= Trace learning
{title2=$\Delta w_{ij}=\eta\bar y_i(t-1)x_j(t)$}
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.<Learning invariance from transformation sequences by Peter Földiák>[Abstract; Neural Computation 3, pp. 194–200, transformation-sequence learning]{ref}
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