GaussianKernel

class braintools.conn.GaussianKernel(sigma, max_distance=None, normalize=True, allow_self_connections=False, weight=None, delay=None, **kwargs)

Gaussian kernel connectivity for center-surround receptive fields.

Creates connections weighted by a Gaussian function of distance, useful for implementing smooth spatial receptive fields.

Only the first two position dimensions (x, y) are used; any extra columns (e.g. z) are ignored.

Parameters:

• sigma (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Standard deviation of Gaussian.

• max_distance (Array | ndarray | bool | number | bool | int | float | complex | Quantity | None) – Maximum distance for connections (default: 3*sigma).

• normalize (bool) – Whether to normalize the Gaussian (default: True). Normalization uses the 2-D constant 1 / (2 * pi * sigma**2) (positions are treated as 2-D, see above).

• allow_self_connections (bool) – Whether a neuron may connect to itself when pre and post are the same population (default: False). A coincident pre/post would otherwise connect at the Gaussian peak.

• weight (Initialization | float | int | ndarray | Array | Quantity | None) – Weight initialization (Gaussian is multiplied by this).

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

Examples

>>> positions = np.random.uniform(0, 1000, (500, 2)) * u.um
>>> gauss = GaussianKernel(
...     sigma=50 * u.um,
...     max_distance=150 * u.um,
...     weight=2.0 * u.nS
... )
>>> result = gauss(
...     pre_size=500, post_size=500,
...     pre_positions=positions, post_positions=positions
... )

generate(**kwargs)

Generate Gaussian kernel connections.

Return type:

ConnectionResult