replay_trajectory_classification.sorted_spikes_simulation.make_simulated_run_data#
- make_simulated_run_data(sampling_frequency: int = 1000, track_height: float = 180, running_speed: float = 15, n_runs: int = 15, place_field_variance: float = 36.0, place_field_means: ndarray = array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180]), make_inbound_outbound_neurons: bool = False) tuple[ndarray, ndarray, float, ndarray, ndarray][source]#
Make simulated data of a rat running back and forth on a linear maze with sorted spikes.
- Parameters:
sampling_frequency (float, optional) – Samples per second
track_height (float, optional) – Height of the simulated track
running_speed (float, optional) – Speed of the simulated animal
n_runs (int, optional) – Number of runs across the track the simulated animal will perform
place_field_variance (float, optional) – Spatial variance of the place field
place_field_means (np.ndarray, shape (n_neurons,), optional) – Location of the center of the Gaussian place fields.
make_inbound_outbound_neurons (bool) – Makes neurons direction selective.
- Returns:
time (np.ndarray, shape (n_time,))
position (np.ndarray, shape (n_time,)) – Position of the simualted animal
sampling_frequency (float) – Samples per second
spikes (np.ndarray, shape (n_time, n_neurons)) – Binned spike indicator. 1 means spike occured. 0 means no spike occured.
place_fields (np.ndarray, shape (n_time, n_neurons))