replay_trajectory_classification.likelihoods.spiking_likelihood_kde.estimate_place_fields_kde

estimate_place_fields_kde(position: ndarray, spikes: ndarray, place_bin_centers: ndarray, position_std: ndarray, is_track_boundary: ndarray | None = None, is_track_interior: ndarray | None = None, edges: list[ndarray] | None = None, place_bin_edges: ndarray | None = None, use_diffusion: bool = False, block_size: int | None = None) → 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,)) – Indicator of spike or no spike at current time.

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

• position_std (float or array_like, shape (n_position_dims,)) – Amount of smoothing for position. Standard deviation of kernel.

• is_track_boundary (None or np.ndarray, shape (n_bins,))

• is_track_interior (None or np.ndarray, shape (n_bins,))

• edges (None or list of np.ndarray)

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

• use_diffusion (bool) – Respect track geometry by using diffusion distances

• block_size (int) – Size of data to process in chunks

Returns:

conditional_intensity

Return type:

np.ndarray, shape (n_bins, n_neurons)