Kohitij Kar

Kohitij
Kar
Research Scientist
Massachusetts Institute of Technology (MIT)
Department:  McGovern Institute for Brain Research
Lab Affiliation(s):  DiCarlo Lab

Associated Research Module: 

Kohitij Kar is currently a Postdoctoral Associate in the DiCarlo Lab at the McGovern Institute for Brain Research at MIT. He completed his Ph.D. in the Department of Behavioral and Neural Sciences at Rutgers University in New Jersey (PhD advisor: Bart Krekelberg) in 2015. His current research work is primarily focussed towards using a variety of experimental and computational techniques to build better models of primate visual cognition.

Email:  kohitij@mit.edu
Room:  46-6157
Website: 
Lab Website:  DiCarlo Lab

CBMM Publications

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.
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.
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.
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.
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.
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.
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.
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.