Author

Daniel M. Bear

Stanford University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Organism:non-human primate· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Organism:human· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Aran NayebiiD· 2 shared Daniel L. K. YaminsiD· 2 shared James J. DiCarloiD· 2 shared Jonas KubiliusiD· 2 shared David SussilloiD· 1 shared Javier Sagastuy-BreñaiD· 1 shared Kohitij KariD· 1 shared Martin SchrimpfiD· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 32 mutual co-author links

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2 shared papersNeuroscience

Aran NayebiORCID

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2 papers

Recent·Most cited

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition

Aran Nayebi, Javier Sagastuy-Breña, Daniel M. Bear, Kohitij Kar, Jonas Kubilius, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

This study develops ConvRNN models to simulate primate ventral visual stream processing, finding that recurrent networks achieve better task performance and efficiency than deeper feedforward networks.

Neural Computation · Jul 7, 2022Peer-reviewedNeuroscience

CORnet: Modeling the Neural Mechanisms of Core Object Recognition

Jonas Kubilius, Martin Schrimpf, Aran Nayebi, Daniel M. Bear, Daniel L. K. Yamins, James J. DiCarlo

CORnet-S, a four-area recurrent network with areas pre-mapped to V1, V2, V4 and inferior temporal cortex, ranks among top Brain-Score models while having a shorter longest convolutional path than competitors.

bioRxiv · Sep 4, 2018 · PreprintNeuroscience✦ In-depth digest