Abstract
Motor skill learning is characterized by improved performance and reduced motor variability. The neural mechanisms that couple skill level and variability, however, are not known. The zebra finch, a songbird, presents a unique opportunity to address this question because production of learned song and induction of vocal variability are instantiated in distinct circuits that converge on a motor cortex analogue controlling vocal output. To probe the interplay between learning and variability, we made intracellular recordings from neurons in this area, characterizing how their inputs from the functionally distinct pathways change throughout song development. We found that inputs that drive stereotyped song-patterns are strengthened and pruned, while inputs that induce variability remain unchanged. A simple network model showed that strengthening and pruning of action-specific connections reduces the sensitivity of motor control circuits to variable input and neural 'noise'. This identifies a simple and general mechanism for learning-related regulation of motor variability.
Highlights
Our capacity to learn and reliably execute motor skills underlies much of what we do
To characterize how inputs to RA change as a function of sensorimotor learning, we used birds of ages corresponding to three distinct phases of song learning (‘Materials and methods’, Figure 1B): (1) Subsong (40–45 days post-hatch, dph)—the earliest stage of sensorimotor learning characterized by highly variable songs driven largely by lateral magnocellular nucleus of the anterior neopallium (LMAN) (Aronov et al, 2008); (2) Plastic song (60–65 dph)— an intermediate stage of development characterized by recognizable but variable song elements or syllables that are subject to further change; (3) Crystallized song (90–130 dph)—adult stage at which a stereotyped and stable version of the bird's courtship song has developed (Immelmann, 1969; Price, 1979)
Adult vocalizations are driven by the precise burst firing of RA projection neurons (Simpson and Vicario, 1990; Yu and Margoliash, 1996; Leonardo and Fee, 2005) that are thought to be largely triggered by inputs from time-keeper neurons in HVC (Hahnloser et al, 2002; Hahnloser, 2006)
Summary
Our capacity to learn and reliably execute motor skills underlies much of what we do. Motor skill learning is characterized by high initial motor variability that is gradually reduced as performance improves and skills are consolidated (Figure 1A) (Lee et al, 1999; Park et al, 2013). As viable solutions are found (a good tennis serve, for example), variability in motor output can become detrimental for expert performance and is often reduced. This capacity of the nervous system to regulate variability and plasticity in motor output as a function of learning or skill level enables new skills to be acquired and those already mastered to be stably expressed and maintained. The neural circuit mechanisms that regulate motor variability as a function of learning have not been identified
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