43 research outputs found
A compact statistical model of the song syntax in Bengalese finch
Songs of many songbird species consist of variable sequences of a finite
number of syllables. A common approach for characterizing the syntax of these
complex syllable sequences is to use transition probabilities between the
syllables. This is equivalent to the Markov model, in which each syllable is
associated with one state, and the transition probabilities between the states
do not depend on the state transition history. Here we analyze the song syntax
in a Bengalese finch. We show that the Markov model fails to capture the
statistical properties of the syllable sequences. Instead, a state transition
model that accurately describes the statistics of the syllable sequences
includes adaptation of the self-transition probabilities when states are
repeatedly revisited, and allows associations of more than one state to the
same syllable. Such a model does not increase the model complexity
significantly. Mathematically, the model is a partially observable Markov model
with adaptation (POMMA). The success of the POMMA supports the branching chain
network hypothesis of how syntax is controlled within the premotor song nucleus
HVC, and suggests that adaptation and many-to-one mapping from neural
substrates to syllables are important features of the neural control of complex
song syntax
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Parallels in the sequential organization of birdsong and human speech.
Human speech possesses a rich hierarchical structure that allows for meaning to be altered by words spaced far apart in time. Conversely, the sequential structure of nonhuman communication is thought to follow non-hierarchical Markovian dynamics operating over only short distances. Here, we show that human speech and birdsong share a similar sequential structure indicative of both hierarchical and Markovian organization. We analyze the sequential dynamics of song from multiple songbird species and speech from multiple languages by modeling the information content of signals as a function of the sequential distance between vocal elements. Across short sequence-distances, an exponential decay dominates the information in speech and birdsong, consistent with underlying Markovian processes. At longer sequence-distances, the decay in information follows a power law, consistent with underlying hierarchical processes. Thus, the sequential organization of acoustic elements in two learned vocal communication signals (speech and birdsong) shows functionally equivalent dynamics, governed by similar processes
An adapting auditory-motor feedback loop can contribute to generating vocal repetition
Consecutive repetition of actions is common in behavioral sequences. Although
integration of sensory feedback with internal motor programs is important for
sequence generation, if and how feedback contributes to repetitive actions is
poorly understood. Here we study how auditory feedback contributes to
generating repetitive syllable sequences in songbirds. We propose that auditory
signals provide positive feedback to ongoing motor commands, but this influence
decays as feedback weakens from response adaptation during syllable
repetitions. Computational models show that this mechanism explains repeat
distributions observed in Bengalese finch song. We experimentally confirmed two
predictions of this mechanism in Bengalese finches: removal of auditory
feedback by deafening reduces syllable repetitions; and neural responses to
auditory playback of repeated syllable sequences gradually adapt in
sensory-motor nucleus HVC. Together, our results implicate a positive
auditory-feedback loop with adaptation in generating repetitive vocalizations,
and suggest sensory adaptation is important for feedback control of motor
sequences
Measuring context dependency in birdsong using artificial neural networks
Context dependency is a key feature in sequential structures of human language, which requires reference between words far apart in the produced sequence. Assessing how long the past context has an effect on the current status provides crucial information to understand the mechanism for complex sequential behaviors. Birdsongs serve as a representative model for studying the context dependency in sequential signals produced by non-human animals, while previous reports were upper-bounded by methodological limitations. Here, we newly estimated the context dependency in birdsongs in a more scalable way using a modern neural-network-based language model whose accessible context length is sufficiently long. The detected context dependency was beyond the order of traditional Markovian models of birdsong, but was consistent with previous experimental investigations. We also studied the relation between the assumed/auto-detected vocabulary size of birdsong (i.e., fine- vs. coarse-grained syllable classifications) and the context dependency. It turned out that the larger vocabulary (or the more fine-grained classification) is assumed, the shorter context dependency is detected
Animal vocal sequences: not the Markov chains we thought they were.
