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Reconstructing neuronal circuitry from parallel spike trains.

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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Kobayashi R,Kurita S,Kurth A,Kitano K,Mizuseki K,Diesmann M,Richmond BJ,Shinomoto S

doi

10.1038/s41467-019-12225-2

subject

Has Abstract

pub_date

2019-10-02 00:00:00

pages

4468

issue

1

issn

2041-1723

pii

10.1038/s41467-019-12225-2

journal_volume

10

pub_type

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