Population coding of song element sequence in the Bengalese finch HVC

Abstract

Birdsong is a complex vocalization composed of various song elements organized according to sequential rules. Two alternative views exist that explain the neural representation of song element sequences in the songbird brain. The finding of sequential selective neurons supports the idea that the song element sequence is encoded in a chain of rigid selective neurons. Alternatively, song structure could be encoded in an ensemble of relatively broad selective neurons arranged in a distributed manner. Here we attempted to determine which neural representation actually occurs in the song system by recording neural responses to various stimuli and performing information-theoretic analysis on the data obtained. We recorded the neural responses to all possible element pairs of stimuli in the Bengalese finch brain nucleus high vocal centre (HVC). Our results showed that each neuron has broad but differential response properties to element sequences beyond the structure of self-generated song. To quantify the transmitted information by such a broadly tuned neural population, we calculated the time course of mutual information between auditory stimuli and neural activities. Confounded information, which represents the relationship between present and previous elements, increased significantly immediately after stimulus presentation. These results indicate that the song element sequence is encoded in a neural ensemble in the HVC via population coding. These findings give us a new encoding scheme for the song element sequence using a distributed neural representation rather than the chain model of rigid selective neurons.