replay_trajectory_classification.sorted_spikes_simulation.make_continuous_replay

make_continuous_replay(sampling_frequency: int = 1000, track_height: float = 180, running_speed: float = 15, place_field_means: ndarray = array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180]), replay_speedup: int = 120.0, is_outbound: bool = True) → tuple[ndarray, ndarray]

Make a simulated continuous replay.

Parameters:

• sampling_frequency (int, optional) – Samples per second

• track_height (float, optional) – Height of the simulated track

• running_speed (float, optional) – Speed of the simulated animal

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

• replay_speedup (int, optional) – _description_, by default REPLAY_SPEEDUP

• is_outbound (bool, optional) – _description_, by default True

Returns:

• replay_time (np.ndarray, shape (n_time,)) – Time in seconds.

• test_spikes (np.ndarray, shape (n_time, n_neurons)) – Binned spike indicator. 1 means spike occured. 0 means no spike occured.