Low dimensional model to study neural systems for motor control in songbirds

ANA AMADOR

Viña del Mar

Conferencia; Dynamics Days South America; 2014

(INVITED PLENARY TALK) Songbirds are a widely studied example of vocal learning that allows integrating neural and peripheral recordings with a precisely quantifiable behavior. 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 making predictions about motor control that we validated 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. The analysis of the neural activity in terms of the model for song production allowed proposing 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.