Kavli Affiliate: Kristofer Bouchard
| Authors: Jiali Lu, Sumithra Surendralal, Kristofer E Bouchard and Dezhe Z. Jin
| Summary:
Generative models are widely used in modeling sequences from language to birdsong. Here we show that a statistical test designed to guard against overgeneralization of a model in generating sequences can be used to infer minimal models for the variable syllable sequences of Bengalese finch songs. Specifically, the generative model we consider is the partially observable Markov model (POMM). A POMM consists of states and probabilistic transitions between them. Each state is associated with a syllable, and one syllable can be associated with multiple states. This multiplicity of association from syllable to states distinguishes a POMM from a simple Markov model, in which one syllable is associated with one state. The multiplicity indicates that syllable transitions are context-dependent. The statistical test is used to infer a POMM with minimal number of states from a finite number of observed sequences. We apply the method to infer POMMs for songs of six adult male Bengalese finches before and shortly after deafening, and show that auditory feedback plays an important role in creating context-dependent syllable transitions in Bengalese finch songs.