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Meenakshi Khosla
Meenakshi Khosla
Massachusetts Institute of Technology
Verified email at cornell.edu - Homepage
Title
Cited by
Cited by
Year
Machine learning in resting-state fMRI analysis
M Khosla, K Jamison, GH Ngo, A Kuceyeski, MR Sabuncu
Magnetic resonance imaging 64, 101-121, 2019
2132019
Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction
M Khosla, K Jamison, A Kuceyeski, MR Sabuncu
NeuroImage 199, 651-662, 2019
1042019
3D convolutional neural networks for classification of functional connectomes
M Khosla, K Jamison, A Kuceyeski, MR Sabuncu
International Workshop on Deep Learning in Medical Image Analysis, 137-145, 2018
1012018
Using artificial neural networks to ask ‘why’questions of minds and brains
N Kanwisher, M Khosla, K Dobs
Trends in Neurosciences 46 (3), 240-254, 2023
792023
A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition
M Khosla, NAR Murty, N Kanwisher
Current Biology 32 (19), 4159-4171. e9, 2022
452022
Cortical response to naturalistic stimuli is largely predictable with deep neural networks
M Khosla, GH Ngo, K Jamison, A Kuceyeski, MR Sabuncu
Science Advances 7 (22), eabe7547, 2021
442021
A switch and wave of neuronal activity in the cerebral cortex during the first second of conscious perception
WX Herman, RE Smith, SI Kronemer, RE Watsky, WC Chen, LM Gober, ...
Cerebral Cortex 29 (2), 461-474, 2019
302019
Neurogen: activation optimized image synthesis for discovery neuroscience
Z Gu, KW Jamison, M Khosla, EJ Allen, Y Wu, G St-Yves, T Naselaris, ...
NeuroImage 247, 118812, 2022
272022
Revalidation of the Sat-Chit-Ananda Scale
K Singh, P Khanna, M Khosla, M Rapelly, A Soni
Journal of religion and health 57, 1392-1401, 2018
222018
Predicting individual task contrasts from resting‐state functional connectivity using a surface‐based convolutional network
GH Ngo, M Khosla, K Jamison, A Kuceyeski, MR Sabuncu
NeuroImage 248, 118849, 2022
212022
High-level visual areas act like domain-general filters with strong selectivity and functional specialization
M Khosla, L Wehbe
bioRxiv, 2022.03. 16.484578, 2022
102022
Polarons explain luminescence behavior of colloidal quantum dots at low temperature
M Khosla, S Rao, S Gupta
Scientific Reports 8 (1), 8385, 2018
102018
Detecting abnormalities in resting-state dynamics: an unsupervised learning approach
M Khosla, K Jamison, A Kuceyeski, MR Sabuncu
Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019 …, 2019
92019
From connectomic to task-evoked fingerprints: Individualized prediction of task contrasts from resting-state functional connectivity
GH Ngo, M Khosla, K Jamison, A Kuceyeski, MR Sabuncu
Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020
82020
High-level visual areas act like domain-general filters with strong selectivity and functional specialization. bioRxiv
M Khosla, L Wehbe
52022
Soft Matching Distance: A metric on neural representations that captures single-neuron tuning
M Khosla, AH Williams
Proceedings of UniReps: the First Workshop on Unifying Representations in …, 2024
42024
Characterizing the ventral visual stream with response-optimized neural encoding models
M Khosla, K Jamison, A Kuceyeski, M Sabuncu
Advances in Neural Information Processing Systems 35, 9389-9402, 2022
42022
Data-driven component modeling reveals the functional organization of high-level visual cortex
M Khosla, NAR Murty, N Kanwisher
Journal of Vision 22 (14), 4184-4184, 2022
32022
Neural encoding with visual attention
M Khosla, G Ngo, K Jamison, A Kuceyeski, M Sabuncu
Advances in Neural Information Processing Systems 33, 15942-15953, 2020
32020
A shared neural encoding model for the prediction of subject-specific fMRI response
M Khosla, GH Ngo, K Jamison, A Kuceyeski, MR Sabuncu
Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020
32020
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