Many animals produce vocal sequences that appear complex. Most researchers assume that these sequences are well characterized as Markov chains (i.e. that the probability of a particular vocal element can be calculated from the history of only a finite number of preceding elements). However, this assumption has never been explicitly tested. Furthermore, it is unclear how language could evolve in a single step from a Markovian origin, as is frequently assumed, as no intermediate forms have been found between animal communication and human language. Here, we assess whether animal taxa produce vocal sequences that are better described by Markov chains, or by non-Markovian dynamics such as the 'renewal process' (RP), characterized by a strong tendency to repeat elements. We examined vocal sequences of seven taxa: Bengalese finches Lonchura striata domestica, Carolina chickadees Poecile carolinensis, free-tailed bats Tadarida brasiliensis, rock hyraxes Procavia capensis, pilot whales Globicephala macrorhynchus, killer whales Orcinus orca and orangutans Pongo spp. The vocal systems of most of these species are more consistent with a non-Markovian RP than with the Markovian models traditionally assumed. Our data suggest that non-Markovian vocal sequences may be more common than Markov sequences, which must be taken into account when evaluating alternative hypotheses for the evolution of signalling complexity, and perhaps human language origins.This is the author's accepted manuscript and will be under embargo until the 20th of August 2015. This final version is published by Royal Society Publishing here: http://dx.doi.org/10.1098/rspb.2014.1370
Modeling the Syntax of the song of the Great Reed Warbler Faculty of Engineering, LTH
The song of many songbirds can be thought of as consisting of variable sequences of a finite set of syllables. A common approach in understanding the structure of these songs is to model the syllable sequences with a Markov Model. The Markov Model can either allow one-to-one (Markov Chain), many-to-many (Hidden Markov Model) or many-to-one (Partially Observed Markov Model) state to syllable mappings. In this project the song of the Great Reed Warbler is being studied in terms of the syllable sequences (strophes) being generated. It is shown that the Markov chain captures a lot of the structure in the song in the sense that it to large degree reproduces the syllable distributions at a specific position in the song that were observed in data. The repetition distribution for some syllable classes was consistent with that of a Markov chain while other syllable classes were better modeled by allowing the self-transition probability to be adapted as the syllable class is repeated more and more. Still some other syllable classes did not have their repetition distributions accurately captured by these two alternatives
Network dynamics in the neural control of birdsong
Sequences of stereotyped actions are central to the everyday lives of humans and animals, from the kingfisher's dive to the performance of a piano concerto. Lashley asked how neural circuits managed this feat nearly 6 decades ago, and to this day it remains a fundamental question in neuroscience. Toward answering this question, vocal performance in the songbird was used as a model to study the performance of learned, stereotyped motor sequences. The first component of this work considers the song motor cortical zone HVC in the zebra finch, an area that sends precise timing signals to both the descending motor pathway, responsible for stereotyped vocal performance in the adult, and the basal ganglia, which is responsible for both motor variability and song learning. Despite intense interest in HVC, previous research has exclusively focused on describing the activity of small numbers of neurons recorded serially as the bird sings. To better understand HVC network dynamics, both single units and local field potentials were sampled across multiple electrodes simultaneously in awake behaving zebra finches. The local field potential and spiking data reveal a stereotyped spatio-temporal pattern of inhibition operating on a 30 ms time-scale that coordinates the neural sequences in principal cells underlying song. The second component addresses the resilience of the song circuit through cutting the motor cortical zone HVC in half along one axis. Despite this large-scale perturbation, the finch quickly recovers and sings a near-perfect song within a single day. These first two studies suggest that HVC is functionally organized to robustly generate neural dynamics that enable vocal performance. The final component concerns a statistical study of the complex, flexible songs of the domesticated canary. This study revealed that canary song is characterized by specific long-range correlations up to 7 seconds long-a time-scale more typical of human music than animal vocalizations. Thus, the neural sequences underlying birdsong must be capable of generating more structure and complexity than previously thought
Collective Phenomena and Non-Finite State Computation in a Human Social System
We investigate the computational structure of a paradigmatic example of
distributed social interaction: that of the open-source Wikipedia community. We
examine the statistical properties of its cooperative behavior, and perform
model selection to determine whether this aspect of the system can be described
by a finite-state process, or whether reference to an effectively unbounded
resource allows for a more parsimonious description. We find strong evidence,
in a majority of the most-edited pages, in favor of a collective-state model,
where the probability of a "revert" action declines as the square root of the
number of non-revert actions seen since the last revert. We provide evidence
that the emergence of this social counter is driven by collective interaction
effects, rather than properties of individual users.Comment: 23 pages, 4 figures, 3 tables; to appear in PLoS ON