The study of plasticity in the intrinsic properties (IPs) of neurons is unveiling mechanisms beyond synaptic plasticity that relate network activity and learning. Prior results in zebra finches establish a relationship between the IPs of forebrain neurons and learned song. Within the premotor nucleus HVC, the IPs of HVC basal-ganglia-projecting neurons (HVCX) are developmentally regulated and differ across birds in a way that is related to their learned songs. In my PhD project, I investigated the role of song learning in regulating HVCX IPs. I used a counterbalanced design to raise siblings or unrelated birds to sing natural or modified songs. I patched onto HVCX neurons in slice and evaluated their firing properties in relation to the bird's song. I found that various features of HVCX IPs, and their variation, were related to the songs the birds sang. Examining the relation between IPs and learned song allowed me to delve deeper into the relation between HVCX and song features. I discovered a correlation between timing features of song and the rebound excitation of HVCX: neurons from birds who sang longer songs with long harmonic stacks had a combination of IPs that reflected stronger rebound excitation. This demonstrates an explicit link between neuronal IPs and features of learned behavior. Given that harmonic stacks are spectrally unchanging over their duration, this result also suggests a mechanism underlying HVCX neurons’ documented ability to integrate over long periods of time. To explore the possible mechanisms quantitatively, I used my results, along with established work, to develop a Hodgkin-Huxley-based network model of HVC that related in vitro IPs with in vivo bursting properties during singing. I conceptualized HVCX as interval encoders that detect sequences by summing rebound depolarization triggered by the removal of inhibition with monosynaptic excitatory events that occur later in time. In the network, HVCX are connected in a nested fashion to encode increasingly complex sequences. This model serves as a hypothesis linking neuronal IPs to network structure and behavior. In the course of these studies, I performed a number of experiments focused on bringing viral tools to the experimental toolbelt in the Margoliash lab (viral tracing, calcium imaging, and activity dependent fluorescent labeling), and replicated work showing internal temporal structure within zebra finch song. Altogether, my work focused on linking fundamental neural mechanisms of information processing to network structure, and learned behavior, and hypothesized how they might relate to temporal integration. My work accentuates the importance of including neuronal IPs when developing realistic network-level descriptions of neural circuits.