= Hebbian learning
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Activity-dependent strengthening associated with coactivation of an input and its postsynaptic output. The idealized update $\Delta w_i\propto yx_i$ requires normalization, competition or other constraints to prevent indefinite weight growth.
In <Hebbian memory weights for a Hopfield network>, the local update $\Delta w_{ij}=\eta\xi_i\xi_j$ records a pairwise association, where $\eta>0$ and $\xi_i,\xi_j\in\{-1,1\}$. Equal signs strengthen a positive coupling; opposite signs favor a negative coupling. Summing such updates gives a <symmetric matrix> of pairwise associations. This is a storage prescription: it does not by itself compare the generated <probability distribution> with the <training data>, as <Boltzmann machine> learning does. Bipolar activity also makes joint inactivity contribute positively; this encoding is a mathematical idealization, not a literal statement about inactive biological cells.
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