spiketools.spatial.information.compute_spatial_information¶

spiketools.spatial.information.compute_spatial_information(bin_firing, occupancy, normalize=False)[source]¶

Compute spatial information.

Parameters:

bin_firing1d or 2d array: Binned firing.
occupancy1d or 2d array: Occupancy across the space.
normalizebool, optional, default: False: If True, normalize the binned firing rate data by the occupancy. If False, it is assumed that the binned firing has already been normalized.

Returns:

infofloat: Spike information rate for spatial information (bits/spike).

Notes

This measure computes the spatial information between the firing and spatial location, as:

\[I = \sum{\lambda (x) log_2 \frac{\lambda(x)} {\lambda} p(x)dx}\]

References

[1]

Skaggs, W. E., McNaughton, B. L., & Gothard, K. M. (1992). An Information-Theoretic Approach to Deciphering the Hippocampal Code. Advances in neural information processing systems.

Examples

Compute spatial information across a 1d space:

>>> bin_firing = np.array([1, 1, 1, 1, 4])
>>> occupancy = np.array([1, 1, 1, 1, 1])
>>> info = compute_spatial_information(bin_firing, occupancy)
>>> print('{:5.4f}'.format(info))
0.3219

Compute spatial information across a 2d space:

>>> bin_firing = np.array([[1, 1, 1, 5],
...                        [1, 1, 1, 5]])
>>> occupancy = np.array([[1, 1, 1, 1],
...                       [1, 1, 1, 1]])
>>> info = compute_spatial_information(bin_firing, occupancy)
>>> print('{:5.4f}'.format(info))
0.4512