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Codebase for discovery of latent dynamic connectivity in brain cortical networks

Home Page: https://www.biorxiv.org/content/10.1101/2023.08.08.552512v1

License: MIT License

Jupyter Notebook 98.75% Python 1.11% Shell 0.15%
brain dynamical-systems inference-algorithms machine-learning-algorithms networks-biology

dynetcp's Introduction

Codes for training and analyzing the Dynamic Network Connectivity Predictor model

This codebase accompanies the paper "Discovery of latent dynamic connectivity in brain cortical networks from massive spiking data" by Colin Graber, Yurii Vlasov, and Alexander Schwing

https://www.biorxiv.org/content/10.1101/2023.08.08.552512

It is a probabilistic model to discover the dynamic connectivity structure in a network of spiking cortical neurons. The model is designed to mine large-scale recordings of spiking activity in the brain. The model learns the latent dynamic functional interactions in brain networks.

Accompanying datasets are available for download at Figshare https://doi.org/10.6084/m9.figshare.24879036

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