Cooperation of deterministic dynamics and random noise in production of complex syntactical avian song sequences: a neural network model.

Jun Tani,Kazuo Okanoya,Tetsu Okumura,Yuichi Yamashita

doi:10.3389/fncom.2011.00018

Jun Tani, Kazuo Okanoya + Show 2 more

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https://doi.org/10.3389/fncom.2011.00018

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Abstract

How the brain learns and generates temporal sequences is a fundamental issue in neuroscience. The production of birdsongs, a process which involves complex learned sequences, provides researchers with an excellent biological model for this topic. The Bengalese finch in particular learns a highly complex song with syntactical structure. The nucleus HVC (HVC), a premotor nucleus within the avian song system, plays a key role in generating the temporal structures of their songs. From lesion studies, the nucleus interfacialis (NIf) projecting to the HVC is considered one of the essential regions that contribute to the complexity of their songs. However, the types of interaction between the HVC and the NIf that can produce complex syntactical songs remain unclear. In order to investigate the function of interactions between the HVC and NIf, we have proposed a neural network model based on previous biological evidence. The HVC is modeled by a recurrent neural network (RNN) that learns to generate temporal patterns of songs. The NIf is modeled as a mechanism that provides auditory feedback to the HVC and generates random noise that feeds into the HVC. The model showed that complex syntactical songs can be replicated by simple interactions between deterministic dynamics of the RNN and random noise. In the current study, the plausibility of the model is tested by the comparison between the changes in the songs of actual birds induced by pharmacological inhibition of the NIf and the changes in the songs produced by the model resulting from modification of parameters representing NIf functions. The efficacy of the model demonstrates that the changes of songs induced by pharmacological inhibition of the NIf can be interpreted as a trade-off between the effects of noise and the effects of feedback on the dynamics of the RNN of the HVC. These facts suggest that the current model provides a convincing hypothesis for the functional role of NIf–HVC interaction.

Highlights

Due to its similarity to human speech in being a learned complex sequential behavior, birdsong has come to be a widely studied topic in neuroscience
KL-divergence decreases until it reaches a level that corresponds to the fluctuations typical of the Bengalese finch’s songs (Figure 7)
Simulation of chemical effects on the NIf In order to examine the effects of changes in the levels of noise and feedback, KL-divergence and entropy are calculated from sequences generated with various levels of noise and feedback

Summary

Introduction

Due to its similarity to human speech in being a learned complex sequential behavior, birdsong has come to be a widely studied topic in neuroscience. A series of syllables without branching constitutes what is referred to as a “chunk,” and sequences of chunks generate diverse “motifs.” Owing to the recursive structure and branching of the automaton describing their songs, the Bengalese finch is considered to generate an almost infinite number of different motifs. The complexity of this song structure is in contrast to the linearity of the songs produced by the Zebra finch, which is a close relative of the Bengalese finch (Zann, 1996)

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