A deep learning framework for synaptic event detection
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Updated
Jan 8, 2025 - Jupyter Notebook
A deep learning framework for synaptic event detection
calculates synaptic parameters by fitting synaptic currents from train stimulation to NpRf model. Estimates N (RRP size), p (initial release probability), R (replenishment), and f (synaptic facilitation).
A Python package for analyzing single-unit spike-sorted data to infer synaptic connections in neural circuits.
This project explores the effect of insulin on neuronal communication and excitability in the rat dorsomedial hypothalamus.
Performs offline series resistance correction/compensation of recorded currents based on "Traynelis SF (1998) Software-based correction of single compartment series resistance errors. J Neurosci Methods 86:25–34."
Python2 code accompanying the paper https://doi.org/10.1101/748400
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