replay_trajectory_classification.clusterless_simulation.make_simulated_run_data

make_simulated_run_data(sampling_frequency: int = 1000, track_height: float = 175, 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, 190]), n_tetrodes: int = 5, make_inbound_outbound_neurons: bool = False)

Make simulated data of a rat running back and forth on a linear maze with unclustered spikes.

Parameters:

• sampling_frequency (int, optional)

• 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 extent of place fields

• place_field_means (np.ndarray, shape (n_neurons,), optional) – Location of the center of the Gaussian place fields.

• n_tetrodes (int, optional) – Total number of tetrodes to simulate

• make_inbound_outbound_neurons (bool, optional) – Create neurons with directional place fields

Returns:

• time (np.ndarray, shape (n_time,))

• position (np.ndarray, shape (n_time,))

• sampling_frequency (float)

• multiunits (np.ndarray, shape (n_time, n_features, n_electrodes))

• multiunits_spikes (np.ndarray (n_time, n_electrodes))

• place_field_means (np.ndarray (n_tetrodes, n_place_fields))