Elemental gesture dynamics are encoded by song premotor cortical neurons

Abstract

Quantitative biomechanical models can identify control parameters used during movements, and movement parameters encoded by premotor neurons. We fit a mathematical dynamical systems model including subsyringeal pressure, syringeal biomechanics, and upper vocal tract filtering to the songs of zebra finches. This reduced the dimensionality of singing dynamics, described as trajectories in pressure-tension space (motor “gestures”). We assessed model performance by characterizing the auditory response "replay" of song premotor HVC neurons to presentation of song variants in sleeping birds, and by examining HVC activity in singing birds. HVC projection neurons were excited and interneurons were suppressed with near-zero time lag, at times of gesture trajectory extrema. Thus, HVC precisely encodes vocal motor output via the timing of extreme points of movement trajectories. We propose that the sequential activity of HVC neurons represents the sequence of gestures in song as a “forward” model making predictions on expected behavior to evaluate feedback.