Computational model for vocal tract dynamics in a suboscine bird

Assaneo, M. Florencia; Ma, Trevisan

doi:10.1103/physreve.82.031906

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…We minimized the objective function using a standard genetic algorithm Holland (1992). This is optimization technique is useful to produce large pools of different solutions and is routinely utilized to estimate parameters in biophysical models (see for example Assaneo and Trevisan, 2010). The algorithm was started with a population of 1000 random seeds that were evolved for ≈10000 generations.…”

Section: Methodsmentioning

confidence: 99%

Visualization of currents in neural models with similar behavior and different conductance densities

Alonso

Marder

2019

eLife

145

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Conductance-based models of neural activity produce large amounts of data that can be hard to visualize and interpret. We introduce visualization methods to display the dynamics of the ionic currents and to display the models’ response to perturbations. To visualize the currents’ dynamics, we compute the percent contribution of each current and display them over time using stacked-area plots. The waveform of the membrane potential and the contribution of each current change as the models are perturbed. To represent these changes over a range of the perturbation control parameter, we compute and display the distributions of these waveforms. We illustrate these procedures in six examples of bursting model neurons with similar activity but that differ as much as threefold in their conductance densities. These visualization methods provide heuristic insight into why individual neurons or networks with similar behavior can respond widely differently to perturbations.

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Section: Methodsmentioning

confidence: 99%

Visualization of currents in neural models with similar behavior and different conductance densities

Alonso

Marder

2019

eLife

145

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“…The upper vocal tract consists of the air-filled passages that link the syrinx to the environment. It determines much of the distribution of energy of the sound across its harmonic frequency components, defining a perceptual property as important as the timbre [21] – [23] . Its filtering characteristics depend on the acoustic properties of its components.…”

Section: Methodsmentioning

confidence: 99%

Prosthetic Avian Vocal Organ Controlled by a Freely Behaving Bird Based on a Low Dimensional Model of the Biomechanical Periphery

et al. 2012

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Because of the parallels found with human language production and acquisition, birdsong is an ideal animal model to study general mechanisms underlying complex, learned motor behavior. The rich and diverse vocalizations of songbirds emerge as a result of the interaction between a pattern generator in the brain and a highly nontrivial nonlinear periphery. Much of the complexity of this vocal behavior has been understood by studying the physics of the avian vocal organ, particularly the syrinx. A mathematical model describing the complex periphery as a nonlinear dynamical system leads to the conclusion that nontrivial behavior emerges even when the organ is commanded by simple motor instructions: smooth paths in a low dimensional parameter space. An analysis of the model provides insight into which parameters are responsible for generating a rich variety of diverse vocalizations, and what the physiological meaning of these parameters is. By recording the physiological motor instructions elicited by a spontaneously singing muted bird and computing the model on a Digital Signal Processor in real-time, we produce realistic synthetic vocalizations that replace the bird's own auditory feedback. In this way, we build a bio-prosthetic avian vocal organ driven by a freely behaving bird via its physiologically coded motor commands. Since it is based on a low-dimensional nonlinear mathematical model of the peripheral effector, the emulation of the motor behavior requires light computation, in such a way that our bio-prosthetic device can be implemented on a portable platform.

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“…The motor gestures that determine the fundamental frequencies of vocalizations of songbirds are coordinated with the geometry of several parts of the vocal tract, such as the length of the trachea, the volume of the oropharyngeal-esophageal cavity, or the beak aperture [3,15,16]. In many of the reported experiments, however, this coordinated activity does not result in nonlinear effects as obvious as jumps in frequency.…”

Section: Introductionmentioning

confidence: 99%

Acoustic signatures of sound source-tract coupling

2011

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Birdsong is a complex behavior, which results from the interaction between a nervous system and a biomechanical peripheral device. While much has been learned about how complex sounds are generated in the vocal organ, little has been learned about the signature on the vocalizations of the nonlinear effects introduced by the acoustic interactions between a sound source and the vocal tract. The variety of morphologies among bird species makes birdsong a most suitable model to study phenomena associated to the production of complex vocalizations. Inspired by the sound production mechanisms of songbirds, in this work we study a mathematical model of a vocal organ, in which a simple sound source interacts with a tract, leading to a delay differential equation. We explore the system numerically, and by taking it to the weakly nonlinear limit, we are able to examine its periodic solutions analytically. By these means we are able to explore the dynamics of oscillatory solutions of a sound source-tract coupled system, which are qualitatively different from those of a sound source-filter model of a vocal organ. Nonlinear features of the solutions are proposed as the underlying mechanisms of observed phenomena in birdsong, such as unilaterally produced “frequency jumps,” enhancement of resonances, and the shift of the fundamental frequency observed in heliox experiments.

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Computational model for vocal tract dynamics in a suboscine bird

Cited by 3 publications

References 15 publications

Visualization of currents in neural models with similar behavior and different conductance densities

Visualization of currents in neural models with similar behavior and different conductance densities

Prosthetic Avian Vocal Organ Controlled by a Freely Behaving Bird Based on a Low Dimensional Model of the Biomechanical Periphery

Acoustic signatures of sound source-tract coupling

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