SigmoidalCoupling

SigmoidalCoupling#

class brainmass.SigmoidalCoupling(x, conn, k=1.0, a=1.0, b=0.0, slope=1.0, midpoint=0.0)#

Sigmoidal coupling (TVB Sigmoidal, post-nonlinearity).

Applies a logistic nonlinearity after the network sum:

$$ c_i = k , sigma!left(mathrm{slope} cdot

left(a sum_j g_{ij} x_{D_{ij}} + b - mathrm{midpoint}right)right)

$$

where $\sigma$ is the logistic function and $g_{ij}$ the connection strength. k is the global coupling strength (TVB G; G ≡ k).

Parameters:

x (PrefetchDelayAt | PrefetchDelay | Prefetch | Callable) – The (optionally delayed) source state, shaped (..., N_out, N_in).
conn (Callable | Array | ndarray | bool | number | bool | int | float | complex | Quantity | Param) – Connection matrix (N_out, N_in) or square-flattened (N_out * N_in,).
k (Callable | Array | ndarray | bool | number | bool | int | float | complex | Quantity | Param) – Global coupling strength (TVB G).
a (Callable | Array | ndarray | bool | number | bool | int | float | complex | Quantity | Param) – Linear scaling of the network sum before the sigmoid.
b (Callable | Array | ndarray | bool | number | bool | int | float | complex | Quantity | Param) – Linear offset added before the sigmoid.
slope (Callable | Array | ndarray | bool | number | bool | int | float | complex | Quantity | Param) – Steepness of the logistic.
midpoint (Callable | Array | ndarray | bool | number | bool | int | float | complex | Quantity | Param) – Centre of the logistic.

Return type:

Any