Publications

Bono, S., Madan, S., Grover, I., Yasueda, M., Breazeal, C., Pfister, H., & Kreiman, G. (2024). Look around! Unexpecetd gains from training on environments in the vicinity of the target. ArXiv, 2401.15856. https://drive.google.com/file/d/1WSpHklye2Edkm0c_SBPltVCoM081Pj7l/view?usp=sharing

Madan, S., Li, Y., Zhang, M., Pfister, H., & Kreiman, G. (2024). Improving generalization by mimicking the human visual diet. BioRxiv, 2206.07802. https://drive.google.com/file/d/17NhH3cw-cYB9pM2bOdnrkSUNlKR_KbWt/view?usp=sharing

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. https://drive.google.com/file/d/1JlzUPI_5BQ1OmPj2e67ZXdv-YJorBWHZ/view?usp=sharing

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, In Press. https://drive.google.com/file/d/1KnwCnhJWfI7CI3p9qp4jNEYom9KYCFnf/view?usp=sharing

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. https://drive.google.com/file/d/1EeH0El2umcbRAYnKymI-X34KjssfJiGR/view?usp=sharing

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. https://www.biorxiv.org/content/biorxiv/early/2024/05/22/2024.05.22.595306.full.pdf

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. https://drive.google.com/file/d/1Vzdm4Uknpn4Ry77A0JecDZEyQE8rqOHF/view?usp=sharing

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). https://drive.google.com/file/d/1w5o5-OptxZ7IGmsrjgw2ZAiqa4TEP8G3/view?usp=sharing

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). https://drive.google.com/file/d/1wLp4rfVNwEEfHoZNo4j43fphQYzOklPi/view?usp=sharing

Jain, V., Alves Feitosa, F., & Kreiman, G. (2024). Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor. ArXiv, 2406.13564. https://drive.google.com/file/d/1jhPcgQz4fZ3n8Dr9zjwVUZv4fEtcfP-3/view?usp=sharing

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. https://drive.google.com/file/d/1fupRPZWoNRKTCpaxDhZgGXZPXu2xBejT/view?usp=sharing

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. https://drive.google.com/file/d/1vjYtz9-5oBMIFco0MnA6Cg1K5nLjM84P/view?usp=sharing

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. https://drive.google.com/file/d/1VtRSxIwrgjZ1dqDOYMCobuZS2N946qc5/view?usp=sharing

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. https://drive.google.com/file/d/1WN6RMwjhIinpMoz7Brg-mjwhrXZCkTcH/view?usp=sharing

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. https://drive.google.com/file/d/12LdYWEuyd0PlTzqKfabInpr5iNbj_ItO/view?usp=sharing

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. https://drive.google.com/file/d/1v6qH8A2sKQmuLsqEN1JFGms3Bl2UHmQm/view?usp=sharing

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. https://drive.google.com/file/d/1HfyMaLGNHirBf5J9tZwI7VuanJzzIzpS/view?usp=sharing

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. https://drive.google.com/file/d/1Y3nNMX-fOPonO8_pdBIgSWtxeuhH9NHI/view?usp=sharing

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. https://drive.google.com/file/d/1yzqKckUcBZzjwZK0Aq1pbLR_4-fsBaMQ/view?usp=sharing

Bricken, T., Schaeffer, R., Olshausen, B. A., & Kreiman, G. (2023). Emergence of sparse representations from noise. International Conferenece on Machine Learning (ICML). https://drive.google.com/file/d/1-fawIn1gDV0wOzXJp-ONQAOwetLpr3Tr/view?usp=sharing

Bricken, T., Davies, A., Singh, D., Krotov, D., & Kreiman, G. (2023). Sparse distributed memory is a continual learner. International Conference on Learning Representations (ICLR). https://drive.google.com/file/d/1Pv0SE1kmPrp5-gfFpp-IEnIEj4livKQJ/view?usp=sharing

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). https://drive.google.com/file/d/1A4S1fgEPfSGHjCU7j4xuraV8gpQRGRL8/view?usp=sharing

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. https://drive.google.com/file/d/16DHYZiusdyryL2N9-Q91t0s4zcSkZ_Xd/view?usp=sharing

Kreiman, G. (2023). Neural coding: Stimulating cortex to alter visual perception. Current Biology, 33, R117–R118. https://drive.google.com/file/d/12LdYWEuyd0PlTzqKfabInpr5iNbj_ItO/view?usp=sharing

