Complex sequencing rules of birdsong can be explained by simple hidden Markov processes

TL;DR

This study demonstrates that complex birdsong sequencing rules in Bengalese finches can be effectively modeled using first-order hidden Markov processes, revealing hierarchical neural mechanisms underlying sequence generation.

Contribution

It shows that higher-order dependencies in birdsong can be explained by first-order hidden Markov models with redundant states, bridging complex behavior and simple statistical models.

Findings

Higher-order context dependencies are present in Bengalese finch songs.

First-order HMMs with redundant states accurately model these dependencies.

Modeling performance is comparable to second-order HMMs and better than GMMs.

Abstract

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical propertiesof the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable sequences, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. This property is shared with other complex sequential behaviors. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model (GMM)), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex sequences with higher-order dependencies.