LogisticProfile

class braintools.init.LogisticProfile(growth_rate, midpoint, max_distance=None)

Logistic distance profile.

Connection probability follows a logistic decay function, similar to sigmoid but normalized for distance-dependent connectivity.

Parameters:

• growth_rate (float) – Growth rate parameter controlling decay speed.

• midpoint (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Distance at which decay is at its midpoint.

• max_distance (Array | ndarray | bool | number | bool | int | float | complex | Quantity | None) – Maximum connection distance (connections beyond this are set to 0).

Examples

>>> import numpy as np
>>> import brainunit as u
>>> from braintools.init import LogisticProfile
>>>
>>> profile = LogisticProfile(
...     growth_rate=0.05,
...     midpoint=100.0 * u.um,
...     max_distance=500.0 * u.um
... )
>>> distances = np.array([0, 50, 100, 200, 500]) * u.um
>>> probs = profile.probability(distances)

probability(distances)

Calculate connection probability based on distance.

Parameters:

distances (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Array of distances between neuron pairs.

Returns:

probability – Connection probabilities (values between 0 and 1).

Return type:

ndarray