Casper, S., Killian, T., Kreiman, G., & Hadfield-Mennell, D. (2023). White-box adversarial policies against RL agents. ArXiv, 2209.02167. https://drive.google.com/file/d/1iQNPD0VderZP1RHihinCPyPQtOEgan_2/view?usp=sharing

Aghajan, Z., Kreiman, G., & Fried, I. (2023). Minute-scale periodicity of neuronal firing in the human entorhinal cortex. Cell Reports, 42, 113271. https://drive.google.com/file/d/17Ql4jFLe0efaBesKWNho8Sfg3c6oFinI/view?usp=sharing

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). https://drive.google.com/file/d/1iaiPhS-IrJMFXzygwnXUa_urok-0bN6Z/view?usp=sharing

Zhang, M., 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. (2022). Human or Machine? Turing Tests for Vision and Language. ArXiv, 2211.13087. https://drive.google.com/file/d/1WhrlNJP9kxS3YeCJYEjbvuz6nRd0lNmG/view?usp=sharing

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. https://drive.google.com/file/d/1nPiLepEojmi0sfJ2Pmf6rd0Zami8Qmln/view?usp=sharing

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. https://drive.google.com/file/d/1JI2VRqVQKvRUOVpZnmXf5-_Q-qOr3xMa/view?usp=sharing

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. https://drive.google.com/file/d/1xXD8YPg45fXJuXwasonKJXMGKpPBKnk2/view?usp=sharing

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. https://drive.google.com/file/d/1wa6yHmT1hWXQWulicpYwpHHE9I4r3gDZ/view?usp=sharing

Dellaferrera, G., & Kreiman, G. (2022). Error-driven input modulation: solving the credit assignment problem without a backward pass. Proceeedings of Machine Learning Research (International Conference on Machine Learning (ICML)), 162, 4937–4955. https://drive.google.com/file/d/1i_4BJ2mZQw9kJVRL2gQnjVIIL4DoRLtZ/view?usp=sharing

Shaham, N., Chandra, J., Kreiman, G., & Sompolinsky, H. (2022). Stochastic consolidation of lifelong memory. Scientific Reports, 12, 13107. https://drive.google.com/file/d/1dyCT17Tsitqw5jzFoqEIY_vXTBIf2g1e/view?usp=sharing

Sikarwar, A., & Kreiman, G. (2022). On the efficacy of co-attention transformer layers in visual question answering. ArXiv, 2201.03965. https://drive.google.com/file/d/1AbjhZIKPMAEoAwWnE48jIkpFS_JlnH9b/view?usp=sharing

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. https://drive.google.com/file/d/1GR4l1smEOmpzB-F1OvI4lFD4KJTCWfZb/view?usp=sharing

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. https://drive.google.com/file/d/18rl1acDXQFG9exuSye9xx2_DVlPX_oMZ/view?usp=sharing

Casper, S., Nadeau, M., & Kreiman, G. (2022). Robust feature-level adversaries are interpretability tools. Neural Information Processing Systems (NeurIPS), 36. https://drive.google.com/file/d/1wa6yHmT1hWXQWulicpYwpHHE9I4r3gDZ/view?usp=sharing

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. https://drive.google.com/file/d/1r_acxvANg9e4H_loPf3Y19-USWaL_8Lx/view?usp=sharing

Liu, X., Sikarwar, A., Lim, J., Kreiman, G., Shi, Z., & Zhang, M. (2022). Reason from context with self-supervised learning. ArXiv, 2211.12817. https://drive.google.com/file/d/1L3fHn7tttVaPLPQppe9drcx74L3oPxeZ/view?usp=sharing

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. https://drive.google.com/file/d/1Mya9VBNj__NaRipMzS63_D88Gko_gZ9k/view?usp=sharing

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. https://drive.google.com/file/d/1qxroNzo8RWu2pr62P1UFlRHhGO6qOG5w/view?usp=sharing

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). https://drive.google.com/file/d/11nUVQHh4vucGpmokq-RD0Fl9fJOAYFWu/view?usp=sharing

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. https://drive.google.com/file/d/1BneHkJcVTX40ji0I6vUNKcer8UmSkQlv/view?usp=sharing

Zhang, M., & Kreiman, G. (2021). Beauty is in the eye of the machine. Nature Human Behaviour, 5. https://drive.google.com/file/d/1rYQe0N7KhpeK5zBNjdhZXtbB4meH4oKl/view?usp=sharing

