | Resources
2017
Tang, H., & Kreiman, G. (2017). Recognition of occluded objects. In Q. Zhao (Ed.), Computational and Cognitive Neuroscience of Vision. Springer-Verlag. PDF
Lotter, W., Kreiman, G., & Cox, D. (2017). Deep predictive coding networks for video prediction and unsupervised learning. International Conference on Learning Representations (ICLR), arXiv:1605.08104. PDF | Resources
Cheney, N., Schrimpf, M., & Kreiman, G. (2017). On the robustness of convolutional neural networks to internal architecture and weight perturbations. arXiv, 1703.08245. PDF
2016
Kreiman, G. (2017). A null model for cortical representations with grandmothers galore. Language, Cognition and Neuroscience, 32(3), 274–285. PDF
Tang, S., Hemberg, M., Cansizoglu, E., Belin, S., Kosik, K., Kreiman, G., Steen, H., & Steen, J. (2016). f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome. Nucleic Acids Res, 44(10), e97. PDF
Tang, H., Singer, J., Ison, M., Pivazyan, G., Romaine, M., Frias, R., Meller, E., Boulin, A., Carroll, J. D., Perron, V., Dowcett, S., Arlellano, M., & Kreiman, G. (2016). Predicting episodic memory formation for movie events. Scientific Reports, 6, 30175. PDF | Resources
Miconi, T., Groomes, L., & Kreiman, G. (2016). There’s Waldo! A normalization model of visual search predicts single-trial human fixations in an object search task. Cerebral Cortex, 26(7), 3064–3082. PDF | Resources
Lotter, W., Kreiman, G., & Cox, D. (2016). Unsupervised representation learning using predictive generative networks. International Conference on Learning Representations (ICLR). PDF
Gomez-Laberge, C., Smolyanskaya, A., Nassi, J. J., Kreiman, G., & Born, R. (2016). Bottom-up and top-down input augment the variability of cortical neurons. Neuron, 91(3), 540–547. PDF
Tang, H., Yu, H.-Y., Chou, C.-C., Crone, N. E., Madsen, J. R., Anderson, W. S., & Kreiman, G. (2016). Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLife, 5. PDF | Resources
2015
Madhavan, R., Millman, D., Tang, H., Crone, N., Lenz, F., Madsen, J., Anderson, W., & Kreiman, G. (2015). Decrease in gamma-band activity in the human parahippocampal gyrus during sequence learning. Frontiers in Systems Neuroscience, 8, 222. PDF
Singer, J. M., Madsen, J. R., Anderson, W. S., & Kreiman, G. (2015). Sensitivity to timing and order in human visual cortex. Journal of Neurophysiology, 113(5), 1656–1669. PDF
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Singer, J. M., Madsen, J. R., Anderson, W. S., & Kreiman, G. (2015). Sensitivity to timing and order in human visual cortex. Journal of Neurophysiology, 113(5), 1656–1669. https://drive.google.com/file/d/1whAf-vHiUigEG_uE9qdsw4GjvZ7J52L-/view?usp=sharing
Madhavan, R., Millman, D., Tang, H., Crone, N., Lenz, F., Madsen, J., Anderson, W., & Kreiman, G. (2015). Decrease in gamma-band activity in the human parahippocampal gyrus during sequence learning. Frontiers in Systems Neuroscience, 8, 222. https://drive.google.com/file/d/122YJxEg9K7tclaa5R7yRnPRaWKHMLl0D/view?usp=sharing
2014
Kim, T.-K., Hemberg, M., & Gray, J. M. (2015). Enhancer RNAs: a class of long noncoding RNAs synthesized at enhancers. Cold Spring Harbor Perspectives in Biology, 7(1), a018622. PDF
Rutishauser, U., Cerf, M., & Kreiman, G. (2014). Data analysis techniques for human microwire recordings: spike detection and sorting, decoding, relation between units and local field potentials. In I. Fried, U. Rutishauser, M. Cerf, & G. Kreiman (Eds.), Single neuron studies of the human brain. Probing cognition. MIT Press. PDF
Nassi, J. J., Gómez-Laberge, C., Kreiman, G., & Born, R. T. (2014). Corticocortical feedback increases the spatial extent of normalization. Frontiers in Systems Neuroscience, 8, 105. PDF
Mormann, F., Ison, M., Quiroga, R., Koch, C., Fried, I., & Kreiman, G. (2014). Visual cognitive adventures of single neurons in the human medial temporal lobe. In I. Fried, U. Rutishauser, M. Cerf, & G. Kreiman (Eds.), Single neuron studies of the human brain. Probing cognition. (pp. 121–151). MIT Press. PDF
