Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks

Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks

Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-den...

Full description

Bibliographic Details
Main Authors: Taehoon Kim, Dexiong Chen, Philipp Hornauer, Vishalini Emmenegger, Julian Bartram, Silvia Ronchi, Andreas Hierlemann, Manuel Schröter, Damian Roqueiro
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-01-01
Series:Frontiers in Neuroinformatics
Subjects:
Graph Neural Network
in vitro neural network
pharmacological perturbation
extracellular electrophysiology
single neuron activity
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/fninf.2022.1032538/full

Internet

https://www.frontiersin.org/articles/10.3389/fninf.2022.1032538/full

Similar Items