 Trace learning

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]