Kreiman, G., Rutishauser, U., Cerf, M., & Fried, I. (2014). The next ten years and beyond. In I. Fried, U. Rutishauser, M. Cerf, & G. Kreiman (Eds.), Single neuron studies of the human brain. Probing cognition. MIT Press. PDF
Kreiman, G. (2014). Neural correlates of consciousness: perception and volition. In M. Gazzaniga (Ed.), Cognitive Neuroscience: V. MIT Press. PDF
Fried, I., Rutishauser, U., Cerf, M., & Kreiman, G. (Eds.). (2014). Single neuron studies of the human brain: probing cognition. The MIT Press. PDF
Bansal, A. (2014). Human single unit activity for reach and grasp motor prostheses. In I. Fried, M. Cerf, U. Rutishauser, & G. Kreiman (Eds.), Single neuron studies of the human brain. MIT Press. PDF
Prabakaran, S., Hemberg, M., Chauhan, R., Winter, D., Tweedie-Cullen, R. Y., Dittrich, C., Hong, E., Gunawardena, J., Steen, H., Kreiman, G., & Steen, J. A. (2014). Quantitative profiling of peptides from RNAs classified as noncoding. Nature Communications, 5, 5429. PDF
Malik, A. N., Vierbuchen, T., Hemberg, M., Rubin, A. A., Ling, E., Couch, C. H., Stroud, H., Spiegel, I., Farh, K. K.-H., Harmin, D. A., & Greenberg, M. E. (2014). Genome-wide identification and characterization of functional neuronal activity-dependent enhancers. Nature Neuroscience, 17(10), 1330–1339. PDF
Pinto, A. L. R., Fernández, I. S., Peters, J. M., Manganaro, S., Singer, J. M., Vendrame, M., Prabhu, S. P., Loddenkemper, T., & Kothare, S. V. (2014). Localization of sleep spindles, k-complexes, and vertex waves with subdural electrodes in children. Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society, 31(4), 367–374. PDF
Tang, H., Buia, C., Madhavan, R., Crone, N. E., Madsen, J. R., Anderson, W. S., & Kreiman, G. (2014). Spatiotemporal dynamics underlying object completion in human ventral visual cortex. Neuron, 83(3), 736–748. PDF | Resources
Singer, J. M., & Kreiman, G. (2014). Short temporal asynchrony disrupts visual object recognition. Journal of Vision, 14(5), 7. PDF
Frost, B., Hemberg, M., Lewis, J., & Feany, M. B. (2014). Tau promotes neurodegeneration through global chromatin relaxation. Nature Neuroscience, 17(3), 357–366. PDF
Bansal, A. K., Madhavan, R., Agam, Y., Golby, A., Madsen, J. R., & Kreiman, G. (2014). Neural dynamics underlying target detection in the human brain. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 34(8), 3042–3055. PDF | Resources
2013
Kreiman, Gabriel. (2013). Mind the quantum? Trends in Cognitive Science, 17(3), 109. PDF
Kreiman, G. (2013). Computational models of visual object recognition. In S. Panzeri & R. Quian Quiroga (Eds.), Principles of neural coding. CRC Press. PDF
2012
Murugan, R., & Kreiman, G. (2012). Theory on the coupled stochastic dynamics of transcription and splice-site recognition. PLoS Computational Biology, 8(11), e1002747. PDF
Hemberg, M., Gray, J. M., Cloonan, N., Kuersten, S., Grimmon, S., Greenberg, M. E., & Kreiman, G. (2012). Integrated genome analysis suggests that most conserved non-coding sequences are regulatory factor binding sites. Nucleic Acids Research, 40, 7858–7869. PDF | Resources
Burbank, K. S., & Kreiman, G. (2012). Depression-biased reverse plasticity rule is required for stable learning at top-down connections. PLoS Computational Biology, 8(3), e1002393. PDF
Bansal, A. K., Singer, J. M., Anderson, W. S., Golby, A., Madsen, J. R., & Kreiman, G. (2012). Temporal stability of visually selective responses in intracranial field potentials recorded from human occipital and temporal lobes. Journal of Neurophysiology, 108(11), 3073–3086. PDF
Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E., & Donoghue, J. P. (2012). Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials. Journal of Neurophysiology, 107(5), 1337–1355. PDF
Ross, S. E., McCord, A. E., Jung, C., Atan, D., Mok, S. I., Hemberg, M., Kim, T.-K., Salogiannis, J., Hu, L., Cohen, S., Lin, Y., Harrar, D., McInnes, R. R., & Greenberg, M. E. (2012). Bhlhb5 and Prdm8 form a repressor complex involved in neuronal circuit assembly. Neuron, 73(2), 292–303. PDF
2011
Singer, J., & Kreiman, G. (2011). Introduction to statistical learning and pattern classification. In N. Kriegeskorte & G. Kreiman (Eds.), Visual Population Codes. MIT Press. PDF
