Song learning circuitry is thought to operate using a unique representation of each moment within each song syllable. Distinct timestamps for each moment in the song have been observed in the premotor cortical nucleus HVC, where neurons burst in sparse sequences. However, such sparse sequences are not present in very young birds, which sing highly variable syllables of random lengths. Furthermore, young birds learn by imitating a tutor song, and it was previously unclear precisely how the experience of hearing a tutor might shape auditory, motor, and evaluation pathways in the songbird brain. My thesis presents a framework for how these pathways may assemble during early learning, using simple neural mechanisms. I start with a neural network model for how premotor sequences may grow and split. This model predicts that the sequence-generating nucleus HVC would receive rhythmic tutoring inputs. Specifically, bursts marking the beginning of every tutor syllable could seed chains of sequential activity in HVC that could also be used to generate the bird's own song. I found such a signal when I recorded from an input to HVC; when the bird is listening, neurons burst at the rhythm of the tutor's song, and when the bird starts singing, neurons burst at the rhythm of the bird's song. I next used functional calcium imaging to track HVC sequences throughout the process of rapidly learning a new syllable. Analysis of these datasets led us to develop a new method for unsupervised detection of neural sequences. Using this method, I was able to observe sequences underlying the emergence of a new syllable, which can occur within days of first hearing a tutor. I was also able to observe sequences prior to tutor exposure, some of which were deployed in an abnormal fashion. In light of my new data, I expand on previous models of song learning to form a detailed hypothesis for how simple neural processes may perform song learning from start to finish.