| Learning representations from EEG with deep recurrent-convolutional neural networks P Bashivan, I Rish, M Yeasin, N Codella arXiv preprint arXiv:1511.06448, 2015 | 938 | 2015 |
| 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 | 630 | 2018 |
| Neural population control via deep image synthesis P Bashivan, K Kar, JJ DiCarlo Science 364 (6439), eaav9436, 2019 | 478 | 2019 |
| 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 | 434 | 2018 |
| Brain-like object recognition with high-performing shallow recurrent ANNs J Kubilius, M Schrimpf, K Kar, R Rajalingham, H Hong, N Majaj, E Issa, ... Advances in neural information processing systems 32, 2019 | 320 | 2019 |
| Spectrotemporal dynamics of the EEG during working memory encoding and maintenance predicts individual behavioral capacity P Bashivan, GM Bidelman, M Yeasin European Journal of Neuroscience 40 (12), 3774-3784, 2014 | 115 | 2014 |
| Computational models of category-selective brain regions enable high-throughput tests of selectivity NA Ratan Murty, P Bashivan, A Abate, JJ DiCarlo, N Kanwisher Nature communications 12 (1), 5540, 2021 | 107 | 2021 |
| Mental State Recognition via Wearable EEG P Bashivan, I Rish, S Heisig Proceedings of 5th NIPS workshop on Machine Learning and Interpretation in …, 2015 | 99 | 2015 |
| Learning neural markers of schizophrenia disorder using recurrent neural networks J Dakka, P Bashivan, M Gheiratmand, I Rish, S Jha, R Greiner arXiv preprint arXiv:1712.00512, 2017 | 58 | 2017 |
| Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms M Gheiratmand, I Rish, GA Cecchi, MRG Brown, R Greiner, PI Polosecki, ... NPJ schizophrenia 3 (1), 22, 2017 | 54 | 2017 |
| Single trial prediction of normal and excessive cognitive load through EEG feature fusion P Bashivan, M Yeasin, GM Bidelman 2015 IEEE signal processing in medicine and biology symposium (SPMB), 1-5, 2015 | 49 | 2015 |
| Teacher guided architecture search P Bashivan, M Tensen, JJ DiCarlo Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 33 | 2019 |
| Adversarial feature desensitization P Bashivan, R Bayat, A Ibrahim, K Ahuja, M Faramarzi, T Laleh, ... Advances in Neural Information Processing Systems 34, 10665-10677, 2021 | 25 | 2021 |
| Improved switching for multiple model adaptive controller in noisy environment P Bashivan, A Fatehi Journal of Process Control 22 (2), 390-396, 2012 | 24 | 2012 |
| Auditory cortex supports verbal working memory capacity GM Bidelman, JA Brown, P Bashivan Neuroreport 32 (2), 163-168, 2021 | 18 | 2021 |
| A neurobiological evaluation metric for neural network model search N Blanchard, J Kinnison, B RichardWebster, P Bashivan, WJ Scheirer Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 16* | 2019 |
| Neural correlates of visual working memory load through unsupervised spatial filtering of EEG P Bashivan, GM Bidelman, M Yeasin Proceedings of 3rd workshop on Machine Learning and Interpretation in …, 2013 | 14 | 2013 |
| Continual learning with self-organizing maps P Bashivan, M Schrimpf, R Ajemian, I Rish, M Riemer, Y Tu arXiv preprint arXiv:1904.09330, 2019 | 13 | 2019 |
| Evaluating effects of methylphenidate on brain activity in cocaine addiction: a machine-learning approach I Rish, P Bashivan, GA Cecchi, RZ Goldstein Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and …, 2016 | 13 | 2016 |
| The feature landscape of visual cortex R Tong, R da Silva, D Lin, A Ghosh, J Wilsenach, E Cianfarano, ... bioRxiv, 2023.11. 03.565500, 2023 | 12 | 2023 |
James J DiCarloMassachusetts Institute of TechnologyVerified email at mit.edu
