James DiCarlo

Current Advisees

Guy Gaziv - Postdoc
Kamila Jozwik - Postdoc
Kohitij Kar - Research Scientist
N Apurva Ratan Murty - Postdoc
Alina Peter - Postdoc
Jon Prescott-Roy - Software Developer
Martin Schrimpf - Research Scientist

Past Advisees

Pouya Bashivan - Postdoc
Kohitij Kar - Research Scientist

CBMM Publications

M. J. Lee and DiCarlo, J. J., “An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models”, bioRxiv, 2023.
C. Guo, Lee, M. J., Leclerc, G., Dapello, J., Rao, Y., Madry, A., and DiCarlo, J. J., “Adversarially trained neural representations may already be as robust as corresponding biological neural representations”, arXiv, 2022.
J. Dapello, Kar, K., Schrimpf, M., Geary, R., Ferguson, M., Cox, D. D., and DiCarlo, J. J., “Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness”, bioRxiv, 2022.
A. Marliawaty Bagus, Marques, T., Sanghavi, S., DiCarlo, J. J., and Schrimpf, M., “Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units”, in NeurIPS, 2022.
K. Kar and DiCarlo, J. J., “Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition”, Neuron, vol. 109, no. 1, pp. 164 - 176.e5, 2021.
T. Marques, Schrimpf, M., and DiCarlo, J. J., “Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior”, bioRxiv, 2021.
K. Kar, Schrimpf, M., Schmidt, K., and DiCarlo, J. J., “ Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior”, Journal of Vision, vol. 21, 2021.
A. Baidya, Dapello, J., DiCarlo, J. J., and Marques, T., “Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs”, in NeurIPS 2021, 2021.
A. Ratan N. Murty, Bashivan, P., Abate, A., DiCarlo, J. J., and Kanwisher, N., “Computational models of category-selective brain regions enable high-throughput tests of selectivity”, Nature Communications, vol. 12, no. 1, 2021.
M. Schrimpf, Sato, F., Sanghavi, S., and DiCarlo, J. J., “Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates ”, in COSYNE, Denver, CO, USA, 2020.
T. Marques, Schrimpf, M., and DiCarlo, J. J., “Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses ”, in COSYNE, Denver, Colorado, USA, 2020.
K. Kar and DiCarlo, J. J., “Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition ”, in COSYNE, Denver, Colorado, USA, 2020.
J. Schwartz, Alter, S., DiCarlo, J. J., McDermott, J. H., Tenenbaum, J. B., Yamins, D. L. K., Gutfreund, D., Gan, C., Traer, J., Kubilius, J., Schrimpf, M., Bhandwaldar, A., De Freitas, J., Mrowca, D., Lingelbach, M., Sano, M., Bear, D., Kim, K., Haber, N., and Fan, C., “ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation”. 2020.
C. Gen, Schwartz, J., Alter, S., Schrimpf, M., Traer, J., De Freitas, J., Kubilius, J., Bhandwaldar, A., Haber, N., Sano, M., Kim, K., Wang, E., Mrowca, D., Lingelbach, M., Curtis, A., Feigleis, K., Bear, D., Gutfreund, D., Cox, D., DiCarlo, J. J., McDermott, J. H., Tenenbaum, J. B., and Yamins, D. L. K., “ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation”, arXiv, 2020.
R. Rajalingham, Kar, K., Sanghavi, S., Dehaene, S., and DiCarlo, J. J., “The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys”, Nature Communications, vol. 11, no. 1, 2020.
K. Kar and DiCarlo, J. J., “Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition”, Neuron, 2020.
M. Schrimpf, Kubilius, J., Lee, M. J., N. Murty, A. Ratan, Ajemian, R., and DiCarlo, J. J., “Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence”, Neuron, vol. 108, no. 3, pp. 413 - 423, 2020.
J. Dapello, Marques, T., Schrimpf, M., Geiger, F., Cox, D., and DiCarlo, J. J., “Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations”, Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020). 2020.
K. M. Jozwik, Lee, H., Kanwisher, N., and DiCarlo, J. J., “Are topographic deep convolutional neural networks better models of the ventral visual stream?”, in Conference on Cognitive Computational Neuroscience, 2019.
K. M. Jozwik, Schrimpf, M., Kanwisher, N., and DiCarlo, J. J., “To find better neural network models of human vision, find better neural network models of primate vision”, in BioRxiv, 2019.
K. Kar, Kubilius, J., Schmidt, K., Issa, E. B., and DiCarlo, J. J., “Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior”, Nature Neuroscience, 2019.
P. Bashivan, Kar, K., and DiCarlo, J. J., “Neural Population Control via Deep Image Synthesis”, Science, vol. 364, no. 6439, 2019.
T. Marques and DiCarlo, J. J., “A meta-analysis of ANNs as models of primate V1 ”, in Bernstein, Berlin, Germany, 2019.
J. Kubilius, Schrimpf, M., Kar, K., Rajalingham, R., Hong, H., Majaj, N. J., Issa, E. B., Bashivan, P., Prescott-Roy, J., Schmidt, K., Nayebi, A., Bear, D., Yamins, D. L. K., and DiCarlo, J. J., “Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs”, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.
K. Kar and DiCarlo, J. J., “Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition ”, in Society for Neuroscience, Chicago, IL, USA, 2019.
M. Schrimpf and Kubilius, J., “Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?”, bioRxiv preprint, 2018.
L. Arend, Han, Y., Schrimpf, M., Bashivan, P., Kar, K., Poggio, T., DiCarlo, J. J., and Boix, X., “Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results”. 2018.
C. F. Cadieu, Hong, H., Yamins, D. L. K., Pinto, N., Majaj, N. J., and DiCarlo, J. J., “Neural Representation Benchmark [code]”. 2013.