MyoGen 0.8.0

Modular and extensible neuromuscular simulation framework for generating physiologically grounded motor-unit activity, muscle force, and EMG signals (surface and intramuscular)

Welcome to

The modular and extandable simulation toolkit for neurophysiology

Installation • Documentation • Examples • How to Cite

Overview

MyoGen is a modular and extensible neuromuscular simulation framework for generating physiologically grounded motor-unit activity, muscle force, and surface EMG signals.

It supports end-to-end modeling of the neuromuscular pathway, from descending neural drive and spinal motor neuron dynamics to muscle activation and bioelectric signal formation at the electrode level. MyoGen is designed for algorithm validation, hypothesis-driven research, and education, providing configurable building blocks that can be independently combined and extended.

Highlights

🧬 Biophysically inspired neuron models — NEURON-based motor neurons with validated calcium dynamics and membrane properties

🎯 Everything is inspectable — Complete access to every motor unit, spike time, fiber location etc. for rigorous algorithm testing

⚡️ Vectorized & parallel — Multi-core CPU processing with NumPy/Numba vectorization for fast computation

🔬 End-to-end simulation — From motor unit recruitment to high-density surface EMG in a single framework

📊 Reproducible science — Deterministic random seeds and standardized Neo Block outputs for exact replication

🧰 Comprehensive toolkit — Surface EMG, intramuscular EMG, force generation, and spinal network modeling

Installation

System Requirements

Platform	Before Installing MyoGen
Windows	NEURON 8.2.6 - Download, run installer, select "Add to PATH"
Linux	sudo apt install libopenmpi-dev (Ubuntu/Debian) or sudo dnf install openmpi-devel (Fedora)
macOS	brew install open-mpi

Python: 3.12 or higher required

[!CAUTION]

Windows Users: Install NEURON First

You MUST install NEURON before installing MyoGen on Windows.

1. Download: NEURON 8.2.6 Installer
2. Run the installer and select "Add to PATH" when prompted
3. Restart your terminal (close and reopen)
4. Then continue with the installation below

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Step 1: Install uv (Package Manager)

We use uv - a fast Python package manager. Install it first:

Windows (open PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Linux/macOS:

curl -LsSf https://astral.sh/uv/install.sh | sh

After installing, restart your terminal (close and reopen it).

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Step 2: Create a New Project

Open a terminal and navigate to where you want your project:

# Create a new folder for your project
mkdir my_emg_project
cd my_emg_project

# Initialize a Python project
uv init

# Add MyoGen to your project
uv add myogen

That's it! MyoGen is now installed and ready to use.

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Step 3: Verify Installation

Create a test file to make sure everything works:

# Create a test script
uv run python -c "from myogen import simulator; print('MyoGen installed successfully!')"

If you see MyoGen installed successfully! - you're all set!

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Alternative: pip install

If you prefer pip over uv:

pip install myogen

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For Developers (From Source)

git clone https://github.com/NsquaredLab/MyoGen.git
cd MyoGen
uv sync
uv run poe setup_myogen

Optional: GPU Acceleration

For 5-10× faster convolutions (requires NVIDIA GPU):

uv add cupy-cuda12x

Quick Start

Generate motor unit action potentials (MUAPs):

from myogen import simulator
import quantities as pq

# 1. Generate recruitment thresholds (100 motor units)
thresholds, _ = simulator.RecruitmentThresholds(
    N=100,
    recruitment_range__ratio=50,
    mode="fuglevand"
)

# 2. Create muscle model with fiber distribution
muscle = simulator.Muscle(
    recruitment_thresholds=thresholds,
    radius_bone__mm=1.0 * pq.mm,
    fiber_density__fibers_per_mm2=400 * pq.mm**-2,
    fat_thickness__mm=10 * pq.mm,
    autorun=True
)

# 3. Set up surface electrode array
electrode_array = simulator.SurfaceElectrodeArray(
    num_rows=5,
    num_cols=5,
    inter_electrode_distances__mm=5 * pq.mm,
    electrode_radius__mm=5 * pq.mm,
    bending_radius__mm=muscle.radius__mm + muscle.skin_thickness__mm + muscle.fat_thickness__mm,
)

# 4. Create surface EMG simulator
surface_emg = simulator.SurfaceEMG(
    muscle_model=muscle,
    electrode_arrays=[electrode_array],
    sampling_frequency__Hz=2048.0,
    MUs_to_simulate=[0, 1, 2, 3, 4]  # First 5 motor units
)

# 5. Simulate MUAPs (parallel processing)
muaps = surface_emg.simulate_muaps(n_jobs=-2)

Access MUAP data:

import numpy as np

# Get MUAP from motor unit 0
muap_signal = muaps.groups[0].segments[0].analogsignals[0]
print(f"MUAP shape: {muap_signal.shape}")  # (time, rows, cols)

# Extract from specific electrode (row 2, col 2)
electrode_muap = muap_signal[:, 2, 2]
peak_amplitude = np.max(np.abs(electrode_muap.magnitude))
print(f"Peak amplitude: {peak_amplitude:.3f} {electrode_muap.units}")

For full EMG simulation with spike trains, see examples

Documentation

📖 Read the full documentation

• User Guide — Working with simulation outputs
• API Reference — Complete class documentation
• Examples — Step-by-step tutorials from recruitment to EMG

How to Cite

If you use MyoGen in your research, please cite:

TBD

Contributing

Contributions welcome! See issues if you want to add a feature or fix a bug.

License

MyoGen is AGPL licensed. See LICENSE for details.