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Birdsong is a complex vocalization composed of various song elements organized according to sequential rules. To reveal the neural representation of song element sequence, we recorded the neural responses to all possible element pairs of stimuli in the Bengalese finch brain nucleus HVC. Our results show that each neuron has broad but differential response properties to element sequences. We calculated the time course of population activity vectors and mutual information between auditory stimuli and neural activities. The clusters of population vectors responding to second elements had a large overlap, whereas the clusters responding to first elements were clearly divided. At the same timing, confounded information also significantly increased. These results indicate that the song element sequence is encoded in a neural ensemble in HVC via population coding. [ABSTRACT FROM AUTHOR]

We argue that the intentional control of vocal organ is the most basic predisposition for vocal learning and thus for language acquisition. Anatomical substrates for intentional vocal control are the direct cortical-medullar projections that connect face motor cortecies and the nucleus retro-ambiguus. We ask how such projections may be reinforced in non-vocal learners including macaque monkeys and rodents by behavioral manipulations. We hypothesize how such connections may be prepared in humans. [ABSTRACT FROM AUTHOR]

We propose an automata induction approach to modeling birdsongs on the basis of Angluin's induction algorithm, which ensures that k-reversible languages can be learned from positive samples in polynomial time. There are similarities between Angluin's algorithm and the vocal learning of songbirds; for example, during a critical period, songbirds learn songs from positive samples of conspecific birds. In our previous method, we could not construct song syntaxes for complex songs. In this paper, we introduce a pattern extraction method to improve our previous method and propose a new birdsong modeling method. We estimate the robustness and properness of our method by using artificial song data, and demonstrate that the song syntaxes of the Bengalese finch can be successfully represented as reversible automata. As a result, almost all Bengalese finchs' song syntaxes can be represented with lower k-reversibility; further, one song has 3-reversibility song syntax showing the highest reversibility. [ABSTRACT FROM AUTHOR]

The origin and evolution of language have been the subjects of numerous debates and hypotheses. Nevertheless, they remain difficult to study in a scientific manner. In this paper, we focus on the string-context mutual segmentation hypothesis proposed by Merker and Okanoya, which is based on experimental findings related to animal songs. As a first step in formally exploring this hypothesis, we model the evolution of agent discourse using coupled recurrent networks (RNNs). This model is a simplified representation of this hypothesis; that is, agents are situated in a single context (e.g., behavioral, social, or environmental) and they mutually learn their utterance strings from the prediction dynamics of their RNNs. Our simulation demonstrates the emergence of shared utterance patterns, which are culturally transmitted from one generation to the next. Furthermore, the distribution of the shared patterns changes over the course of this evolution. These findings demonstrate an important aspect of language evolution: "language shaped by society." [ABSTRACT FROM AUTHOR]

We propose a new methodology for ethology in terms of automata induction. Recent studies on Bengalese finch reported unique features of its songs. As opposed to most other songbirds, the songs of the Bengalese finch are neither monotonous nor random; they can be represented by a finite automaton, which we call song syntax [3]. Juvenile finches learn songs from their fathers during a critical period. The song learning has a similarity to the grammatical inference from positive samples, which is known as Angluin΄s algorithm [1]. This is an induction algorithm for inferring certain subclasses of regular languages, which are known as k-reversible languages, from positive samples, where k = 0,1,2,.... A regular language is k-reversible under the following condition: whenever two prefixes whose last k words match have a tail in common, then these prefixes have all tails in common. For each k, Angluin΄s algorithm provides a finite automaton that accepts the smallest k-reversible language, including the given finite positive sample within polynomial time. [ABSTRACT FROM AUTHOR]

Information on anterior forebrain pathway lesions and the birdsong of adult and bengalese finch song from chapter 8 of the book "Nature's Music: The Science of Birdsong" is presented. It explores the effects of Lesioning Area X or LMAN in the Brains of juvenile zebra finches. It examines the real-time involvement of the anterior forebrain pathway in song production.

Information on auditory feedback and bengalese finch song from chapter 7 of the book "Nature's Music: The Science of Birdsong" is presented. It presents quantitative analyses of the probability of syllable transitions in pre- and post-deafening songs. It explores the complexity of the song syntax of Bengalese finches.