spiketools.spatial.place.compute_place_bins

spiketools.spatial.place.compute_place_bins(spikes, position, timestamps, bins, area_range=None, speed=None, min_speed=None, max_speed=None, time_threshold=None, occupancy=None, orientation=None)

Compute the spatially binned spiking activity.

Parameters:

spikes1d array

Spike times, in seconds.

position1d or 2d array

Position values.

timestamps1d array

Timestamps, in seconds, corresponding to the position values.

binsint or list of [int, int]

The bin definition for dividing up the space. If 1d, can be integer. If 2d should be a list, defined as [number of x_bins, number of y_bins].

area_rangelist of list, optional

Edges of the area to bin, defined as [[x_min, x_max], [y_min, y_max]].

speed1d array, optional

Speed values corresponding to each position. Should be the same length as timestamps.

min_speed, max_speedfloat, optional

Minimum and/or maximum speed thresholds to apply. Any spikes with an associated speed below the minimum or above maximum are dropped.

time_thresholdfloat, optional

A maximum time threshold, per bin observation, to apply. If provided, any bin values with an associated time length above this value are dropped.

occupancy1d or 2d array, optional

Computed occupancy across the space. If provided, used to normalize bin counts.

orientation{‘row’, ‘column’}, optional

The orientation of the position data. If not provided, is inferred from the position data.

Returns:

place_bins2d array

The spike activity per spatial bin.

Examples

Compute spike activity across 2d spatial bins:

>>> spikes = np.array([0.2, 0.25, 0.3, 0.38, 0.41, 0.5, 0.59, 0.77, 0.95, 0.96])
>>> position = np.array([[0.1, 0.3, 0.35, 0.36, 0.37, 0.4, 0.45, 0.46, 0.55, 0.7],
...                      [1.0, 1.5, 1.55, 1.65, 1.66, 2.0, 3.0, 4.0, 5.5, 7.0]])
>>> timestamps = np.array([0.01, 0.03, 0.2, 0.25, 0.45, 0.46, 0.47, 0.49, 0.5, 0.65])
>>> bins = [3, 2]
>>> compute_place_bins(spikes, position, timestamps, bins)
array([[5, 0, 0],
       [0, 1, 4]])