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. https://drive.google.com/file/d/1X5R_1iic19J0csWHsy6UtEIy3M5MzVGw/view?usp=sharing

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). https://drive.google.com/file/d/1G-Bs2uu4SlyQYmJfjxtVj2gFG8DG1l7J/view?usp=sharing

Bricken, T., & Pehlevan, C. (2021). Attention approximates sparse distributed memory. 1172, 15301–15315. https://drive.google.com/file/d/1Xgv3z9T1dDDCGHwKWSbOh1UGzOP4zK3R/view?usp=sharing

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. https://drive.google.com/file/d/1gfeBMnjb2LzNpy8JgKsOWKxhkATaCtyO/view?usp=sharing

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. https://drive.google.com/file/d/1nIgHtPva-0-G3hMZ-8DjqHXQezkFYlno/view?usp=sharing

Olson, J., & Kreiman, G. (2020). Simple learning rules generate complex cannonical circuits. ArXiv, 2009.06118. https://drive.google.com/file/d/1f-FIfDo1s2wZliv8S5REmRyjyjHaLONd/view?usp=sharing

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. https://drive.google.com/file/d/1v-FHz3ywI5zKEBhG6uuOBri2v8_NXaec/view?usp=sharing

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. https://drive.google.com/file/d/1gIwKz14reyTs_KUXCNLapTr3jn5k1hkK/view?usp=sharing

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. https://drive.google.com/file/d/1rZbCB5BigMch0tDq4ZSrhJIQAVEUoFL0/view?usp=sharing

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. https://drive.google.com/file/d/1oVtV74EXEUBkD_gzFz8rmfnJlQ_sias_/view?usp=sharing

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. https://drive.google.com/file/d/1QTfDn3nbFr0TFimm_wC3g5f7-qI6VvJB/view?usp=sharing

Yuan, L., Xiao, W., Kreiman, G., Tay, F., Feng, J., & Livingstone, M. (2020). Adversarial images for the primate brain. ArXiv, 2011.05623. https://drive.google.com/file/d/1PtPaXlPn3R2tCt4cLeR_3fwDa2tAWdJu/view?usp=sharing

O’Connell, T. P., Chun, M. M., & Kreiman, G. (2019). Zero-shot neural decoding of visual categories without prior exemplars. BioRxiv, 700344. https://drive.google.com/file/d/1OfRgR9-ldp9N7UaR3-sh7Ath_X48cmz4/view?usp=sharing

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). https://drive.google.com/file/d/1ghrMTjLRPfQ4u01OanPXE2qtOHKizYbU/view?usp=sharing

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. https://drive.google.com/file/d/1pbnU190s0Z5m-sxg-2yC01TXQ4JNIVDD/view?usp=sharing

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. https://drive.google.com/file/d/1Yex3VGmNP7A7N1O4BWQbeI16I27zc5ZA/view?usp=sharing

Xiao, W., Chen, H., Liao, Q., & Poggio, T. (2019). Biologically-plausible learning algorithms can scale to large datasets. International Conference on Learning Representations (ICLR). https://drive.google.com/file/d/1W0vPDjFkwEMBXC58n5GYA-34R_jii8u7/view?usp=sharing

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. https://drive.google.com/file/d/1GbiSjI7JcbRGI_ALSOCtn35jPlaUSH8M/view?usp=sharing

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]. https://drive.google.com/file/d/10eGX_Ms3W5yvt3xlWmmwPaf_7ejo3sbo/view?usp=sharing

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. https://drive.google.com/file/d/1qIgJEEl1faR40XNJwzjrEiuoiqnZq7_P/view?usp=sharing

Zhang, M., Feng, J., Lim, J. H., Zhao, Q., & Kreiman, G. (2018). What am I searching for? ArXiv:1807.11926 [Cs]. https://drive.google.com/file/d/1kiDngbF1VJcQZ09jDEdnEoEp52wraBCF/view?usp=sharing

Misra, P., Marconi, A., Peterson, M., & Kreiman, G. (2018). Minimal memory for details in real life events. Scientific Reports, 8(1), 16701. https://drive.google.com/file/d/1BR-KWWt2D33bcMf3zWeujWPyHYYdnrix/view?usp=sharing

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. https://drive.google.com/file/d/1wKvAO8A4APgdqut-uTTGiGoL1YeJO7rT/view?usp=sharing

