Hebbian learning

Activity-dependent strengthening associated with coactivation of an input and its postsynaptic output. The idealized update $\Delta w_i\propto y{x}_i$ 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.

A local association rule uses a decaying activity trace to connect representations encountered at nearby times. One implementation has ${\bar{y}}_i(t)=\rho {\bar{y}}_i(t-1)+(1-\rho )y_i(t)$ and $\Delta w_{ij}=\eta {\bar{y}}_i(t-1)x_j(t)$, followed by synaptic normalization. Temporal continuity can link distinct views of one object, but a trace spanning an identity change can create a false association.^[ref]

With $y=w\cdot x$, the squared weight norm $s=|w{|}^2$ satisfies $\tau \dot{s}=2y^2(1-\alpha s^2)$. It moves monotonically toward $s_{\ast }={\alpha }^{-1/2}$ without crossing it. Explicitly $(s-s_{\ast })/(s+s_{\ast })$ equals its initial value times $\exp [-(4\sqrt{\alpha }/\tau ){\int }_0^{t}y(u{)}^{2}du]$. Persistent excitation therefore drives $|w|$ to ${\alpha }^{-1/4}$; without excitation it can remain stationary. This nonlinear normalization avoids the unbounded amplification of unmodified Hebbian learning.

For a linear output $y=w^{T}x$, the second term limits the growth produced by the Hebbian term. On the averaged dynamics with $C=\mathbb{E}[x{x}^T]$, $\tau \dot{w}=Cw-\alpha (w^{T}Cw)w$. For $\alpha >0$, positive-eigenvalue equilibria have norm $1/\sqrt{\alpha }$ and align with eigenvectors of $C$. If inputs are centered, $C$ is their covariance matrix.

At the equilibrium $v_i/\sqrt{\alpha }$, linearization along orthonormal eigenvectors of $C$ gives the displayed rates, with the first equation applying to $j\ne i$. A simple positive largest eigenvalue makes its two normalized eigenvectors locally asymptotically stable; smaller eigenvalues have an unstable direction. Repeated largest eigenvalues give a manifold of equilibria with neutral directions. Generic convergence also needs a nonzero initial component in the largest eigenspace. The squared norm satisfies $\tau d\Vert w{\Vert }^2/dt=2(w^{T}Cw)(1-\alpha \Vert w{\Vert }^2)$.