Conv2dKernel

Conv2dKernel#

class braintools.conn.Conv2dKernel(kernel, kernel_size, threshold=0.0, allow_self_connections=False, 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 magnitude exceeds a threshold. This allows implementing receptive field structures in spiking neural networks.

Only the first two position dimensions (x, y) are used; any extra columns (e.g. z) are ignored. kernel_size is the physical support of the kernel along BOTH axes: the kernel rows are mapped onto the y extent and the kernel columns onto the x extent, each bounded independently so non-square kernels cover the correct neighbourhood.

Parameters:

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

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

  • threshold (float) – Connection threshold - only kernel values whose magnitude exceeds this create connections. Thresholding on magnitude keeps essential negative coefficients (e.g. for edge-detection kernels). Default 0.0.

  • allow_self_connections (bool) – Whether a neuron may connect to itself when pre and post are the same population and coincide with the kernel centre (default: False).

  • 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)[source]#

Generate convolutional kernel connections.

Return type:

ConnectionResult