MexicanHat

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MexicanHat#

class braintools.conn.MexicanHat(sigma, max_distance=None, weight=None, delay=None, **kwargs)#

Mexican hat (Laplacian of Gaussian) connectivity pattern.

A special case of DoG with specific amplitude ratios to approximate the Laplacian of Gaussian. Creates strong lateral inhibition patterns.

Parameters:

  • sigma (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Standard deviation of the Gaussian (surround sigma will be sqrt(2)*sigma).

  • max_distance (Array | ndarray | bool | number | bool | int | float | complex | Quantity | None) – Maximum distance for connections (default: 4*sigma).

  • weight (Initialization | float | int | ndarray | Array | Quantity | None) – Weight initialization.

  • delay (Initialization | float | int | ndarray | Array | Quantity | None) – Delay initialization.

Examples

>>> positions = np.random.uniform(0, 1000, (500, 2)) * u.um
>>> mexican = MexicanHat(
...     sigma=40 * u.um,
...     weight=1.0 * u.nS
... )
>>> result = mexican(
...     pre_size=500, post_size=500,
...     pre_positions=positions, post_positions=positions
... )
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