Conv2dKernel

class braintools.conn.Conv2dKernel(kernel, kernel_size, threshold=0.0, weight=None, delay=None, **kwargs)

Convolutional kernel connectivity for spatially arranged point neurons.

Applies a 2D convolution kernel to neuron positions, creating connections where the kernel weight exceeds a threshold. This allows implementing receptive field structures in spiking neural networks.

Parameters:

• kernel (ndarray) – 2D convolution kernel array.

• kernel_size (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Physical size of the kernel in position units.

• threshold (float) – Connection threshold - only kernel values above this create connections.

• weight (Initialization | float | int | ndarray | Array | Quantity | None) – Weight initialization (kernel values are multiplied by this).

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

Examples

>>> # Create 5x5 Gaussian-like kernel
>>> kernel = np.array([
...     [0.04, 0.12, 0.18, 0.12, 0.04],
...     [0.12, 0.37, 0.56, 0.37, 0.12],
...     [0.18, 0.56, 1.00, 0.56, 0.18],
...     [0.12, 0.37, 0.56, 0.37, 0.12],
...     [0.04, 0.12, 0.18, 0.12, 0.04]
... ])
>>> positions = np.random.uniform(0, 1000, (500, 2)) * u.um
>>> conn = Conv2dKernel(
...     kernel=kernel,
...     kernel_size=100 * u.um,
...     threshold=0.1,
...     weight=1.0 * u.nS
... )
>>> result = conn(
...     pre_size=500, post_size=500,
...     pre_positions=positions, post_positions=positions
... )

generate(**kwargs)

Generate convolutional kernel connections.

Return type:

ConnectionResult