Publication

Found 51 results
Author Title Type [ Year(Desc)]
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2013
Meyers, E. The Neural Decoding Toolbox. (2013). at <http://www.readout.info/>
Cadieu, C. F. et al. Neural Representation Benchmark [code]. (2013).
Lattman, E., Poggio, T. & Westervelt, R. NSF Science and Technology Centers – The Class of 2013. (2013).PDF icon NSFGender2013_poster.pdf (2.77 MB)
2014
Kreiman, G. Cognitive Neuroscience V, (2014).
Bansal, A. et al. Neural Dynamics Underlying Target Detection in the Human Brain. Journal of Neuroscience 34, (2014).
Baldauf, D. & Desimone, R. Neural Mechanisms of Object-Based Attention. Science 344, 424 - 427 (2014).
Tan, C. & Poggio, T. Neural tuning size is a key factor underlying holistic face processing. (2014).PDF icon CBMM-Memo-021-1406.3793.pdf (387.79 KB)
Kreiman, G., Rutishauser, U., Cerf, M. & Fried, I. Single neuron studies of the human brain. Probing cognition (2014).
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).PDF icon CBMM-Memo-027.pdf (9.44 MB)
Miconi, T., Groomes, L. & Kreiman, G. A normalization model of visual search predicts single trial human fixations in an object search task. (2014).PDF icon CBMM-Memo-008.pdf (854.51 KB)
2015
Siegle, J. H., Hale, G. J., Newman, J. P. & Voigts, J. Neural ensemble communities: open-source approaches to hardware for large-scale electrophysiology. Current Opinion in Neurobiology 32, 53 - 59 (2015).
Jara-Ettinger, J. Not So Innocent: Toddlers’ Inferences About Costs and Culpability. Psychological Science 26, 633-40 (2015).PDF icon NotSoInnocent_InPress.pdf (238.53 KB)
Poggio, T., Rosasco, L., Shashua, A., Cohen, N. & Anselmi, F. Notes on Hierarchical Splines, DCLNs and i-theory. (2015).PDF icon CBMM Memo 037 (1.83 MB)
2016
The naive utility calculus: Computational principles underlying commonsense psychology. (2016).
Jara-Ettinger, J., Gweon, H., Schulz, L. & Tenenbaum, J. B. The naive utility calculus: computational principles underlying social cognition. Trends Cogn Sci. (2016). doi:10.1016/j.tics.2016.05.011
Bramley, N., Gerstenberg, T. & Tenenbaum, J. B. Natural science: Active learning in dynamic physical microworlds. 38th Annual Meeting of the Cognitive Science Society (2016).PDF icon Natural Science (Bramley, Gerstenberg, Tenenbaum, 2016).pdf (5.39 MB)
Morère, O., Veillard, A., Chandrasekhar, V. & Poggio, T. Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. arXiv.org (2016). at <https://arxiv.org/abs/1603.04595>PDF icon 1603.04595.pdf (2.9 MB)
Kreiman, G. Neural Information Processing Systems (NIPS) 2015 Review. (2016).PDF icon Read the Views & Review article by Gabriel Kreiman (443.87 KB)
Robertson, C. E., Hermann, K., Mynick, A., Kravitz, D. J. & Kanwisher, N. Neural Representations Integrate the Current Field of View with the Remembered 360° Panorama. Current Biology (2016). doi:10.1016/j.cub.2016.07.002
Tan, C. & Poggio, T. Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing. Public Library of Science | PLoS ONE 1(3): e0150980, (2016).PDF icon journal.pone_.0150980.PDF (384.15 KB)
Meyers, E., Dean, M. & Hale, G. J. New Data Science tools for analyzing neural data and computational models. Society for Neuroscience (2016).
2017
Sliwa, J. & Freiwald, W. A. A Network for Social interaction understanding in the primate brain. Organization for Human Brain Mapping - OHBM 2017 (2017).
Mendoza-Halliday, D. & Martinez-Trujillo, J. Neuronal population coding of perceived and memorized visual features in the lateral prefrontal cortex. Nature Communications 8, (2017).
Grossman, N. et al. Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields. Cell 169, 1029 - 1041.e16 (2017).
Kreiman, G. A null model for cortical representations with grandmothers galore. Language, Cognition and Neuroscience 274 - 285 (2017). doi:10.1080/23273798.2016.1218033

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