replay_trajectory_classification.likelihoods.spiking_likelihood_glm.estimate_place_fields

estimate_place_fields(position: ndarray, spikes: ndarray, place_bin_centers: ndarray, place_bin_edges: ndarray, edges: ndarray | None = None, is_track_boundary: ndarray | None = None, is_track_interior: ndarray | None = None, penalty: float = 0.1, knot_spacing: int = 10) → DataArray

Gives the conditional intensity of the neurons’ spiking with respect to position.

Parameters:

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

• spikes (np.ndarray, shape (n_time, n_neurons))

• place_bin_centers (np.ndarray, shape (n_bins, n_position_dims))

• place_bin_edges (np.ndarray, shape (n_bins + 1, n_position_dims))

• is_track_boundary (None or np.ndarray)

• is_track_interior (None or np.ndarray)

• penalty (None or float, optional) – L2 penalty on regression. If None, penalty is smallest possible.

• knot_spacing (int, optional) – Spacing of position knots. Controls how smooth the firing rate is.

Returns:

conditional_intensity

Return type:

xr.DataArray, shape (n_bins, n_neurons)