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. https://drive.google.com/file/d/1IXgM2OAnLNIRR75hJTcpNg0rNny7gG-h/view?usp=sharing

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. https://drive.google.com/file/d/1iP_4T-Q04x_3TZ9jxiH6SWVuYoyG1aY3/view?usp=sharing

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. https://drive.google.com/file/d/1M9sTcFimpIqcIEmGw9d6B0dCIkjT9zV5/view?usp=sharing

Wu, E., Wu, K., & Kreiman, G. (n.d.). Learning scene gist with convolutional neural networks to improve object recognition. Information Sciences and Systems (CISS). https://drive.google.com/file/d/1OcQKDEQYMhh2RhIv37w1mKXt1rk4Habz/view?usp=sharing

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. https://drive.google.com/file/d/1XjMhd70hMCduoMMW0V0wNw6LaDLCmikG/view?usp=sharing

Tang, H., & Kreiman, G. (2017). Recognition of occluded objects. In Q. Zhao (Ed.), Computational and Cognitive Neuroscience of Vision. Springer-Verlag. https://drive.google.com/file/d/1ulsENJnTlQdcoiq2RmfGr6IFrIFlJT3d/view?usp=sharing

Cheney, N., Schrimpf, M., & Kreiman, G. (2017). On the robustness of convolutional neural networks to internal architecture and weight perturbations. ArXiv, 1703.08245. https://drive.google.com/file/d/15WcwKBsS7dkg0jOFdBhNGR-tHqsdP9oP/view?usp=sharing

Kreiman, G. (2017). A null model for cortical representations with grandmothers galore. Language, Cognition and Neuroscience, 32(3), 274–285. https://drive.google.com/file/d/1D4JsAoXYZA8lMOgUqwnPeyQBgPMVvn_i/view?usp=sharing

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. https://drive.google.com/file/d/1WwhlRmjakVwHayaRYZXur2o25ywbaHKw/view?usp=sharing

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. https://drive.google.com/file/d/1hZdCTjS11FJmkuibFkqnXY-0gx9i3PiW/view?usp=sharing

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. https://drive.google.com/file/d/1D-KMyDTAuZlcz-Fnw_v-miO_DXpoopMl/view?usp=sharing

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. https://drive.google.com/file/d/1U_EdEJ4n88cJ-OUwDa5-zkKMcX8bUPp3/view?usp=sharing

Lotter, W., Kreiman, G., & Cox, D. (2016). Unsupervised representation learning using predictive generative networks. International Conference on Learning Representations (ICLR). https://drive.google.com/file/d/1WS1HKyMHLECGPLHKQ9MDVyB8a5Bjp7Nm/view?usp=sharing

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. https://drive.google.com/file/d/1dFHJTXbjSnwZeYipvL639qObNwahIT8U/view?usp=sharing

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

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. https://drive.google.com/file/d/1jrZoKoui9jjlUOlZySsdog_K4ZcuuqAl/view?usp=sharing

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. https://drive.google.com/file/d/1pOsiWNBiVoXBseNzC2TYoyYTccwWrzV8/view?usp=sharing

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. https://drive.google.com/file/d/1jYDWzE_ALtq7Ckz2bYbO11Z5kaWtL6Nx/view?usp=sharing

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. https://drive.google.com/file/d/1M7S0aPXpn6eVpIcpXGp4LLHOc4P9aw-p/view?usp=sharing

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. https://drive.google.com/file/d/1dUKV7iER4HQNly6DWw46rvKtmJ_82O6B/view?usp=sharing

Singer, J. M., & Kreiman, G. (2014). Short temporal asynchrony disrupts visual object recognition. Journal of Vision, 14(5), 7. https://drive.google.com/file/d/1-lnhI2APb210E5voFg_Rkh5sXGNaltX2/view?usp=sharing

Frost, B., Hemberg, M., Lewis, J., & Feany, M. B. (2014). Tau promotes neurodegeneration through global chromatin relaxation. Nature Neuroscience, 17(3), 357–366. https://drive.google.com/file/d/14Qiq4JBQY1N7i5E808Aqzh40DEOA44ll/view?usp=sharing

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. https://drive.google.com/file/d/1oq85yIaWNWgAJrVan5HKJyyeHGtIIefg/view?usp=sharing

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. https://drive.google.com/file/d/1uKUX7-C5NKZKTFXy8irB-RlLstquCcnV/view?usp=sharing

