MotorNeuronPool#
- class myogen.simulator.MotorNeuronPool(recruitment_thresholds, diameter_soma_min=77.5, diameter_soma_max=82.5, y_min=18.0, y_max=36.0, diameter_dend_min=41.5, diameter_dend_max=62.5, x_min=-5500, x_max=-6789, vt_min=12.35, vt_max=20.9, kf_cond_min=4, kf_cond_max=0.5, CV=0.01)[source]#
Bases:
object
Motor neuron pool with specified parameters
- Parameters:
recruitment_thresholds (
numpy.ndarray
) – Array of recruitment thresholds for the motor neuronsdiameter_soma_min (
float
) – Minimum diameter of the somadiameter_soma_max (
float
)y_min (
float
) – Minimum y coordinate of the somay_max (
float
) – Maximum y coordinate of the somadiameter_dend_min (
float
) – Minimum diameter of the dendritediameter_dend_max (
float
) – Maximum diameter of the dendritex_min (
float
) – Minimum x coordinate of the dendritex_max (
float
) – Maximum x coordinate of the dendritevt_min (
float
) – Minimum voltage threshold of the neuronvt_max (
float
) – Maximum voltage threshold of the neuronkf_cond_min (
float
) – Minimum conductance density of the potassium fast channelkf_cond_max (
float
) – Maximum conductance density of the potassium fast channelCV (
float
) – Coefficient of variation of the noise
- Returns:
Motor neuron pool with specified parameters
- Return type:
Methods
__init__
Generate the spike trains for as many neuron pools as input currents there are
- generate_spike_trains(input_current__matrix, timestep__ms=0.05, noise_mean__nA=30, noise_stdev__nA=30, what_to_record=[{'locations': ['dendrite', 'soma'], 'variables': ['v']}])[source]#
Generate the spike trains for as many neuron pools as input currents there are
Each motor neuron pools have each “neurons_per_pool” neurons. The input currents are injected into each pool, and the spike trains are recorded.
- Parameters:
input_current__matrix (
INPUT_CURRENT__MATRIX
) – Matrix of shape (n_pools, t_points) containing current values Each row represents the current for one pooltimestep__ms (
float
) – Simulation timestep__ms in msnoise_mean__nA (
float
) – Mean of the noise current in nAnoise_stdev__nA (
float
) – Standard deviation of the noise current in nAwhat_to_record (
list
[dict
['variables'
,'to_file'
,'sampling_interval'
,'locations'
],Any
]) –List of dictionaries specifying what to record.
- Each dictionary contains the following keys:
variables: list of strings specifying the variables to record
to_file: bool specifying whether to save the recorded data to a file
sampling_interval: int specifying the sampling interval in ms
locations: list of strings specifying the locations to record from
See pyNN documentation for more details: https://pynn.readthedocs.io/en/stable/recording.html.
Spike trains are recorded by default.
- Returns:
spike_trains (
SPIKE_TRAIN__MATRIX
) – Matrix of shape (n_pools, neurons_per_pool, t_points) containing spike trains Each row represents the spike train for one pool Each column represents the spike train for one neuron Each element represents whether the neuron spiked at that time pointactive_neuron_indices (
list
[numpy.ndarray
]) – List of arrays of indices of the active neurons in each pooldata (
list
[neo.core.segment.Segment
]) – List of neo segments containing the recorded data
- Return type:
tuple[Annotated[ndarray[tuple[int, …], dtype[bool]], beartype.vale.Is[lambda x: x.ndim == 3]], list[ndarray], list[Segment]]