Meyers, E. M., & Kreiman, G. (2011). Tutorial on pattern classification in cell recordings. In N. Kriegeskorte & G. Kreiman (Eds.), Understanding visual population codes. MIT Press. PDF
Kriegeskorte, N., & Kreiman, G. (2011). Visual Population Codes. MIT Press. PDF | Resources
Kreiman, G., & Maunsell, J. (2011). Nine criteria for a measure of scientific output. Frontiers in Computational Neuroscience, 5, 48. PDF
Kreiman, Gabriel. (2011). Literary inspiration. Nature, 475, 453–454. PDF
Hemberg, M., & Kreiman, G. (2011). Conservation of transcription factor binding events predicts gene expression across species. Nucleic Acids Research, 39(16), 11. PDF
Chen, L. L., Madhavan, R., Rapoport, B. I., & Anderson, W. S. (2011). A method for real-time cortical oscillation detection and phase-locked stimulation. Conference Proceedings: … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011, 3087–3090. PDF
Burbank, K., & Kreiman, G. (2011). Introduction to the anatomy and function of visual cortex. In N. Kriegeskorte & G. Kreiman (Eds.), Understanding visual population codes. MIT Press. PDF
Anderson, G., WS, Kreiman. (2011). Neuroscience: What we cannot model, we do not understand. Current Biology, 21(3), R124. PDF
Tang, H., & Kreiman, G. (2011). Face recognition: vision and emotions beyond the bubble. Current Biology: CB, 21(21), R888-890. PDF
Cohen, S., Gabel, H. W., Hemberg, M., Hutchinson, A. N., Sadacca, L. A., Ebert, D. H., Harmin, D. A., Greenberg, R. S., Verdine, V. K., Zhou, Z., Wetsel, W. C., West, A. E., & Greenberg, M. E. (2011). Genome-wide activity-dependent MeCP2 phosphorylation regulates nervous system development and function. Neuron, 72(1), 72–85. PDF
Murugan, R., & Kreiman, G. (2011). On the minimization of fluctuations in the response times of autoregulatory gene networks. Biophysical Journal, 101(6), 1297–1306. PDF
Fried, I., Mukamel, R., & Kreiman, G. (2011). Internally generated preactivation of single neurons in human medial frontal cortex predicts volition. Neuron, 69(3), 548–562. PDF
2010
6543092
2010
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14
https://kreimanlab.com/wp-content/plugins/zotpress/
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2009
6543092
2009
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50
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https://kreimanlab.com/wp-content/plugins/zotpress/
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6543092
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Kreiman, G. (2007). Brain science: from the very large to the very small. Current Biology, 17(17), R768–R770. https://drive.google.com/file/d/1yxrywwRQXs3A0iWVaVMUXU53F-O3a2-V/view?usp=sharing
Serre, T., Kreiman, G., Kouh, M., Cadieu, C., Knoblich, U., & Poggio, T. (2007). A quantitative theory of immediate visual recognition. Progress in Brain Research, 165, 33–56. https://drive.google.com/file/d/1eKjTWY6axodqrOCcH0jBkXzUXlaeeDqX/view?usp=sharing
Kreiman, G. (2007). Single neuron approaches to human vision and memories. Current Opinion in Neurobiology, 17(4), 471–475. https://drive.google.com/file/d/1YWLNfpbBBW-n0FSorR9mMPpTubujgq6e/view?usp=sharing
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Kreiman, G., Hung, C., Quian Quiroga, R., Kraskov, A., Poggio, T., & DiCarlo, J. (2006). Object selectivity of local field potentials and spikes in the inferior temporal cortex of macaque monkeys. Neuron, 49, 433–445. https://drive.google.com/file/d/1SSwgiMj-O0LrvUIEsoKFFcFvQNaIDAyz/view?usp=sharing
Tropea, D., Kreiman, G., Lychman, A., Mukherjee, S., Yu, H., Horng, S., & Sur, M. (2006). Gene expression changes and molecular pathways mediating activity-dependent plasticity in visual cortex. Nature Neuroscience, 9(5), 660–668. https://drive.google.com/file/d/1ZL2AH-7eTpY3WdyyGPyD3DMIOrQBzzFk/view?usp=sharing
2005
6543092
2005
1
apa
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date
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https://kreimanlab.com/wp-content/plugins/zotpress/
