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Dean Pospisil
Dean Pospisil
在 princeton.edu 的电子邮件经过验证
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引用次数
引用次数
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'Artiphysiology'reveals V4-like shape tuning in a deep network trained for image classification
DA Pospisil, A Pasupathy, W Bair
Elife 7, e38242, 2018
782018
Directing eye gaze enhances auditory spatial cue discrimination
RK Maddox, DA Pospisil, GC Stecker, AKC Lee
Current Biology 24 (7), 748-752, 2014
522014
The unbiased estimation of the fraction of variance explained by a model
DA Pospisil, W Bair
PLoS computational biology 17 (8), e1009212, 2021
192021
Comparing the brainʼs representation of shape to that of a deep convolutional neural network
D Pospisil, A Pasupathy, W Bair
Proceedings of the 9th EAI International Conference on Bio-inspired …, 2016
122016
Accounting for biases in the estimation of neuronal signal correlation
DA Pospisil, W Bair
Journal of Neuroscience 41 (26), 5638-5651, 2021
62021
From connectome to effectome: learning the causal interaction map of the fly brain
DA Pospisil, MJ Aragon, S Dorkenwald, A Matsliah, AR Sterling, ...
Biorxiv, 2023
22023
Accounting for bias in the estimation of r2 between two sets of noisy neural responses
DA Pospisil, W Bair
Journal of Neuroscience 42 (50), 9343-9355, 2022
2*2022
Preserved criterion flexibility in item recognition in older adults.
D Olfman, LL Light, M Schmalstig, DA Pospisil, R Pendergrass, C Chung
Psychology and Aging 32 (7), 675, 2017
22017
Revisiting the high-dimensional geometry of population responses in visual cortex
DA Pospisil, JW Pillow
bioRxiv, 2024.02. 16.580726, 2024
12024
Comparing response properties of V1 neurons to those of units in the early layers of a convolutional neural net
D Pospisil, W Bair
Journal of Vision 17 (10), 804-804, 2017
12017
Evaluating and interpreting a convolutional neural net as a model of V4
DA Pospisil, A Pasupathy, W Bair
12017
Estimating shape distances on neural representations with limited samples
DA Pospisil, BW Larsen, SE Harvey, AH Williams
arXiv preprint arXiv:2310.05742, 2023
2023
The estimation and explanation of tuning curves in intermediate visual areas
D Pospisil
University of Washington, 2021
2021
Uninformative dynamic visual stimuli aid in segregating two similar acoustic stimuli, but not in detecting a single stimulus in noise
RK Maddox, DA Pospisil, AK Lee
Journal of the Acoustical Society of America 140 (4_Supplement), 3207-3208, 2016
2016
The Natural Tendency of Feed Forward Neural Networks to Favor Invariant Units
DA Pospisil, W Bair
Real Neurons {\&} Hidden Units: Future directions at the intersection of …, 0
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