Using a low dimensional birdsong model to unveil motor control in zebra finches

A. AMADOR, Y. SANZ PERL, G.B. MINDLIN AND D. MARGOLIASH

Villa Carlos Paz, Cordoba, Argentina

Conferencia; XIII Latin American Workshop on Nonlinear Phenomena (LAWNP); 2013

(INVITED TALK) Songbirds are a well studied example of vocal learning that allows to integrate neural and peripheral recordings with a precisely quantitative behavior (song). Although neural activity has been related to song acoustics in auditory playback experiments, it remains unresolved whether neural activity is related to song spectral structure during singing. To address this issue, we worked with a minimal physical model for birdsong production, having as an output a synthetic song. Each syllable was coded in terms of parameters related to air sac pressure and tension of the syringeal labia, defining motor gestures. The model for birdsong production allowed to make predictions about motor control that were validated by developing a novel technique for dynamic control of subsyringeal pressure in zebra finches. To further validate this model, we assessed responses of the premotor forebrain nucleus HVC to song playback in sleeping birds, as HVC neurons exhibit highly selective responses to the bird´s own song (BOS). Remarkably, the mathematical model was able to elicit responses strikingly similar to those for BOS, with the same phasic-tonic features. The analysis of the neural activity in terms of the model for song production allowed to propose a novel model for motor control in songbirds. We confirmed this result with HVC recordings in singing birds, showing the pertinence of working with a low dimensional model that represents an approximation of peripheral mechanics but captures relevant features of behavior.