Kreiman, G. (2014). Neural correlates of consciousness: perception and volition. In M. Gazzaniga (Ed.), Cognitive Neuroscience: Vol. V. MIT Press. https://drive.google.com/file/d/1BpVk3nqnY_M4lsV8eMFjwE5nJYIRCzO0/view?usp=sharing

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. https://drive.google.com/file/d/1weFPGw1Wrd_NAtaRQRGO8mwI7UB84UNk/view?usp=sharing

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. https://drive.google.com/file/d/1gHJRrjUfv2R9fRJB2hdgJJLXIp8BiCLV/view?usp=sharing

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. https://drive.google.com/file/d/1xEBRGwp7PJCBAbufG89i6zVqLp2r-Nii/view?usp=sharing

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. https://drive.google.com/file/d/1IecHoty80HH6jVpIUcVysBWqxA7f64rG/view?usp=sharing

Fried, I., Rutishauser, U., Cerf, M., & Kreiman, G. (Eds.). (2014). Single neuron studies of the human brain: probing cognition. The MIT Press. https://klab.tch.harvard.edu/publications/Publications_Books.html

Kreiman, Gabriel. (2013). Mind the quantum? Trends in Cognitive Science, 17(3), 109. https://drive.google.com/file/d/1xtKxEXk5w0UKH5Lkf7e5rU9njUa4M6QW/view?usp=sharing

Kreiman, G. (2013). Computational models of visual object recognition. In S. Panzeri & R. Quian Quiroga (Eds.), Principles of neural coding. CRC Press. https://drive.google.com/file/d/1rV7Ff0Q6piAUf9JBjAt72kvk1Tr4gA_s/view?usp=sharing

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. https://drive.google.com/file/d/18Q69QcUTWYRsGWxbGPjvJC-NJCcb__PD/view?usp=sharing

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. https://drive.google.com/file/d/1EftO04tVg3i_A3sd5i0pGs_PHb-YMR7x/view?usp=sharing

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. https://drive.google.com/file/d/1bEgM4WaCyqZd5PVU9-BKf3LtLbfIcC6R/view?usp=sharing

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. https://drive.google.com/file/d/1HkcACvvq78RFXOzBBa3J96svxWFDd_b_/view?usp=sharing

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. https://drive.google.com/file/d/1ndFlFnoNm6NWJqVM82p7F71Tmqhte02q/view?usp=sharing

Murugan, R., & Kreiman, G. (2012). Theory on the coupled stochastic dynamics of transcription and splice-site recognition. PLoS Computational Biology, 8(11), e1002747. https://drive.google.com/file/d/1lDHgV3BfQQpx0KeOAZ4HaZj7LfM0sJTL/view?usp=sharing

Tang, H., & Kreiman, G. (2011). Face recognition: vision and emotions beyond the bubble. Current Biology: CB, 21(21), R888-890. https://drive.google.com/file/d/1DgBM0YDB4CsbMBfUXm12JG7jy6O0RJUm/view?usp=sharing

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. https://drive.google.com/file/d/1tJsJHp_8ttqkht3Z_Dr4H63lvJzTZK4s/view?usp=sharing

Murugan, R., & Kreiman, G. (2011). On the minimization of fluctuations in the response times of autoregulatory gene networks. Biophysical Journal, 101(6), 1297–1306. https://drive.google.com/file/d/1092cg5fEGY2NZKcuZCqvcEMVxIrI9uVU/view?usp=sharing

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. https://drive.google.com/file/d/1tsfukdWlN4ar0qlCRWOIw2VsuVWsWX_b/view?usp=sharing

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. https://drive.google.com/file/d/1o55hr04F1wEbT75P9TOkHHiGfCoU8-xH/view?usp=sharing

Anderson, G., WS, Kreiman. (2011). Neuroscience: What we cannot model, we do not understand. Current Biology, 21(3), R124. https://drive.google.com/file/d/1bg6hYxE2-eFjUDNuAX7al2te7Zpd6cFQ/view?usp=sharing

Hemberg, M., & Kreiman, G. (2011). Conservation of transcription factor binding events predicts gene expression across species. Nucleic Acids Research, 39(16), 11. https://drive.google.com/file/d/1hqBF8ION7EIjOlk6he0kPRDST5P2VwCS/view?usp=sharing

