Reconstructing neuronal circuitry from parallel spike trains

Ryota Kobayashi, Shuhei Kurita, Anno Kurth, Katsunori Kitano, Kenji Mizuseki, Markus Diesmann, Barry J. Richmond and Shigeru Shinomoto ()
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Ryota Kobayashi: National Institute of Informatics
Shuhei Kurita: RIKEN
Anno Kurth: Jülich Research Centre
Katsunori Kitano: Ritsumeikan University
Kenji Mizuseki: Osaka City University Graduate School of Medicine
Markus Diesmann: Jülich Research Centre
Barry J. Richmond: NIMH/NIH/DHHS
Shigeru Shinomoto: Kyoto University

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-12225-2

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