Nearest neighbours reveal fast and slow components of motor learning

Sepp Kollmorgen (), Richard H. R. Hahnloser and Valerio Mante ()
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Sepp Kollmorgen: University of Zurich and ETH Zurich
Richard H. R. Hahnloser: University of Zurich and ETH Zurich
Valerio Mante: University of Zurich and ETH Zurich

Nature, 2020, vol. 577, issue 7791, 526-530

Abstract: Abstract Changes in behaviour resulting from environmental influences, development and learning1–5 are commonly quantified on the basis of a few hand-picked features2–4,6,7 (for example, the average pitch of acoustic vocalizations3), assuming discrete classes of behaviours (such as distinct vocal syllables)2,3,8–10. However, such methods generalize poorly across different behaviours and model systems and may miss important components of change. Here we present a more-general account of behavioural change that is based on nearest-neighbour statistics11–13, and apply it to song development in a songbird, the zebra finch3. First, we introduce the concept of ‘repertoire dating’, whereby each rendition of a behaviour (for example, each vocalization) is assigned a repertoire time, reflecting when similar renditions were typical in the behavioural repertoire. Repertoire time isolates the components of vocal variability that are congruent with long-term changes due to vocal learning and development, and stratifies the behavioural repertoire into ‘regressions’, ‘anticipations’ and ‘typical renditions’. Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of a stratified ‘behavioural trajectory’, revealing numerous previously unrecognized components of behavioural change on fast and slow timescales, as well as distinct patterns of overnight consolidation1,2,4,14,15 across the behavioral repertoire. We find that diurnal changes in regressions undergo only weak consolidation, whereas anticipations and typical renditions consolidate fully. Because of its generality, our nonparametric description of how behaviour evolves relative to itself—rather than to a potentially arbitrary, experimenter-defined goal2,3,14,16—appears well suited for comparing learning and change across behaviours and species17,18, as well as biological and artificial systems5.

Date: 2020
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DOI: 10.1038/s41586-019-1892-x

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