Physiologically driven electonic vocal organ

EZEQUIEL M. ARNEODO; YONATAN SANZ PERL; FRANZ GOLLER; GABRIEL B. MINDLIN

New Orleans, LA

Conferencia; Society for Neuroscience Annual Meeting; 2012

Society for Neuroscience

Birdsong has become an ideal animal model to study general mechanisms underlying complex, learned motor behavior. The rich and diverse vocalizations composing birdsong require 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 this peripheral biomechanical device as a nonlinear dynamical system leads to the conclusion thatnontrivial behavior emerges even when the organ is commanded by simple motor instructions: smooth paths in a low dimensional parameter space.Brain Machine Interfaces (BMIs) decode motor instructions from neuro-physiological recordings and feed them to bio-mimetic effectors to produce synthetic behavior.Many applications achieve high accuracy on a limited number of tasks by applying statistical methodsto these data to extract features corresponding to certain motor instructions.We built a bio-prosthetic avian vocal organ, that is based on a low-dimensional mathematical model that accounts for the dynamics of the bird´s syrinxand robustly relates smooth paths in a physiologically meaningful parameter space to complex sequences of vocalizations.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 in real-time on a Digital Signal Processor,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.This exemplifies the plausibility of a type of synthetic interfacing between the brain and a complex motor behavior. In this type of devices, the understanding of the bio-mechanics of the periphery is key to identifying a low dimensionalphysiological signal coding the motor instructions, therefore enabling real-time implementation at a low computational cost.