Author

Gard Spreemann

École Polytechnique Fédérale de LausanneORCID 0000-0002-5945-0466Unclaimed ORCID profile · follow to get notified of new preprints

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Gard Spreemann1 paper

Biology Disciplines

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Organism:Drosophila· 1 Approach:experimental assay· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Kathryn P. HessiD· 2 shared Brian D. McCabeiD· 1 shared Evelyne RuchtiiD· 1 shared Jean-Baptiste Bardin· 1 shared R Steven StowersiD· 1 shared Samuel W. VernoniD· 1 shared Soumya BanerjeeiD· 1 shared Wei JiaoiD· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 29 mutual co-author links

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2 shared papersNeuroscience

Kathryn P. HessORCID

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2 papers

Recent·Most cited

NeuroscienceeLifePeer-reviewedJun 9, 2022

Intact Drosophila Central Nervous System Cellular Quantitation reveals Sexual Dimorphism

Wei Jiao, Gard Spreemann, Evelyne Ruchti…·Brian D. McCabe

The paper quantifies neurons and glia in the Drosophila larval CNS, revealing sexual dimorphism with more neurons in females and topological differences.

12 citations

Drosophilaimaging

𝕏 PostBluesky

NeuroscienceNetwork NeurosciencePeer-reviewedJan 1, 2019

Topological exploration of artificial neuronal network dynamics

Jean-Baptiste Bardin, Gard Spreemann, Kathryn P. Hess

The paper uses algebraic topology to classify artificial neuronal network dynamics by analyzing topological features from spike-train distances, showing improved classification accuracy with multiple distance measures.

27 citations

statistical modelmachine learning (classical)

𝕏 PostBluesky