A Hierarchical Neuronal Model for Generation and Online Recognition of Birdsongs

Izzet B Yildiz,Stefan J Kiebel,Olaf Sporns

doi:10.1371/journal.pcbi.1002303

Izzet B Yildiz, Stefan J Kiebel + Show 1 more

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https://doi.org/10.1371/journal.pcbi.1002303

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Abstract

The neuronal system underlying learning, generation and recognition of song in birds is one of the best-studied systems in the neurosciences. Here, we use these experimental findings to derive a neurobiologically plausible, dynamic, hierarchical model of birdsong generation and transform it into a functional model of birdsong recognition. The generation model consists of neuronal rate models and includes critical anatomical components like the premotor song-control nucleus HVC (proper name), the premotor nucleus RA (robust nucleus of the arcopallium), and a model of the syringeal and respiratory organs. We use Bayesian inference of this dynamical system to derive a possible mechanism for how birds can efficiently and robustly recognize the songs of their conspecifics in an online fashion. Our results indicate that the specific way birdsong is generated enables a listening bird to robustly and rapidly perceive embedded information at multiple time scales of a song. The resulting mechanism can be useful for investigating the functional roles of auditory recognition areas and providing predictions for future birdsong experiments.

Highlights

Songbirds are able to repeat the same, often complex songs with amazing precision
We show that the recognition mechanism is robust against differences in the anatomical connectivity pattern in the second layer
Four simulations showed that the Bayesian recognition mechanism works efficiently in several settings and its functional behavior might be helpful to understand the mechanisms of birdsong recognition

Summary

Introduction

When male birds sing to a female repeatedly, there is on average a 1% temporal deviation across the whole song [1,2] This combination of complexity and precision is remarkable. Studying the neuronal basis of birdsong generation may lead to an understanding of the mechanism underlying how sequences of song syllables are expressed as complex and temporally precise sound wave modulations. Such a mechanism may be useful for understanding how action sequences at a relatively slow time-scale (e.g. the words in a sentence) can be generated by a neuronal system while a high degree of precision is maintained in the output at a fast time-scale (e.g. the sound wave modulations necessary to form speech sounds). Recent findings [1,2,3] have shown that the song generation mechanism in birds is hierarchical where neurons in one particular high-level structure, HVC, fire in a specific sequence with high temporal precision and drive neurons in the lower level structure RA (robust nucleus of the arcopallium)

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