Code from: Inference technique for the synaptic conductances in rhythmically active networks and application to respiratory central pattern generation circuits
Data files
Mar 25, 2026 version files 10.61 MB
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README.md
2.54 KB
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synaptic_conductance_inference-master.zip
10.61 MB
Abstract
Unraveling synaptic interactions between excitatory and inhibitory interneurons within rhythmic neural circuits, such as central pattern generation (CPG) circuits for rhythmic motor behaviors, is critical for deciphering circuit interactions and functional architecture, which is a major problem for understanding how neural circuits operate. Here we present a general method for extracting and separating patterns of inhibitory and excitatory synaptic conductances at high temporal resolution from single neuronal intracellular recordings in rhythmically active networks. These post-synaptic conductances reflect the combined synaptic inputs from the key interacting neuronal populations and can reveal the functional connectome of the active circuits. To illustrate the applicability of our analytic technique, we employ our method to infer the synaptic conductance profiles in identified rhythmically active interneurons within key microcircuits of the mammalian (mature rat) brainstem respiratory CPG and provide a perspective on how our approach can resolve the functional interactions and circuit organization of these interneuron populations. We demonstrate the versatility of our approach, which can be applied to any other rhythmic circuits where conditions allow for neuronal intracellular recordings.
https://doi.org/10.5061/dryad.bcc2fqzrp
Robust inference technique for extracting excitatory and inhibitory synaptic conductances from membrane potential or current recordings in rhythmically active biological networks.
This methodology uses linear regression of voltage-current relationships at distinct phases of a rhythmic cycle to accurately decompose total synaptic input into its constituent components.
Inference Tool (med2)
The med2 utility is the core engine for processing experimental or simulated electrophysiological recordings. It performs several key steps:
- Filtering: Implements median filtering and wavelet transforms to reduce noise.
- Cycle Detection: Identifies rhythmic components and segments the data into phases.
- Linear Regression: Calculates the best-fit conductance and reversal potential for each phase bin.
- Conductance Decomposition: Infers the time-course of excitatory ($g_{e}$) and inhibitory ($g_{i}$) conductances.
Usage
./med2 [options] < input_data > processed_data 2> phase_data
Key Command Line Options:
-vc: Enable Voltage Clamp mode (expects current and voltage inputs).-q [val]: Threshold for cycle detection (default: 0).-f [n]: Median filter window size (default: 5).-p [ms]: Minimum period to consider for rhythmic activity.-s [val]: Scale factor for noise-robust inference.
Workflow
- Pre-processing: Ensure your data is in a tab-separated format (typically: Time, Command Current, Membrane Potential).
- Inference: Run
med2with appropriate parameters to generateprocessed_dataandphase_data. - Analysis: Use the provided gnuplot scripts to visualize the quality of the fits.
Visualization
The project includes specialized Gnuplot scripts for validation:
linreg.plot: Generates a multi-panel PDF (linreg.pdf) showing the linear voltage-current relationship for each phase of the cycle. This is used to verify the linearity and quality of the inference.plot: Visualizes the final inferred conductances ($g_{e}$ and $g_{i}$) compared to the original recordings.
Building
The tool is written in C++ and can be compiled using a standard compiler:
# Using Makefile
make med2
Requires a C++ compiler (like g++ or icpx).
Files
synaptic_conductance_inference-master.zip
