Author

Daniel B. Ehrlich

Yale 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· 5

Signature Assays, Organisms & Methods

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Approach:statistical model· 5 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1

Frequent Co-Authors

John D. MurrayiD· 3 shared Alexander AtanasoviD· 1 shared Charles Gordon· 1 shared Clara G. DeFontesiD· 1 shared Daeyeol LeeiD· 1 shared David Brandfonbrener· 1 shared David van DijkiD· 1 shared Hyojung SeoiD· 1 shared

Activity by Year

2020-2022 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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3 shared papersNeuroscience

John D. MurrayORCID

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Author profile →

5 papers

Recent·Most cited

Geometry of neural computation unifies working memory and planning

Daniel B. Ehrlich, John D. Murray

This study proposes contingency representations as a unifying framework for working memory and planning, supported by human behavior and neural network simulations.

Proceedings of the National Academy of Sciences · Sep 6, 2022Peer-reviewedNeuroscience

Single-Cell Transcriptional Profiling of the Adult Corticospinal Tract Reveals Forelimb and Hindlimb Molecular Specialization

Noa Golan, Sierra D. Kauer, Daniel B. Ehrlich, Neal G. Ravindra, David van Dijk, William B.J. Cafferty

This study uses single-cell RNA sequencing to identify molecular markers of forelimb and hindlimb projecting corticospinal neurons, revealing subtype-specific gene expression patterns.

bioRxiv · Jun 2, 2021 · PreprintNeuroscience

PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks

Daniel B. Ehrlich, Jasmine T. Stone, David Brandfonbrener, Alexander Atanasov, John D. Murray

PsychRNN is a Python package for training RNNs on cognitive tasks, enabling neuroscience research with accessible, flexible, and extensible tools.

eNeuro · Dec 16, 2020Peer-reviewedNeuroscience

Confluence of timing and reward biases in perceptual decision-making dynamics

Maxwell Shinn, Daniel B. Ehrlich, Daeyeol Lee, John D. Murray, Hyojung Seo

This study investigates how temporal and reward contexts influence perceptual decision-making by training monkeys and modeling their behavior with generalized drift-diffusion models.

Journal of Neuroscience · Aug 24, 2020Peer-reviewedNeuroscience

From Value to Saliency: Neural Computations of Subjective Value under Uncertainty in PTSD

Ruonan Jia, Lital Ruderman, Charles Gordon, Daniel B. Ehrlich, Márk Horváth, Serena Mirchandani, Clara G. DeFontes, Steven Mark Southwick, John Harrison Krystal, Ilan Harpaz‐Rotem, Ifat Levy

This study examines how PTSD affects neural computations of subjective value under uncertainty using fMRI and a gambling paradigm, finding increased aversion to uncertainty and altered valuation system activity in PTSD patients.

bioRxiv · Apr 14, 2020 · PreprintNeuroscience