Birdsong shares many similarities with language. The sequences of syllables in birdsong follow probabilistic transition rules and exhibit context-dependent syllable transitions similar to those observed in word sequences. In this talk, I will illustrate how neurons in the songbird brain encode the timing and sequences of these syllables. These neural mechanisms indicate that the probabilistic rules governing birdsong can be effectively described by a partially observable Markov model (POMM). This model incorporates states and probabilistic transitions between these states, with each state corresponding to a synaptic chain network of neurons in the brain area known as HVC. Each state is associated with a specific syllable. Within the framework of POMM, it is possible for multiple states to correspond to the same syllable, thereby encoding the context-dependent transitions of syllables. This suggests the existence of multiple syllable chains (or hidden neural states) in HVC that are activated for the same syllable in different parts of the song. We have developed an algorithm for uncovering these state multiplicities from the observed sequences of syllables, which facilitates predictions about the number of hidden states for each syllable. I will also demonstrate how POMM can be adapted to produce texts in the manner of large language models such as GPT.