| Machine learning in resting-state fMRI analysis M Khosla, K Jamison, GH Ngo, A Kuceyeski, MR Sabuncu Magnetic resonance imaging 64, 101-121, 2019 | 240 | 2019 |
| 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 | 126 | 2023 |
| 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 | 119 | 2019 |
| 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 | 108 | 2018 |
| 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 | 67 | 2022 |
| 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 | 58 | 2021 |
| 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 | 36 | 2022 |
| 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 | 35 | 2019 |
| 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 | 32 | 2022 |
| 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 | 23 | 2018 |
| 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 | 17 | 2024 |
| 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 | 15 | 2022 |
| Polarons explain luminescence behavior of colloidal quantum dots at low temperature M Khosla, S Rao, S Gupta Scientific Reports 8 (1), 8385, 2018 | 11 | 2018 |
| 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 | 10 | 2019 |
| 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 | 9 | 2020 |
| Privileged representational axes in biological and artificial neural networks M Khosla, AH Williams, J McDermott, N Kanwisher bioRxiv, 2024.06. 20.599957, 2024 | 8 | 2024 |
| 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 | 8 | 2022 |
| 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 | 6 | 2020 |
| High-level visual areas act like domain-general filters with strong selectivity and functional specialization. bioRxiv M Khosla, L Wehbe | 5 | 2022 |
| Conclusions about neural network to brain alignment are profoundly impacted by the similarity measure A Soni, S Srivastava, K Kording, M Khosla bioRxiv, 2024.08. 07.607035, 2024 | 4 | 2024 |
Mert R. SabuncuCornell University, Cornell Tech, Weill Cornell MedicineПотвърден имейл адрес: cornell.edu