| Performance-optimized hierarchical models predict neural responses in higher visual cortex DLK Yamins, H Hong, CF Cadieu, EA Solomon, D Seibert, JJ DiCarlo Proceedings of the national academy of sciences 111 (23), 8619-8624, 2014 | 2027 | 2014 |
| How does the brain solve visual object recognition? JJ DiCarlo, D Zoccolan, NC Rust Neuron 73 (3), 415-434, 2012 | 1897 | 2012 |
| Using goal-driven deep learning models to understand sensory cortex DLK Yamins, JJ DiCarlo Nature neuroscience 19 (3), 356-365, 2016 | 1586 | 2016 |
| Untangling invariant object recognition JJ DiCarlo, DD Cox Trends in cognitive sciences 11 (8), 333-341, 2007 | 1060 | 2007 |
| Fast readout of object identity from macaque inferior temporal cortex CP Hung, G Kreiman, T Poggio, JJ DiCarlo Science 310 (5749), 863-866, 2005 | 994 | 2005 |
| Deep neural networks rival the representation of primate IT cortex for core visual object recognition CF Cadieu, H Hong, DLK Yamins, N Pinto, D Ardila, EA Solomon, ... PLoS computational biology 10 (12), e1003963, 2014 | 799 | 2014 |
| Why is real-world visual object recognition hard? N Pinto, DD Cox, JJ DiCarlo PLoS computational biology 4 (1), e27, 2008 | 752 | 2008 |
| Why is real-world visual object recognition hard? N Pinto, DD Cox, JJ DiCarlo PLoS computational biology 4 (1), e27, 2008 | 748 | 2008 |
| Brain-score: Which artificial neural network for object recognition is most brain-like? M Schrimpf, J Kubilius, H Hong, NJ Majaj, R Rajalingham, EB Issa, K Kar, ... BioRxiv, 407007, 2018 | 463 | 2018 |
| Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior K Kar, J Kubilius, K Schmidt, EB Issa, JJ DiCarlo Nature neuroscience 22 (6), 974-983, 2019 | 424 | 2019 |
| Neural population control via deep image synthesis P Bashivan, K Kar, JJ DiCarlo Science 364 (6439), eaav9436, 2019 | 389 | 2019 |
| Selectivity and tolerance (“invariance”) both increase as visual information propagates from cortical area V4 to IT NC Rust, JJ DiCarlo Journal of Neuroscience 30 (39), 12978-12995, 2010 | 388 | 2010 |
| A high-throughput screening approach to discovering good forms of biologically inspired visual representation N Pinto, D Doukhan, JJ DiCarlo, DD Cox PLoS computational biology 5 (11), e1000579, 2009 | 379 | 2009 |
| A high-throughput screening approach to discovering good forms of biologically inspired visual representation N Pinto, D Doukhan, JJ DiCarlo, DD Cox PLoS computational biology 5 (11), e1000579, 2009 | 379 | 2009 |
| Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks R Rajalingham, EB Issa, P Bashivan, K Kar, K Schmidt, JJ DiCarlo Journal of Neuroscience 38 (33), 7255-7269, 2018 | 373 | 2018 |
| Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex G Kreiman, CP Hung, A Kraskov, RQ Quiroga, T Poggio, JJ DiCarlo Neuron 49 (3), 433-445, 2006 | 363 | 2006 |
| Explicit information for category-orthogonal object properties increases along the ventral stream H Hong, DLK Yamins, NJ Majaj, JJ DiCarlo Nature neuroscience 19 (4), 613-622, 2016 | 348 | 2016 |
| Stimulus configuration, classical conditioning, and hippocampal function. NA Schmajuk, JJ DiCarlo Psychological review 99 (2), 268, 1992 | 328 | 1992 |
| Unsupervised neural network models of the ventral visual stream C Zhuang, S Yan, A Nayebi, M Schrimpf, MC Frank, JJ DiCarlo, ... Proceedings of the National Academy of Sciences 118 (3), e2014196118, 2021 | 315 | 2021 |
| Unsupervised natural experience rapidly alters invariant object representation in visual cortex N Li, JJ DiCarlo science 321 (5895), 1502-1507, 2008 | 308 | 2008 |
