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CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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Lotter, W., Kreiman, G. & Cox, D. UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS. (2015).

CBMM Memo 040_rev1.pdf (1.92 MB)

Lotter, W., Kreiman, G. & Cox, D. A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception. ( arXiv | Cornell University, 2018). at <https://arxiv.org/pdf/1805.10734.pdf>

1805.10734.pdf (9.59 MB)

Lotter, W., Kreiman, G. & Cox, D. A neural network trained for prediction mimics diverse features of biological neurons and perception. Nature Machine Intelligence 2, 210 - 219 (2020).

Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (2017).

1605.08104.pdf (2.9 MB)

Livingstone, M. S., Arcaro, M. J. & Schade, P. F. Cortex Is Cortex: Ubiquitous Principles Drive Face-Domain Development. Trends in Cognitive Sciences (2018). doi:10.1016/j.tics.2018.10.009

1-s2.0-S1364661318302572-main.pdf (260.4 KB)

Liu, S. Nature and origins of intuitive psychology in human infants. (2020).

Liu, S., Brooks, N. B. & Spelke, E. S. Origins of the concepts cause, cost, and goal in prereaching infants. PNAS (2019). doi:https://doi.org/10.1073/pnas.1904410116

Author's last draft (2.58 MB)

Liu, H., Agam, Y., Madsen, J. & Kreiman, G. Timing, timing, timing: Fast decoding of object inforrmation from intracranial field potentials in human visual cortex. (2009). at <http://klab.tch.harvard.edu/resources/liuetal_timing3.html>

Liu, S. & Spelke, E. S. Six-month-old infants represent action efficiency on a continuous scale. 9th Biennial Meeting of the Cognitive Development Society (CDS) (2015).

Liu, S., McCoy, J. P. & Ullman, T. D. People's perceptions of others’ risk preferences. Cognitive Science Society (2019).

risk_cogsci_2019_final.pdf (899.8 KB)

Liu, C., Mao, J., Sha, F. & Yuille, A. Attention Correctness in Neural Image Captioning. AAAI 2017 (2017).

1605.09553.pdf (2.22 MB)

Liu, S., Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer value from effort. SRCD (2017).

Liu, S. & Spelke, E. S. Continuous representations of action efficiency in infancy. CEU Conference on Cognitive Development (BCCCD16) (2016).

Liu, S., Ullman, T. D., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer the value of goals from the costs of actions. Science 358, 1038-1041 (2017).

ivc_full_preprint_withsm.pdf (1.6 MB)

Liu, C. et al. Recurrent Multimodal Interaction for Referring Image Segmentation. (2018).

CBMM-Memo-079.pdf (10.16 MB)

Liu, S., Cushman, F. A., Gershman, S. J., Kool, W. & Spelke, E. S. Hard choices: Children’s understanding of the cost of action selection. . Cognitive Science Society (2019).

phk_cogsci_2019_final.pdf (276.14 KB)

Liu, Y. et al. Temporally delayed linear modelling (TDLM) measures replay in both animals and humans. eLife 10, (2021).

Liu, S. et al. Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents’ Action PlansAbstract. Open Mind 6, 211 - 231 (2022).

Liu, S. & Spelke, E. S. Six-month-old infants expect agents to minimize the cost of their actions. Cognition 160, 35-42 (2017).

Liu, S., Brooks, N. B. & Spelke, E. S. Origins of the concepts cause, cost, and goal in prereaching infants. Cognitive Development Society (2019).

liu_etal_lumi_cds2019_final.pdf (22.95 MB)

Liu, S., Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Ten-month-old infants infer value from effort. Society for Research in Child Development (2017).

Liu, S., Brooks, N. B. & Spelke, E. S. Pre-reaching infants expect causal agents to act efficiently without motor training. 20th Biennial International Conference on Infant Studies (ICIS) (2016).

Linderman, S. W., Johnson, M. J., Wilson, M. A. & Chen, Z. A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation. Journal of Neuroscience Methods 263, (2016).

Journal of Neuroscience Methods (2.27 MB)

Linderman, S. W., Stock, C. & Adams, R. A framework for studying synaptic plasticity with neural spike train data. Neural Information Processing Systems (2014).

5274-a-framework-for-studying-synaptic-plasticity-with-neural-spike-train-data.pdf (4.6 MB)

Linderman, S. W., Johnson, M. J., Wilson, M. A. & Chen, Z. A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation. (2014).

CBMM-Memo-027.pdf (9.44 MB)

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