delvendahl/miniML
A deep learning framework for synaptic event detection
This tool helps neuroscientists and electrophysiologists automatically detect and analyze synaptic events from 1D time-series data. You input raw electrophysiological recordings from formats like HEKA .dat or Axon .abf files, and it outputs detailed information and statistics about detected synaptic events. It's designed for researchers working with neuronal signaling data.
Use this if you need to quickly and accurately identify miniature excitatory postsynaptic currents (mEPSCs) or other synaptic events in large datasets of electrophysiological recordings.
Not ideal if you are analyzing non-electrophysiological time-series data or require event detection in 2D or 3D datasets.
Stars
20
Forks
10
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 11, 2026
Commits (30d)
0
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