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Title: Reconstructing neuronal circuitry from parallel spike trains
Authors: Kobayashi, Ryota
Kurita, Shuhei
Kurth, Anno
Kitano, Katsunori
Mizuseki, Kenji
Diesmann, Markus
Richmond, Barry J.
Shinomoto, Shigeru
Author's alias: 小林, 亮太
篠本, 滋
Issue Date: 2-Oct-2019
Publisher: Springer Nature
Journal title: Nature Communications
Volume: 10
Thesis number: 4468
Abstract: State-of-the-art techniques allow researchers to record large numbers of spike trains in parallel for many hours. With enough such data, we should be able to infer the connectivity among neurons. Here we develop a method for reconstructing neuronal circuitry by applying a generalized linear model (GLM) to spike cross-correlations. Our method estimates connections between neurons in units of postsynaptic potentials and the amount of spike recordings needed to verify connections. The performance of inference is optimized by counting the estimation errors using synthetic data. This method is superior to other established methods in correctly estimating connectivity. By applying our method to rat hippocampal data, we show that the types of estimated connections match the results inferred from other physiological cues. Thus our method provides the means to build a circuit diagram from recorded spike trains, thereby providing a basis for elucidating the differences in information processing in different brain regions.
Description: 神経信号からニューロンのつながりを高精度で推定する解析法を開発 --神経活動データから脳の回路図を描く--. 京都大学プレスリリース. 2019-10-04.
Rights: © The Author(s) 2019. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
URI: http://hdl.handle.net/2433/244205
DOI(Published Version): 10.1038/s41467-019-12225-2
PubMed ID: 31578320
Related Link: http://www.kyoto-u.ac.jp/ja/research/research_results/2019/191002_1.html
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