Kreiman, Gabriel. (2011). Literary inspiration. Nature, 475, 453–454. https://drive.google.com/file/d/1V5hA0S6khvyi_hTpr5WwgXI587MI8NZl/view?usp=sharing

Kreiman, G., & Maunsell, J. (2011). Nine criteria for a measure of scientific output. Frontiers in Computational Neuroscience, 5, 48. https://drive.google.com/file/d/1_XzzjqcibvkJyCjODIFfOe4aCfoKCZEy/view?usp=sharing

Singer, J., & Kreiman, G. (2011). Introduction to statistical learning and pattern classification. In N. Kriegeskorte & G. Kreiman (Eds.), Visual Population Codes. MIT Press. https://drive.google.com/file/d/1CM6ti1EXjMsqKvB_0b8a_jdMRDBTcrdN/view?usp=sharing

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. https://drive.google.com/file/d/1dXGwmMaLqhB6gcK78ArZ97LwQ7C1rEcD/view?usp=sharing

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. https://drive.google.com/file/d/1eTCRt53AjXj8fXetDI3vg3nyGaat9nxI/view?usp=sharing

Kriegeskorte, N., & Kreiman, G. (2011). Visual Population Codes. MIT Press. https://klab.tch.harvard.edu/publications/Publications_Books.html

Blumberg, J., & Kreiman, G. (2010). How cortical neurons help us see: visual recognition in the human brain. The Journal of Clinical Investigation, 120(9), 3054–3063. https://drive.google.com/file/d/1sCpP3eZ6wIcOK1DZqWL0q1QC9cBaKeDj/view?usp=sharing

Pfenning, A. R., Kim, T.-K., Spotts, J. M., Hemberg, M., Su, D., & West, A. E. (2010). Genome-wide identification of calcium-response factor (CaRF) binding sites predicts a role in regulation of neuronal signaling pathways. PloS One, 5(5), e10870. https://drive.google.com/file/d/1j8C-Kc4JNNB5nbpUyj8mCYkhYFi67PRe/view?usp=sharing

Kim, T.-K., Hemberg, M., Gray, J. M., Costa, A. M., Bear, D. M., Wu, J., Harmin, D. A., Laptewicz, M., Barbara-Haley, K., Kuersten, S., Markenscoff-Papadimitriou, E., Kuhl, D., Bito, H., Worley, P. F., Kreiman, G., & Greenberg, M. E. (2010). Widespread transcription at neuronal activity-regulated enhancers. Nature, 465(7295), 182–187. https://drive.google.com/file/d/10FHtat4ALZbxgqyX2jJ8dXvw0WCaGUAY/view?usp=sharing

Agam, Y., Liu, H., Papanastassiou, A., Buia, C., Golby, A. J., Madsen, J. R., & Kreiman, G. (2010). Robust selectivity to two-object images in human visual cortex. Current Biology: CB, 20(9), 872–879. https://drive.google.com/file/d/1CqrR4x-99X8UQUujnzpHFq7OaLMyB4aX/view?usp=sharing

Singer, J. M., & Sheinberg, D. L. (2010). Temporal cortex neurons encode articulated actions as slow sequences of integrated poses. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 30(8), 3133–3145. https://drive.google.com/file/d/1mFIl5NtSuW3-upZ_E7q7pGAot2ea1MC0/view?usp=sharing

Quian Quiroga, R., & Kreiman, G. (2010). Measuring sparseness in the brain. Psychological Review, 117, 291–297. https://drive.google.com/file/d/1NlzGiHTnI1PHma43XcP7bdLV6muUuK3f/view?usp=sharing

Ståhlberg, A., Bengtsson, M., Hemberg, M., & Semb, H. (2009). Quantitative transcription factor analysis of undifferentiated single human embryonic stem cells. Clinical Chemistry, 55(12), 2162–2170. https://drive.google.com/file/d/1N4q8Jdms7SVNXiPsTDIlHqblcr5Q2MWV/view?usp=sharing

Singer, J., & Kreiman, G. (2009). Toward unmasking the dynamics of visual perception. Neuron, 64(4), 446–447. https://drive.google.com/file/d/1PDJp3yp0ymEWAxlZSFhFFtBvwJtkGnN2/view?usp=sharing

Rasch, M., Logothetis, N. K., & Kreiman, G. (2009). From neurons to circuits: linear estimation of local field potentials. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 29(44), 13785–13796. https://drive.google.com/file/d/1OOVtJPHTuw9XGj6nbqs7E8rMDnHJOXWK/view?usp=sharing

