GolgiTendonOrganModel#

class GolgiTendonOrganModel(
simulation_time__ms: Quantity__ms,
time_step__ms: Quantity__ms,
gto_parameters: Dict[str, Any],
)[source]#

Bases: object

API wrapper for the Golgi Tendon Organ (GTO) model.

This class provides an intuitive interface for creating GTO models with user-friendly parameter names that are internally mapped to the correct format expected by the underlying GTO implementation.

The Golgi Tendon Organ is a proprioceptive sensory organ located at the muscle-tendon junction that detects muscle force/tension and provides feedback for motor control and protection against excessive forces.

The model is based on Lin & Crago (2002) and implements a logarithmic force-to-firing relationship with digital filtering for realistic afferent discharge patterns.

Parameters:

  • simulation_time__ms (Quantity__ms) – Total simulation time in milliseconds

  • time_step__ms (Quantity__ms) – Integration time step in milliseconds

  • gto_parameters (Dict[str, Any]) – Dictionary containing GTO model parameters

Methods

__init__

create_default_gto_parameters

Create default Golgi Tendon Organ parameter dictionary.

create_gto_parameters_for_muscle

Create GTO parameters optimized for specific muscle types.

integrate

Integrate the GTO model for one time step.

Attributes

ib_afferent_firing__Hz

Get Ib afferent firing rate time series in Hz.
integrate(muscle_force__N: float) → float[source]#

Integrate the GTO model for one time step.

Parameters:

muscle_force__N (float) – Current muscle force in Newtons

Returns:

Ib afferent firing rate in Hz

Return type:

float

property ib_afferent_firing__Hz: ndarray#

Get Ib afferent firing rate time series in Hz.

static create_default_gto_parameters() → Dict[str, Any][source]#

Create default Golgi Tendon Organ parameter dictionary.

The GTO model uses a logarithmic force-to-firing relationship: firing_rate = G1 * log(force/G2 + 1)

This is followed by digital filtering to create realistic temporal dynamics in the afferent discharge pattern.

Returns:

Dictionary of GTO parameters with detailed explanations

Return type:

Dict[str, Any]

Notes

Model based on: - Lin & Crago (2002): Mathematical model framework - Aniss et al. (1990b): Human GTO physiological data - Elias PhD thesis (pg 83): Implementation details

The logarithmic relationship captures the GTO’s ability to encode force over a wide dynamic range, from threshold detection of small forces to saturation at high forces, providing force feedback for motor control and protective reflexes.

static create_gto_parameters_for_muscle(
muscle_type: str = 'FDI',
) → Dict[str, Any][source]#

Create GTO parameters optimized for specific muscle types.

Parameters:

muscle_type (str, optional) – Type of muscle (“FDI”, “Sol”, “generic”), by default “FDI”

Returns:

Dictionary of muscle-specific GTO parameters

Return type:

Dict[str, Any]

Notes

Different muscles have different force production capabilities and thus require different GTO sensitivity parameters:

  • FDI (First Dorsal Interosseous): Small hand muscle, low forces

  • Sol (Soleus): Large calf muscle, high forces

  • Generic: General-purpose parameters