Motor timing is crucial for a wide range of motor tasks and sequence generation. In the order of milliseconds, the underlying mechanisms remain unclear, whereas the zebra finch song system has shown to be an outstanding model to study temporal control and sequential neuronal activity in the same scale. Like human speech, birdsong relies on a tight muscle coordination, with its premotor nucleus, HVC, responsible for the precise control of song tempo. Current computational models of HVC rely on synfire chains, a purely feedforward network model that can account for HVC sequential activity. Synfire chains are however not robust to noise and function for a narrow range of feedforward weights, thus requiring fine tuning during learning. On the contrary, attractor dynamics provide networks with robust functional properties that make them an alternative to feedforward models. Therefore, we propose that HVC neuronal dynamics may be modelled using a Ring Attractor with a narrow Gaussian connectivity profile, where recurrent connections allow the formation of an activity bump that remains stable across a wide range of weights. In the case of asymmetrical connectivity, the bump of activity moves across the network, generating sequential neuronal activity. We show that the width of the activity bump, and thus the duration of transient neuronal activation, can be decreased to reproduce the brief activity bursts of HVC neurons. Additionally, we reproduce a syllable duration plasticity experiment by implementing a reinforcement learning mechanism in the network and show that consistent with behavioral results, the change in duration is specific to the target syllable. Lastly, we investigate further with a spiking neural network and show with a more biologically plausible and precise model, we are able to reproduce HVC's fast spiking dynamics and its specificity in learning to modify syllable duration.