<p>Synaptic state matching (+SSM) is crucial for accuracy and long term stability. In the (-SSM) simulation, potentiation was unbounded (e.g. unconstrained by comparison of mean potentiation between the open and closed states). Spike-timing dependent covariance plasticity (+STCP) substantially improves convergence rate and accuracy as compared to a plasticity rule that is not modulated by spike-rate history (-STCP). In the (-STCP) simulation, Δw = +/−α. The 40 neuron SSM is trained on two alternating random spike patterns (features), each 40 time-steps long, with an intervening 20 time step quiescent period. Accuracy is a conservative measure of how well internally generated patterns match the missing input during the same time interval. It is defined as one minus the fraction of discordant spikes between the missing input spike trains and the internally generated spike trains. The value here is the average for all forty neurons. Accuracy can be negative in cases where internally generated activity is noisy and/or unstable. The accuracy does not reach maximum (1.0) because the network is unable to generate the earliest part of each random pattern due to the absence of any input during the quiescent period preceding the closed state.</p