Pouya Bashivan
Pouya Bashivan
Assistant Professor, McGill university
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Zitiert von
Zitiert von
Learning representations from EEG with deep recurrent-convolutional neural networks
P Bashivan, I Rish, M Yeasin, N Codella
arXiv preprint arXiv:1511.06448, 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
Neural population control via deep image synthesis
P Bashivan, K Kar, JJ DiCarlo
Science 364 (6439), eaav9436, 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
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
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
Mental State Recognition via Wearable EEG
P Bashivan, I Rish, S Heisig
Proceedings of 5th NIPS workshop on Machine Learning and Interpretation in …, 2015
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
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
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
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
Brain-score: Which artificial neural network for object recognition is most brain-like? bioRxiv, 407007
M Schrimpf, J Kubilius, H Hong, NJ Majaj, R Rajalingham, EB Issa, K Kar, ...
Teacher guided architecture search
P Bashivan, M Tensen, JJ DiCarlo
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 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
Improved switching for multiple model adaptive controller in noisy environment
P Bashivan, A Fatehi
Journal of Process Control 22 (2), 390-396, 2012
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
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
Auditory cortex supports verbal working memory capacity
GM Bidelman, JA Brown, P Bashivan
Neuroreport 32 (2), 163-168, 2021
Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results
L Arend, Y Han, M Schrimpf, P Bashivan, K Kar, T Poggio, JJ DiCarlo, ...
Center for Brains, Minds and Machines (CBMM), 2018
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
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