Author

Tao Hong

University of PittsburghORCID 0000-0003-4244-9028Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Assay:electronic health records· 2 Organism:non-human primate· 2 Assay:ATAC-seq· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Organism:rat· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

William R. StaufferiD· 3 shared Aydin AlikayaiD· 2 shared Andreas R. PfenningiD· 1 shared Andreea C. BostaniD· 1 shared Ashley R. BrowniD· 1 shared BaDoi N. PhaniD· 1 shared Bryan M. HooksiD· 1 shared Chaitanya SrinivasaniD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersNeuroscience

William R. StaufferORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Neural correlates of combinatorial reasoning in prefrontal cortex

Tao Hong, William R. Stauffer

This study identifies neural correlates of combinatorial reasoning in rhesus macaques by analyzing single-unit activity in the dorsolateral prefrontal cortex during decision-making tasks.

bioRxiv · Mar 5, 2026 · PreprintNeuroscience

Cell Type Specific Enhancers for Dorsolateral Prefrontal Cortex.

Jing He, BaDoi N. Phan, Willa G. Kerkhoff, Aydin Alikaya, Tao Hong, Olivia R Brull, J. Megan Fredericks, Morgan Sedorovitz, Chaitanya Srinivasan, Michael J. Leone, Olivia M. Wirfel, Ashley R. Brown, Samuel Dauby, Rachel K. Tittle, Meng Kuan Lin, , William R. Stauffer

The paper identifies cell type-specific enhancers for the DLPFC in NHPs using single nucleus RNA-Seq and ATAC-Seq, validates them with AAV injections, and demonstrates functional expression in specific neuron types.

Neuron · May 1, 2025Peer-reviewedNeuroscience

Rare Rewards Amplify Dopamine Learning Responses

Kathryn M. Rothenhoefer, Tao Hong, Aydin Alikaya, William R. Stauffer

This study shows that dopamine responses encode probability distributions of reward prediction errors, with rare rewards amplifying these responses and enhancing learning and arousal.

bioRxiv · Nov 22, 2019 · PreprintNeuroscience