Daniel Bear
Cited by
Cited by
Widespread transcription at neuronal activity-regulated enhancers
TK Kim, M Hemberg, JM Gray, AM Costa, DM Bear, J Wu, DA Harmin, ...
Nature 465 (7295), 182-187, 2010
Genome-wide analysis of MEF2 transcriptional program reveals synaptic target genes and neuronal activity-dependent polyadenylation site selection
SW Flavell, TK Kim, JM Gray, DA Harmin, M Hemberg, EJ Hong, ...
Neuron 60 (6), 1022-1038, 2008
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
Threedworld: A platform for interactive multi-modal physical simulation
C Gan, J Schwartz, S Alter, D Mrowca, M Schrimpf, J Traer, J De Freitas, ...
arXiv preprint arXiv:2007.04954, 2020
Task-Driven Convolutional Recurrent Models of the Visual System
A Nayebi, D Bear, J Kubilius, K Kar, S Ganguli, D Sussillo, JJ DiCarlo, ...
Advances in Neural Information Processing Systems, 5291-5302, 2018
A family of non-GPCR chemosensors defines an alternative logic for mammalian olfaction
PL Greer, DM Bear, JM Lassance, ML Bloom, T Tsukahara, ...
Cell 165 (7), 1734-1748, 2016
Cornet: Modeling the neural mechanisms of core object recognition
J Kubilius, M Schrimpf, A Nayebi, D Bear, DLK Yamins, JJ DiCarlo
BioRxiv, 408385, 2018
The evolving neural and genetic architecture of vertebrate olfaction
DM Bear, JM Lassance, HE Hoekstra, SR Datta
Current Biology 26 (20), R1039-R1049, 2016
The zebra fish cassiopeia mutant reveals that SIL is required for mitotic spindle organization
KL Pfaff, CT Straub, K Chiang, DM Bear, Y Zhou, LI Zon
Molecular and cellular biology 27 (16), 5887, 2007
Learning physical graph representations from visual scenes
D Bear, C Fan, D Mrowca, Y Li, S Alter, A Nayebi, J Schwartz, LF Fei-Fei, ...
Advances in Neural Information Processing Systems 33, 6027-6039, 2020
Visual grounding of learned physical models
Y Li, T Lin, K Yi, D Bear, D Yamins, J Wu, J Tenenbaum, A Torralba
International conference on machine learning, 5927-5936, 2020
Goal-driven recurrent neural network models of the ventral visual stream
A Nayebi, J Sagastuy-Brena, DM Bear, K Kar, J Kubilius, S Ganguli, ...
bioRxiv, 2021.02. 17.431717, 2021
Physion: Evaluating physical prediction from vision in humans and machines
DM Bear, E Wang, D Mrowca, FJ Binder, HYF Tung, RT Pramod, ...
arXiv preprint arXiv:2106.08261, 2021
Spatial transcriptomic reconstruction of the mouse olfactory glomerular map suggests principles of odor processing
IH Wang, E Murray, G Andrews, HC Jiang, SJ Park, E Donnard, ...
Nature neuroscience 25 (4), 484-492, 2022
Unsupervised segmentation in real-world images via spelke object inference
H Chen, R Venkatesh, Y Friedman, J Wu, JB Tenenbaum, DLK Yamins, ...
European Conference on Computer Vision, 719-735, 2022
CORnet: Modeling the neural mechanisms of core object recognition. BioRxiv, 408385
J Kubilius, M Schrimpf, A Nayebi, D Bear, DLK Yamins, JJ DiCarlo
arXiv preprint arXiv:1702.02181, 2018
Unifying (Machine) Vision via Counterfactual World Modeling
DM Bear, K Feigelis, H Chen, W Lee, R Venkatesh, K Kotar, A Durango, ...
arXiv preprint arXiv:2306.01828, 2023
Predicting children's and adults' preferences in physical interactions via physics simulation
G Kachergis, SF Radwan, B Long, JE Fan, M Lingelbach, DM Bear, ...
Proceedings of the Annual Meeting of the Cognitive Science Society 43 (43), 2021
Measuring and predicting variation in the interestingness of physical structures
C Holdaway, DM Bear, SF Radwan, MC Frank, DLK Yamins, JE Fan
Proceedings of the Annual Meeting of the Cognitive Science Society 43 (43), 2021
Using brain-score to evaluate and build neural networks for brain-like object recognition
M Schrimpf, J Kubilius, H Hong, NJ Majaj, R Rajalingham, C Ziemba, ...
Cosyne 19, Date: 2019/02/28-2019/03/03, Location: Lisbon, Portugal, 2019
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