Publication

Found 171 results
Author Title Type [ Year(Desc)]
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2012
Dehghani, N. et al. Avalanche analysis from multielectrode ensemble recordings in cat, monkey, and human cerebral cortex during wakefulness and sleep. Frontiers in Physiology (2012). doi:10.3389/fphys.2012.00302PDF icon AvalancheDynamics.pdf (2.45 MB)
Canas, G. D., Poggio, T. & Rosasco, L. Learning manifolds with k-means and k-flats. Advances in Neural Information Processing Systems 25 (NIPS 2012) (2012). at <https://papers.nips.cc/paper/2012/hash/b20bb95ab626d93fd976af958fbc61ba-Abstract.html>
Peyrache, A. et al. Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep. Proceedings of the National Academy of Sciences (2012). doi:10.1073/pnas.1109895109PDF icon SpatiotemporalDynamic.pdf (2.56 MB)
2013
Cadieu, C. F. et al. Neural Representation Benchmark [code]. (2013).
2014
Amir, N. et al. Abstracts of the 2014 Brains, Minds, and Machines Summer Course. (2014).PDF icon CBMM-Memo-024.pdf (2.86 MB)
Barbu, A. et al. The Compositional Nature of Event Representations in the Human Brain. (2014).PDF icon CBMM Memo 011.pdf (3.95 MB)
Barbu, A. et al. The Compositional Nature of Event Representations in the Human Brain. (2014).PDF icon CBMM Memo 011.pdf (3.95 MB)
Rutishauser, U., Cerf, M. & Kreiman, G. Single Neuron Studies of the Brain: Probing Cognition (2014).
Chen, X. et al. Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts. (2014).PDF icon CBMM-Memo-015.pdf (974.07 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)
Barbu, A. et al. Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions. (2014).PDF icon CBMM Memo 012.pdf (678.95 KB)
Barbu, A. et al. Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions. (2014).PDF icon CBMM Memo 012.pdf (678.95 KB)
Barbu, A. et al. Computer Vision – ECCV 2014, Lecture Notes in Computer Science 8693, 612–627 (Springer International Publishing, 2014).
Barbu, A. et al. Computer Vision – ECCV 2014, Lecture Notes in Computer Science 8693, 612–627 (Springer International Publishing, 2014).
Fried, I., Rutishauser, U., Cerf, M. & Kreiman, G. Single Neuron Studies of the Human Brain. Probing Cognition. Probing cognition (MIT Press, 2014).
Tang, H. et al. Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex. Neuron 83, 736 - 748 (2014).
2015
Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nature Neuroscience 18, 1844 (2015).PDF icon Preprint (40.28 MB)
Madhavan, R. et al. Decrease in gamma-band activity tracks sequence learning. Frontiers in Systems Neuroscience 8, (2015).PDF icon fnsys-08-00222.pdf (5.62 MB)
Chen, F., Tillberg, P. W. & Boyden, E. S. Expansion microscopy. Science 347 , 543-548 (2015).
Meyers, E. How PFC and LIP process single and multiple-object ‘pop-out’ displays. Society for Neuroscience (2015). at <https://www.sfn.org/~/media/SfN/Documents/Annual%20Meeting/FinalProgram/NS2015/Full%20Abstract%20PDFs%202015/SfN15_Abstracts_PDF_Nanos.ashx>
Rudi, A., Camoriano, R. & Rosasco, L. Less is More: Nyström Computational Regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5936-less-is-more-nystrom-computational-regularization>PDF icon Less is More- Nystr ̈om Computational Regularization_1507.04717v4.pdf (287.14 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)
Chen, X. & Yuille, A. Parsing Occluded People by Flexible Compositions. Computer Vision and Pattern Recognition (CVPR) (2015).PDF icon CBMM Memo 034.pdf (5.54 MB)
Buice, M. & de Vries, S. Population Coding, Correlations, and Functional Connectivity in the mouse visual system with the Cortical Activity Map (CAM). Society for Neuroscience 2015 (2015).PDF icon 2015 SFN Population_Coding.pdf (2.94 MB)

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