Robbe L.T. Goris
Affiliations: | University of Texas at Austin, Austin, Texas, U.S.A. |
Area:
visual system, computation & theoryGoogle:
"Robbe Goris"Mean distance: 13.44 (cluster 29) | S | N | B | C | P |
Parents
Sign in to add mentor| Felix A. Wichmann | grad student | Bernstein Center for Computational Neuroscience and TU Berlin | |
| Johan Wagemans | grad student | 2009 | KU Leuven |
| J Anthony Movshon | post-doc | 2010-2016 | NYU |
| Eero P. Simoncelli | post-doc | 2010-2016 | NYU |
Children
Sign in to add trainee| Yoon Bai | grad student | UT Austin | |
| Zoe M Boundy-Singer | grad student | UT Austin | |
| Julie A Charlton | grad student | UT Austin | |
| Corey M Ziemba | post-doc | 2018- | UT Austin |
BETA: Related publications
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Publications
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| Henaff O, Goris R, Simoncelli E. (2017) Perceptual straightening of natural video trajectories Journal of Vision. 17: 402 |
| Ziemba C, Goris R, Simoncelli E, et al. (2017) Uncoupling choice formation and choice-correlated activity in early visual cortex Journal of Vision. 17: 1271 |
| Goris RL, Simoncelli EP, Movshon JA. (2015) Origin and Function of Tuning Diversity in Macaque Visual Cortex. Neuron |
| Rabinowitz NC, Goris RL, Cohen M, et al. (2015) Attention stabilizes the shared gain of V4 populations. Elife. 4 |
| Zaharia A, Goris R, Movshon J, et al. (2015) Compound stimuli reveal velocity separability of spatiotemporal receptive fields in macaque area MT. Journal of Vision. 15: 485 |
| Goris RL, Movshon JA, Simoncelli EP. (2014) Partitioning neuronal variability. Nature Neuroscience. 17: 858-65 |
| Goris RL, Putzeys T, Wagemans J, et al. (2013) A neural population model for visual pattern detection. Psychological Review. 120: 472-96 |
| Putzeys T, Bethge M, Wichmann F, et al. (2012) A new perceptual bias reveals suboptimal population decoding of sensory responses. Plos Computational Biology. 8: e1002453 |
| Goris R, Putzeys T, Wagemans J, et al. (2011) A neural population model for pattern detection Journal of Vision. 11: 1165-1165 |
| Goris RL, de Beeck HP. (2010) Invariance in visual object recognition requires training: a computational argument. Frontiers in Neuroscience. 4: 71 |