Motivation:

Most ML moderns become statistics once they are trained. Or one has to retrain the model to keep it adaptive to the new environment.

Main idea:

Instead of updating weights directly, update the rules (determined by the Hebbian coefficients) that update the weights

Weights are updated by Hebbian ABCD model, $\Delta w_{i,j} = \eta_w (A_w o_i o_j + B_w o_i +C_w o_j + D_w)$
Let $bf h$ be the vectorize Hebbian coefficients, ${\bf h}_{t+1} \leftarrow h_t + \frac{\alpha} {n \sigma} \sum_{i=1}^n F_i ({\bf h}_t + \Delta {\bf h}_i)$ , where $\Delta {\bf h}_i \sim \mathcal{N} ({\bf 0}, \sigma{\bf I})$ and $F_i$ is a fitnness evalution of ${\bf h}_t + \Delta {\bf h}_i$ (This I am not completely certained how it is evaluated)