Using a low dimensional birdsong model to unveil neural coding in zebra finches

ANA AMADOR; YONATAN SANZ PERL; DANIEL MARGOLIASH; GABRIEL B. MINDLIN

Conferencia; Society for Neuroscience (SFN) 42nd Annual Meeting; 2012

Songbirds are a well studied example of vocal learning that allows to integrate neural and peripheral recordings with a precisely quantifiable behavior. Although neural activity in the premotor forebrain nucleus HVC 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 that included a description of the sound source and vocal tract, where mathematical parameters can be linked to physiological properties observed during singing. The model output is a synthetic song, where each syllable was coded in terms of parameters related to air sac pressure and tension of the syringeal labia. In this way, we defined motor ?gestures? as trajectories in the parameter space of the minimal model. To validate this model, we assessed the responses of HVC neurons to song playback in sleeping birds. Under these conditions, HVC neurons exhibit selective responses to the bird´s own song (BOS), and weaker responses to tones, noises, conspecific songs, or even slightly modified BOS. The mathematical model was able to elicit responses strikingly similar to those for BOS, with the same phasic-tonic features albeit somewhat lower magnitude of response. These results demonstrate that a low dimensional model representing an approximation of peripheral mechanics is sufficient to capture behaviorally relevant features of song, providing important and valuable simplification that can help clarify neural coding. Analyzing the HVC neurons responses to playback of each bird?s own song, we observed that projection neurons were excited and interneurons were suppressed, with near-zero time lag, at the times of gesture extrema (defined as beginning, end or maxima of gestures). In this way, HVC neurons precisely encode the timing of extreme points of movement trajectories. In preliminary data, we confirm these results with HVC recordings in singing birds. Our results suggest that movements are represented as trajectories, not static parameters, at higher levels of motor systems. Given that HVC activity occurs with near synchrony to behavioral output, we propose that the activity of HVC neurons represents the sequence of gestures in song as a ?forward? model making predictions on expected behavior