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DataJoint pipeline and NWB conversion for Inagaki-2018 paper
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README.md

Inagaki et al., 2018, 2019

Data pipeline for Inagaki et al., 2018, 2019 from Svoboda Lab.

This project presents a DataJoint pipeline design for the data accompanying the papers:

Hidehiko K. Inagaki, Lorenzo Fontolan, Sandro Romani & Karel Svoboda. "Discrete Attractor Dynamics Underlies Persistent Activity in the Frontal Cortex" (2019) Nature (https://doi.org/10.1038/s41586-019-0919-7)

Hidehiko K. Inagaki, Miho Inagaki, Sandro Romani and Karel Svoboda. "Low-Dimensional and Monotonic Preparatory Activity in Mouse Anterior Lateral Motor Cortex" (2018) Jneurosci (https://doi.org/10.1523/JNEUROSCI.3152-17.2018)

The data in original MATLAB format (.mat) have been ingested into a DataJoint data pipeline.

The data: (Not available)

Design DataJoint data pipeline

This repository contains the Python 3.7 code of the DataJoint data pipeline design for this dataset, as well as scripts for data ingestions and visualization.

Pipeline diagram of intracellular and extracellular

Conversion to NWB 2.0

This repository contains the Python 3.7 code to convert the DataJoint pipeline into NWB 2.0 format (See https://neurodatawithoutborders.github.io/) Each NWB file represents one recording session. The conversion script can be found here

Demonstration of the data pipeline

Data queries and usages are demonstrated in this Jupyter Notebook, where several figures from the paper are reproduced.

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