Author

Theodore Moskovitz

Columbia 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· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Assay:electronic health records· 1 Approach:experimental assay· 1 Organism:mouse· 1

Frequent Co-Authors

Alina Gubanova· 1 shared Claudia Clopath AiD· 1 shared Emmett J. ThompsoniD· 1 shared Francesca GreenstreetiD· 1 shared Grace W. LindsayiD· 1 shared Guangyu Robert YangiD· 1 shared Hernando Martínez VergaraiD· 1 shared Jasvin Kaur· 1 shared

Activity by Year

2018-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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1 shared paperNeuroscience

Alina Gubanova

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Author profile →

3 papers

Recent·Most cited

Action prediction error: a value-free dopaminergic teaching signal that drives stable learning

Francesca Greenstreet, Hernando Martínez Vergara, Yvonne Johansson, Sthitapranjya Pati, Laura Schwarz, Stephen C. Lenzi, Jesse P. Geerts, Matthew Wisdom, Alina Gubanova, Lars B. Rollik, Jasvin Kaur, Theodore Moskovitz, Joseph Cohen, Emmett J. Thompson, Troy W. Margrie, , Marcus Stephenson‐Jones

This study identifies a movement-based dopamine signal in mice that acts as a value-free teaching signal for learning sound-action associations, working alongside reward prediction error.

Nature · May 14, 2025Peer-reviewedNeuroscience

Understanding the functional and structural differences across excitatory and inhibitory neurons

Sun Minni, Li Ji-An, Theodore Moskovitz, Grace W. Lindsay, Kenneth D. Miller, Mario Dipoppa, Guangyu Robert Yang

The paper uses an artificial neural network to study why excitatory and inhibitory neurons differ in selectivity and connectivity, finding that these differences can be explained by a few factors.

Conference on Cognitive Computational Neuroscience (CCN) · Jan 1, 2019Peer-reviewedNeuroscience

A comparison of deep learning and linear-nonlinear cascade approaches to neural encoding

Theodore Moskovitz, Nicholas A. Roy, Jonathan W. Pillow

The paper compares deep learning and traditional methods for fitting linear-nonlinear-Poisson models of neural encoding, finding that deep learning methods achieve higher accuracy and require more filters.

bioRxiv · Nov 21, 2018 · PreprintNeuroscience