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      Complex Sequencing Rules of Birdsong Can be Explained by Simple Hidden Markov Processes

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          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 such as human speech and musical performance, 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 properties of the complex birdsong of the Bengalese finch ( Lonchura striata var. domestica). Based on manual-annotated syllable labeles, 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. 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 behavioral sequences with higher-order dependencies.

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          Bayesian Interpolation

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              Central contributions to acoustic variation in birdsong.

              Birdsong is a learned behavior remarkable for its high degree of stereotypy. Nevertheless, adult birds display substantial rendition-by-rendition variation in the structure of individual song elements or "syllables." Previous work suggests that some of this variation is actively generated by the avian basal ganglia circuitry for purposes of motor exploration. However, it is unknown whether and how natural variations in premotor activity drive variations in syllable structure. Here, we recorded from the premotor nucleus robust nucleus of the arcopallium (RA) in Bengalese finches and measured whether neural activity covaried with syllable structure across multiple renditions of individual syllables. We found that variations in premotor activity were significantly correlated with variations in the acoustic features (pitch, amplitude, and spectral entropy) of syllables in approximately a quarter of all cases. In these cases, individual neural recordings predicted 8.5 +/- 0.3% (mean +/- SE) of the behavioral variation, and in some cases accounted for 25% or more of trial-by-trial variations in acoustic output. The prevalence and strength of neuron-behavior correlations indicate that each acoustic feature is controlled by a large ensemble of neurons that vary their activity in a coordinated manner. Additionally, we found that correlations with pitch (but not other features) were predominantly positive in sign, supporting a model of pitch production based on the anatomy and physiology of the vocal motor apparatus. Collectively, our results indicate that trial-by-trial variations in spectral structure are indeed under central neural control at the level of RA, consistent with the idea that such variation reflects motor exploration.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                7 September 2011
                : 6
                : 9
                : e24516
                Affiliations
                [1 ]ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency, Wako, Saitama, Japan
                [2 ]Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
                [3 ]RIKEN Brain Science Institute, Wako, Saitama, Japan
                [4 ]Graduate School of Science and Engineering, Saitama University, Saitama, Japan
                [5 ]Graduate School of Arts and Sciences, The University of Tokyo, Meguro, Tokyo, Japan
                Cajal Institute, Consejo Superior de Investigaciones Científicas, Spain
                Author notes

                Conceived and designed the experiments: KK KO MO. Performed the experiments: KS. Analyzed the data: KK KS. Wrote the paper: KK MO.

                Article
                PONE-D-11-05308
                10.1371/journal.pone.0024516
                3168521
                21915345
                047c69c2-d5f4-42ba-a16c-3b64548d8d5c
                Katahira et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 23 March 2011
                : 12 August 2011
                Page count
                Pages: 9
                Categories
                Research Article
                Biology
                Computational Biology
                Computational Neuroscience
                Coding Mechanisms
                Neuroscience
                Computational Neuroscience
                Coding Mechanisms
                Behavioral Neuroscience
                Zoology
                Ornithology
                Mathematics
                Probability Theory
                Markov Model

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                Uncategorized

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