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Kreiman, G., Fried, I., & Koch, C. (2005). Responses of single neurons in the human brain during flash suppression. In R. Blake & D. Alais (Eds.), Binocular Rivalry and Perceptual Ambiguity. MIT Press. https://drive.google.com/file/d/1J0Sck-KgSSWpcl6H8TcJNpWw6ukYXTAi/view?usp=sharing
Quian Quiroga, R., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005). Invariant visual representation by single neurons in the human brain. Nature, 435, 1102–1107. https://drive.google.com/file/d/1Q1kJ2mNvcNiJQnqqGajreqodScHcwqcn/view?usp=sharing
Hung, C. P., Kreiman, G., Poggio, T., & DiCarlo, J. J. (2005). Fast Read-out of Object Identity from Macaque Inferior Temporal Cortex. Science, 310, 863–866. https://drive.google.com/file/d/1ch3LJKYzuDNnnA_QeQANE-N4QTHBoRem/view?usp=sharing
2004
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2004
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apa
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date
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https://kreimanlab.com/wp-content/plugins/zotpress/
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Kreiman, G., Fried, I., & Koch, C. (2002). Single-neuron correlates of subjective vision in the human medial temporal lobe. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 8378–8383. https://drive.google.com/file/d/1iakO4lxWz9C6LqiOJtWo3RoPEUbIJF3u/view?usp=sharing
Krahe, R., Kreiman, G., Gabbiani, F., Koch, C., & Metzner, W. (2002). Stimulus encoding and feature extraction by multiple pyramidal cells in the hindbrain of weakly electric fish. Journal of Neuroscience, 22(6), 2374–2382. https://drive.google.com/file/d/1HmijUZp7r_dsSQ_chEFOxsIG7ym1iRJ8/view?usp=sharing
Rees, G., Kreiman, G., & Koch, C. (2002). Neural correlates of consciousness in humans. Nature Reviews Neuroscience, 3, 261–270. https://drive.google.com/file/d/1DrO4QqZrfP4_Y4-FRDKuWsSWyjHYstTn/view?usp=sharing
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Kreiman, Gabriel. (2001). Moveo ergo sum. BioEssays, 23, 662. https://drive.google.com/file/d/1eIZcgWwRxQSY4jSpwgMOzwOA2f8wpBMh/view?usp=sharing
Zirlinger, M., Kreiman, G., & Anderson, D. (2001). Amygdala-enriched genes identified by microarray technology are restricted to specific amygdaloid sub-nuclei. PNAS, 98(9), 5270–5275. https://drive.google.com/file/d/1XoMGp0F8R_CjJ-xiFmcyvJc8cbdusYDj/view?usp=sharing
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Kreiman, G., Krahe, R., Metzner, W., Koch, C., & Gabbiani, F. (2000). Robustness and variability of neuronal coding by amplitude sensitive Afferents in the weakly electric fish Eigenmannia. Journal of Neurophysiology, 84(1), 189–204. https://drive.google.com/file/d/1ZUY3SmoXg3jOR_vn4iiZ8flwXcn2oxUS/view?usp=sharing
Kreiman, G., Koch, C., & Fried, I. (2000). Category-specific visual responses of single neurons in the human medial temporal lobe. Nature Neuroscience, 3(9), 946–953. https://drive.google.com/file/d/1OrcqlyF1qw-8OYcMsWAbfgBXGj-teep7/view?usp=sharing
Kreiman, G., Koch, C., & Fried, I. (2000). Imagery neurons in the human brain. Nature, 408, 357–361. https://drive.google.com/file/d/1gze5zwvfefOBWUanS4HC8OjGnh4VMmxr/view?usp=sharing
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Ouyang, Y., Rosenstein, A. J., Kreiman, G., Kantor, D. B., Schuman, E. M., & Kennedy, M. B. (n.d.). Tetanic Stimulation increases both autophosphorylation and synthesis of CAMKII in area CA1 of hippocampal slices in 5 minutes. Annual Meeting of the Society for Neuroscience, 24, 1072. Retrieved https://drive.google.com/file/d/1dQoBvaf_W–xkCsDt4c7gbkQY69X1RgD/view?usp=sharing
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https://kreimanlab.com/wp-content/plugins/zotpress/
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Iñón de Iannino, N., Briones, G., Kreiman, G., & Ugalde, R. (1996). Characterization of the biosynthesis of beta(1-2) cyclic glucan in R. Fredii. Beta(1-2) glucan has no apparent role in nodule invasion of Mc Call and Peking soybean cultivars. Cellular and Molecular Biology (Noisy-Le-Grand, France), 42(5), 617–629. https://drive.google.com/file/d/1igwIl9Bd87L-J5D55AG8avILX_VqQ9S2/view?usp=sharing
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