Author

Sadegh Ebrahimi

Stanford UniversityORCID 0000-0002-0132-3245Unclaimed 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· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1 Approach:statistical model· 1

Frequent Co-Authors

Mark J. SchnitzeriD· 2 shared Marta Blanco-PozoiD· 2 shared Adam S. ShaiiD· 1 shared Cameron SylberiD· 1 shared Christopher MirandaiD· 1 shared David Alexander KlindtiD· 1 shared Fatih DinçiD· 1 shared Fatih Dinct· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersNeuroscience

Mark J. SchnitzerORCID

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2 papers

Recent·Most cited

A geometric and dynamical theory of latent computations in biological neural networks

Fatih Dinç, Marta Blanco-Pozo, David Alexander Klindt, Francisco Acosta, Cameron Sylber, Yiqi Jiang, Sadegh Ebrahimi, Adam S. Shai, Hidenori Tanaka, Peng Yuan, Nina Miolane, Mark J. Schnitzer

The paper introduces latent processing units (LPUs) to explain how neural networks perform reliable computations despite variability, using six theorems to unify geometric and dynamical perspectives.

bioRxiv · Jul 15, 2026 · PreprintNeuroscience

ActSort: An active-learning accelerated cell sorting algorithm for large-scale calcium imaging datasets

Yiqi Jiang, Hakki Orhun Akengin, Ji Zhou, Mehmet Anil Aslihak, Yang Li, Radosław Chrapkiewicz, Oscar Hernandez, Sadegh Ebrahimi, Omar Jáidar, Yanping Zhang, Hakan Inan, Christopher Miranda, Fatih Dinct, Marta Blanco-Pozo, Mark J. Schnitzer

ActSort reduces human labor in cell sorting for calcium imaging by 1-3% and improves accuracy through active learning and expert features.

neural information processing systems · Jan 1, 2024Peer-reviewedNeuroscience