Deconvolution improves the detection and quantification of spike transmission gain from spike trains

Lidor Spivak; Amir Levi; Hadas E. Sloin; Shirly Someck; Eran Stark

doi:10.1038/s42003-022-03450-5

Communications Biology (May 2022)

Deconvolution improves the detection and quantification of spike transmission gain from spike trains

Lidor Spivak,
Amir Levi,
Hadas E. Sloin,
Shirly Someck,
Eran Stark

Affiliations

Lidor Spivak: Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University
Amir Levi: Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University
Hadas E. Sloin: Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University
Shirly Someck: Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University
Eran Stark: Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University

DOI: https://doi.org/10.1038/s42003-022-03450-5
Journal volume & issue: Vol. 5, no. 1
pp. 1 – 17

Abstract

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A mathematical framework and algorithm are developed that decompose the cross-correlation histogram between two spike trains to determine connectivity between two neurons.

Published in Communications Biology

ISSN: 2399-3642 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General)
Website: https://www.nature.com/commsbio/

About the journal