Author

Abhishek Shah

Columbia UniversityORCID 0000-0001-9643-4564Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Organism:C. elegans· 1 Organism:mouse· 1 Approach:statistical model· 1

Frequent Co-Authors

Alain Michel GardieriD· 1 shared Attila LosonczyiD· 1 shared Brenna L. WilliamsiD· 1 shared Briana K. CheniD· 1 shared Christine Ann DennyiD· 1 shared Denis Joseph Paul DavidiD· 1 shared Dennis R Dumesnil· 1 shared Gergely F. TuriiD· 1 shared

Activity by Year

2025-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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1 shared paperNeuroscience

Alain Michel GardierORCID

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3 papers

Recent·Most cited

A tale of two receptors: simultaneous targeting of NMDARs and 5-HT4Rs exerts additive effects against stress

Briana K. Chen, Michelle Jin, Gergely F. Turi, Victor M. Luna, Abhishek Shah, Taylor Moniz, Margaret E. Shannon, Michaela M Pauers, Brenna L. Williams, Vananh Pham, Holly C. Hunsberger, Alain Michel Gardier, Indira Mendez‐David, Denis Joseph Paul David, Christine Ann Denny

This study investigates the combined effects of (R,S)-ketamine and prucalopride on stress-induced behaviors and neural activity, finding additive benefits in reducing fear, despair, and hyponeophagia.

Biological Psychiatry · Oct 1, 2025Peer-reviewedNeuroscience

Glucose promotes neuron morphology abnormalities and egg-laying defects dependentofthe serotonin signaling pathway in Caenorhabditis elegans

Manuel A Ruiz, Dennis R Dumesnil, Abhishek Shah, Mary L Ladage, Luhua Song, Pamela A. Padilla

This study shows that a glucose-rich diet in C. elegans causes neuron morphology issues and egg-laying defects, which are linked to serotonin signaling and the DAF16/FOXO pathway.

bioRxiv · Sep 20, 2025 · PreprintGenetics

Optimal sparsity in autoencoder memory models of the hippocampus

Abhishek Shah, René Hen, Attila Losonczy, Stefano Fusi

This study investigates how input compressibility affects optimal sparsity in sparse autoencoders, finding that optimal coding levels depend on memory demand and input statistics.

bioRxiv · Jan 6, 2025 · PreprintNeuroscience