Recruitment Thresholds

The first step in using MyoGen is to generate the recruitment thresholds of the motor units (MUs).

Note

A recruitment threshold is the minimum force required to activate a MU.

In MyoGen, the threshold is defined between 0 and 1, where 0 is the minimum force required to activate a MU and 1 is the maximum force required to activate a MU.

MyoGen offers 4 different models to generate the recruitment thresholds:

• Fuglevand model: Classic exponential distribution (Fuglevand et al., 1993)

• De Luca model: Slope-corrected exponential distribution (De Luca & Contessa, 2012)

• Konstantin model: Exponential with explicit maximum threshold control (Konstantin et al., 2019)

• Combined model: Hybrid approach combining De Luca shape with Konstantin scaling (Ours)

Import Libraries

from pathlib import Path

import joblib

from myogen import simulator

Define Parameters

Each recruitment threshold simulation is defined by the following parameters:

• N: Number of motor units in the pool

• recruitment_range: Recruitment range (max_threshold / min_threshold)

Note

The recruitment_range is defined as the ratio between the maximum and minimum recruitment thresholds. For example, if the recruitment_range is 50, the biggest MU will have a recruitment threshold 50 times bigger than the smallest MU.

With some models you also need to define additional parameters:

• De Luca model:

  • deluca__slopes: Different slope values to demonstrate variety

• Konstantin model:

  • konstantin__max_threshold: Maximum recruitment threshold

• Combined model:

  • deluca__slopes: Different slope values to demonstrate variety

  • konstantin__max_threshold: Maximum recruitment threshold

n_motor_units = 100  # Number of motor units in the pool
recruitment_range = 50  # Recruitment range (max_threshold / min_threshold)

# Model specific parameters
fuglevand_params = {}  # No additional parameters needed

deluca_slopes = [0.001, 5, 25, 50]  # Different slope values to demonstrate variety

konstantin_max_threshold = 1.0  # Maximum recruitment threshold

combined_slopes = [0.001, 5, 25, 50]  # Slopes for combined model
combined_max_threshold = 1.0  # Maximum threshold for combined model

Generate Recruitment Thresholds

Generating the recruitment thresholds is done by calling the generate_mu_recruitment_thresholds function.

Important

MyoGen is intended to be used with the following API:

from myogen import simulator

# load_nmodl_files()

# Create results directory
save_path = Path("./results")
save_path.mkdir(exist_ok=True)

# 1. Fuglevand Model
rt_fuglevand, rtz_fuglevand = simulator.generate_mu_recruitment_thresholds(
    N=n_motor_units, recruitment_range=recruitment_range, mode="fuglevand"
)

# 2. De Luca Model with different slopes
deluca_results = {}
for slope in deluca_slopes:
    rt, _ = simulator.generate_mu_recruitment_thresholds(
        N=n_motor_units,
        recruitment_range=recruitment_range,
        deluca__slope=slope,
        mode="deluca",
    )
    deluca_results[slope] = rt

# 3. Konstantin Model
rt_konstantin, rtz_konstantin = simulator.generate_mu_recruitment_thresholds(
    N=n_motor_units,
    recruitment_range=recruitment_range,
    konstantin__max_threshold=konstantin_max_threshold,
    mode="konstantin",
)

# 4. Combined Model with different slopes
combined_results = {}
for slope in combined_slopes:
    rt, _ = simulator.generate_mu_recruitment_thresholds(
        N=n_motor_units,
        recruitment_range=recruitment_range,
        deluca__slope=slope,
        konstantin__max_threshold=combined_max_threshold,
        mode="combined",
    )
    combined_results[slope] = rt

Save Recruitment Thresholds

Note

All MyoGen objects can be saved to a file using joblib. This is useful to avoid re-running expensive simulations if you need to use the same parameters.

joblib.dump(combined_results[50], save_path / "thresholds.pkl")

['results/thresholds.pkl']

Plot Recruitment Thresholds

The recruitment thresholds can be plotted using the plot_recruitment_thresholds function.

Note

Plotting helper functions are available in the myogen.utils.plotting module.

from myogen.utils.plotting import plot_recruitment_thresholds

# Suppress font warnings to keep output clean
import warnings
import logging

warnings.filterwarnings("ignore", message=".*Font family.*not found.*")
warnings.filterwarnings("ignore", message=".*findfont.*")
logging.getLogger("matplotlib.font_manager").setLevel(logging.ERROR)

from myogen.utils.plotting import plot_recruitment_thresholds
from matplotlib import pyplot as plt

Fuglevand Model Visualization

The Fuglevand model uses a simple exponential distribution for recruitment thresholds. This is the classic approach from Fuglevand et al. (1993).

print("Plotting Fuglevand model...")
with plt.xkcd():
    _, ax = plt.subplots(figsize=(10, 6))
    plot_recruitment_thresholds(
        rt_fuglevand, [ax], model_name="Fuglevand", colors="#90b8e0"
    )
plt.tight_layout()
plt.show()

Plotting Fuglevand model...
Creating Fuglevand model visualization...

De Luca Model Visualization

The De Luca model includes a slope correction parameter that allows control over the shape of the recruitment threshold distribution.

print("Plotting De Luca model...")
with plt.xkcd():
    _, ax = plt.subplots(figsize=(10, 6))
    plot_recruitment_thresholds(
        deluca_results,
        [ax],
        model_name="De Luca",
        colors=["#90b8e0", "#af8bff", "#90b8e0", "#af8bff"],
    )
plt.tight_layout()
plt.show()

Plotting De Luca model...
Creating De Luca model visualization...

Konstantin Model Visualization

The Konstantin model provides explicit control over the maximum recruitment threshold while maintaining physiological recruitment patterns.

print("Plotting Konstantin model...")
with plt.xkcd():
    _, ax = plt.subplots(figsize=(10, 6))
    plot_recruitment_thresholds(
        rt_konstantin,
        [ax],
        model_name="Konstantin",
        y_max=konstantin_max_threshold,
        colors="#90b8e0",
        markers="s",
    )
plt.tight_layout()
plt.show()

Plotting Konstantin model...
Creating Konstantin model visualization...

Combined Model Visualization

The Combined model merges De Luca’s shape control with Konstantin’s scaling, offering the most flexibility for custom recruitment patterns.

print("Plotting Combined model...")
with plt.xkcd():
    _, ax = plt.subplots(figsize=(10, 6))
    plot_recruitment_thresholds(
        combined_results,
        [ax],
        model_name="Combined",
        y_max=combined_max_threshold,
        colors=["#90b8e0", "#af8bff", "#90b8e0", "#af8bff"],
    )
plt.tight_layout()
plt.show()

Plotting Combined model...
Creating Combined model visualization...