INVESTIGADORES
MINDLIN Bernardo Gabriel
congresos y reuniones científicas
Título:
Motor coding unveiled by a low dimensional model of song production
Autor/es:
A AMADOR; SANZ PERL Y; G B MINDLIN; MARGOLIASH DANIEL
Lugar:
Maryland
Reunión:
Congreso; 10th International congress of neuroethology; 2012
Institución organizadora:
Society for Neuroethology
Resumen:
Songbirds are widely studied as an attractive model system for vocal
learning and production. 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 low dimensional model for song 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 complete 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 birds 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.