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Rishi Rajalingham
Rishi Rajalingham
mit.edu의 이메일 확인됨 - 홈페이지
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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
4662018
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
3732018
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
2552019
Comparison of object recognition behavior in human and monkey
R Rajalingham, K Schmidt, JJ DiCarlo
Journal of Neuroscience 35 (35), 12127-12136, 2015
1112015
An open resource for non-human primate optogenetics
S Tremblay, L Acker, A Afraz, DL Albaugh, H Amita, AR Andrei, ...
Neuron 108 (6), 1075-1090. e6, 2020
972020
Towards the quantitative evaluation of visual attention models
Z Bylinskii, EM DeGennaro, R Rajalingham, H Ruda, J Zhang, JK Tsotsos
Vision research 116, 258-268, 2015
722015
Chronically implantable LED arrays for behavioral optogenetics in primates
R Rajalingham, M Sorenson, R Azadi, S Bohn, JJ DiCarlo, A Afraz
Nature Methods 18 (9), 1112-1116, 2021
562021
Reversible inactivation of different millimeter-scale regions of primate IT results in different patterns of core object recognition deficits
R Rajalingham, JJ DiCarlo
Neuron 102 (2), 493-505. e5, 2019
462019
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, ...
432018
The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys
R Rajalingham, K Kar, S Sanghavi, S Dehaene, JJ DiCarlo
Nature communications 11 (1), 3886, 2020
402020
A network perspective on sensorimotor learning
H Sohn, N Meirhaeghe, R Rajalingham, M Jazayeri
Trends in Neurosciences 44 (3), 170-181, 2021
342021
Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task
R Rajalingham, A Piccato, M Jazayeri
Nature communications 13 (1), 1-15, 2022
29*2022
Contact sensing and interaction techniques for a distributed, multimodal floor display
Y Visell, S Smith, A Law, R Rajalingham, JR Cooperstock
2010 IEEE Symposium on 3D User Interfaces (3DUI), 75-78, 2010
292010
Brain-score: Which artificial neural network for object recognition is most brain-like? bioRxiv [Preprint](2018)
M Schrimpf, J Kubilius, H Hong, NJ Majaj, R Rajalingham, EB Issa, K Kar, ...
URL https://www. biorxiv. org/content/10.1101/407007v2, 2018
202018
Probabilistic tracking of pedestrian movements via in-floor force sensing
R Rajalingham, Y Visell, JR Cooperstock
2010 Canadian Conference on Computer and Robot Vision, 143-150, 2010
142010
Neural foundations of mental simulation: Future prediction of latent representations on dynamic scenes
A Nayebi, R Rajalingham, M Jazayeri, GR Yang
Advances in Neural Information Processing Systems 36, 2024
62024
Modulation of neural activity by reward in medial intraparietal cortex is sensitive to temporal sequence of reward
R Rajalingham, RG Stacey, G Tsoulfas, S Musallam
Journal of neurophysiology 112 (7), 1775-1789, 2014
62014
Characterization of neurons in the primate medial intraparietal area reveals a joint representation of intended reach direction and amplitude
R Rajalingham, S Musallam
Plos one 12 (8), e0182519, 2017
42017
Dynamic tracking of objects in the macaque dorsomedial frontal cortex
R Rajalingham, H Sohn, M Jazayeri
bioRxiv, 2022.06. 24.497529, 2022
32022
Auditory and haptic augmentation of floor surfaces
A De Sena, C Drioli, F Fontana, S Papetti, R Nordahl, S Serafin, L Turchet, ...
Deliverable, 2009
22009
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학술자료 1–20