Horng, S., Kreiman, G., Ellsworth, C., Page, D., Blank, M., Millen, K., & Sur, M. (2009). Differential gene expression in the developing lateral geniculate nucleus and medial geniculate nucleus reveals novel roles for Zic4 and Foxp2 in visual and auditory pathway development. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 29(43), 13672–13683. https://drive.google.com/file/d/1ecTrCI7k9yXCKtEv4qTLmoJyoT6POFJn/view?usp=sharing

Liu, H., Agam, Y., Madsen, J. R., & Kreiman, G. (2009). Timing, timing, timing: fast decoding of object information from intracranial field potentials in human visual cortex. Neuron, 62(2), 281–290. https://drive.google.com/file/d/1WXsjrDrGU-rtw9nVbO5SY_wFEAdQf0mi/view?usp=sharing

Flavell, S. W., Kim, T.-K., Gray, J. M., Harmin, D. A., Hemberg, M., Hong, E. J., Markenscoff-Papadimitriou, E., Bear, D. M., & Greenberg, M. E. (2008). Genome-wide analysis of MEF2 transcriptional program reveals synaptic target genes and neuronal activity-dependent polyadenylation site selection. Neuron, 60(6), 1022–1038. https://drive.google.com/file/d/1v7cqo9BlHZRQnHn1IhSn4YWFl8w3OD1F/view?usp=sharing

Meyers, E. M., Freedman, D. J., Kreiman, G., Miller, E. K., & Poggio, T. (2008). Dynamic population coding of category information in inferior temporal and prefrontal cortex. Journal of Neurophysiology, 100(3), 1407–1419. https://drive.google.com/file/d/1jpUEbJbdCOreaUFL5BxfLq11eoHG8wMa/view?usp=sharing

Quiroga, R. Q., Kreiman, G., Koch, C., & Fried, I. (2008). Sparse but not “grandmother-cell” coding in the medial temporal lobe. Trends in Cognitive Sciences, 12(3), 87–91. https://drive.google.com/file/d/1ieNqRqBbxG39ndPMGIvIs76Ed-WMLzeU/view?usp=sharing

Leamey, C. A., Glendining, K. A., Kreiman, G., Kang, N.-D., Wang, K. H., Fassler, R., Sawatari, A., Tonegawa, S., & Sur, M. (2008). Differential gene expression between sensory neocortical areas: potential roles for Ten_m3 and Bcl6 in patterning visual and somatosensory pathways. Cerebral Cortex (New York, N.Y.: 1991), 18(1), 53–66. https://drive.google.com/file/d/1X8Z4mFI3hYIuWJBIKg6HS0ONDs3m44Lk/view?usp=sharing

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

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

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

Crick, F., Koch, C., Kreiman, G., & Fried, I. (2004). Consciousness and neurosurgery. Neurosurgery, 55(2), 273–281; discussion 281-282. https://drive.google.com/file/d/1WXHLq7vkWVAjiep4a975_m0uwJ1CTnP5/view?usp=sharing

Su, A. I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K. A., Block, D., Zhang, J., Soden, R., Hayakawa, M., Kreiman, G., Cooke, M. P., Walker, J. R., & Hogenesch, J. B. (2004). A gene atlas of the mouse and human protein-encoding transcriptomes. Proceedings of the National Academy of Sciences of the United States of America, 101(16), 6062–6067. https://drive.google.com/file/d/1h2jnRXXZ1N9PjRxjn3ccw7DIq5kiWzjk/view?usp=sharing

Kreiman, G. (2004). Identification of sparsely distributed clusters of cis-regulatory elements in sets of co-expressed genes. Nucleic Acids Research, 32(9), 2889–2900. https://drive.google.com/file/d/19ZgYqjD24ayz65dwxrSyVK7vETvxPj_m/view?usp=sharing

Kreiman, G. (2004). Neural coding: computational and biophisical perspectives. Physics of Life Reviews, 2, 71–102. https://drive.google.com/file/d/1VLUDAmcAFBHsaoYFCfaZ16CqIDOjvwSM/view?usp=sharing

Yeo, G., Holste, D., Kreiman, G., & Burge, C. B. (2004). Variation in alternative splicing across human tissues. Genome Biology, 5(10), R74. https://drive.google.com/file/d/1DEAcBlU0DCEF6cARx8TrVZWlYLIrqX4z/view?usp=sharing

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

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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