Welcome to the official GitHub page of the biomems lab at the University of Applied Sciences Aschaffenburg. This repository serves as a hub for sharing the algorithms, scripts, and tools developed by our lab for analyzing data from in vitro neuronal networks cultivated on microelectrode array (MEA) chips.
The biomems lab is dedicated to advancing the field of bio-microelectromechanical systems with a focus on neuroscience. Our research includes the development of tools and methodologies for studying neuronal behavior and interactions using cutting-edge technology.
Here, you will find resources to explore, analyze, and process neuronal network data with an emphasis on reproducibility and accessibility.
In addition to the resources hosted here, the following repositories are associated with the biomems lab and maintained on other GitHub pages:
A Python-based tool for organizing and storing data from neuronal recordings in a format that is both human- and machine-readable. This ensures efficient data handling and analysis workflows.
Automated Spike Train Analysis Kit A-stak is a GUI for analysing electrophysiological data. It allows to use algorithms from DrCell (MatLab) and Elephant (Python) simultaneously.
This repository contains Python scripts for applying machine learning techniques and complex network measures to an EEG dataset from ayahuasca experiments, providing insights into altered states of consciousness.
A machine learning workflow developed to diagnose autism spectrum disorder based on functional brain networks derived from fMRI data.
Python scripts for analyzing the acute effects of drugs, such as bicuculline, on spike trains from neuronal networks using machine learning workflows. This repository specializes in paired sample analysis (drug was applied on all MEA chips, so comparison before vs. after drug was done for all chips).
Python scripts for analyzing the longterm effects of drugs, such as BDNF or LSD, on spike trains from neuronal networks using machine learning workflows. This repository specializes in unpaired sample analysis (half of all MEA chips were treated with a drug, the other half treated with placebo).
We encourage collaboration and welcome contributions from researchers and developers. If you have any questions, feedback, or suggestions, feel free to open an issue or submit a pull request.
Thank you for visiting our GitHub page. We hope you find these resources helpful for your research and projects!